An interactive personal health management system based on artificial intelligence and its operation method

Abstract

The present invention discloses an interactive personal health management system based on artificial intelligence and an operating method. Unlike some existing methods, the present invention is not limited to a single collection device to obtain limited health data and brief basic information as the user's only health information source, but only uses it as basic health information, and proposes the concept and implementation plan of "supplementary health information". On the basis of obtaining the above-mentioned basic health information, more supplementary health information of the user is obtained by alternating or using multiple types of collection devices, as well as multiple collection channels and methods, to jointly build a comprehensive AI health information source for the user. The AI ​​health information source is analyzed and calculated using an artificial intelligence health algorithm. Once the AI ​​health information source changes, the AI ​​health assessment result is also adjusted accordingly, forming an infinite loop mechanism, which makes the user's health assessment result infinitely close to accuracy, and uses an AI health digital person to interact with the user in real time about health information in natural language.

Description

Technical Field

[0001] This invention lies at the intersection of artificial intelligence and health management. Specifically, it is an innovative design and method for applying artificial intelligence (AI) technology to a health management system. This system integrates artificial intelligence algorithms from computer science, natural language processing, and machine learning techniques, along with specialized knowledge from the health management field.

[0002] In the health data collection phase, the collected data is preliminarily processed and analyzed with the help of a combination of multiple sensor technologies integrated in the collection equipment and machine learning algorithms. Natural language processing technology plays a role in the interaction between users and the system, allowing users to easily input and obtain health information. In the health analysis and evaluation phase, artificial intelligence algorithms and machine learning technologies jointly conduct in-depth mining and analysis of large amounts of health data, and combine professional health knowledge to generate accurate evaluation results. Finally, in the health management recommendation generation phase, the user's personalized information and analysis and evaluation results are comprehensively considered, and natural language processing technology is used to provide users with multi-dimensional and interactive health management recommendations in an easy-to-understand manner. The entire process involves the coordinated operation of hardware equipment (such as collection equipment and operation terminal equipment) and software systems (including AI health intelligent bodies, AI health digital people and related management systems), and is committed to providing users with personalized, multi-dimensional and interactive health management services. Background Art

[0003] In the early stages of the development of the health management field, traditional medical models were dominant, focusing on disease treatment. With increasing health awareness and a pursuit of a better quality of life, the focus of health management has gradually shifted to disease prevention and health promotion, and modern technology has begun to be integrated into this process. People's needs for health management are becoming increasingly complex and diverse, and the search for more effective health protection methods is underway at all stages. However, both traditional medical health management methods, emerging health management applications using wearable smart devices, and some current artificial intelligence health management systems all have numerous limitations in different aspects.

[0004] (1) Deficiencies of traditional medical and health management methods

[0005] Inadequate personalized health services: In the traditional healthcare system, due to the imbalance between supply and demand of medical resources and the impact of the medical system on doctors' work patterns, general doctors often have to deal with numerous patients simultaneously. This leads to a shortage of time and attention to detail for each patient, despite their busy schedule. This large number of patients makes it difficult for doctors to fully understand each patient's specific situation. As a result, the health advice they provide is less precise and fails to meet patients' demand for personalized health services.

[0006] Limited health service hours: Traditional general physicians typically have fixed working hours. Patients experiencing health issues outside of working hours often lack timely access to professional medical advice, potentially delaying their treatment. While some hospitals employ on-call physicians, this dilemma remains difficult to address due to the high volume of patients and limited access to patient information. Furthermore, interactions between doctors and patients are often brief and superficial, making it difficult to fully understand each patient's unique circumstances and needs, hindering the provision of long-term, personalized health management services.

[0007] Lag in health assessment results: Faced with the massive amounts of monitoring data accumulated over time, traditional general practitioners are relatively slow to process and analyze this data, sometimes even requiring multi-departmental consultations. Because traditional healthcare lacks efficient data processing tools and algorithms, manual data analysis is prone to errors and omissions. In these cases, patients may have to wait a long time for diagnostic results and recommendations, impacting the timeliness of health management.

[0008] While dedicated personal or family doctors can overcome the shortcomings of conventional medical treatment, they are difficult to achieve due to the current state of medical resources and the general public's economic level. This prevents the public from receiving continuous, personalized health management services, potentially hindering the timely and effective prevention and control of some chronic diseases, and negatively impacting public health management.

[0009] Untimely knowledge updates and service adjustments: Due to their busy schedules and limited access to information, ordinary doctors struggle to maintain a constant grasp of the latest medical and health knowledge and research findings, making it difficult for them to quickly adapt to advances in medical research and changes in user health needs. For example, in health management, new health risk assessment indicators or healthy lifestyle recommendations may not be communicated to patients in a timely manner, potentially impacting their accurate understanding of their health status and their adoption of appropriate health management measures.

[0010] (2) Insufficiencies of general wearable smart devices in health management

[0011] With the rise of wearable smart devices and the emergence of new sensor technologies and their improved accuracy, health monitoring devices have moved from traditional medical fields into everyday homes, ushering in a new era of personal health management and playing a significant role in promoting and popularizing health management. However, current wearable smart devices generally have the following shortcomings in health management:

[0012] Incomplete health information acquisition: Wearable smart devices typically rely solely on limited physiological data from their own monitoring systems. They are limited in collecting basic health-related information (such as medical and physical examination reports, user mood and ability assessments, etc.), and lack the interactive information provided by doctors. Consequently, their health information sources lack systematic and comprehensive information, which in turn affects the accuracy of health assessments.

[0013] Health assessment results lack reference value: Typical wearable smart devices often simply aggregate and summarize health monitoring data for users to review and analyze, failing to achieve the effectiveness of intelligent health management. Even if individual devices can provide certain health assessment results, they are calculated based on simple physiological characteristic data models, not large-scale artificial intelligence models. Furthermore, these health assessments lack a systematic and comprehensive health information source and ignore individual user differences. Therefore, these health assessments are not valuable references.

[0014] Single user interaction method: Most wearable smart devices only display health monitoring data or health assessment results in the corresponding interface window, and their systems lack user interactivity and participation. Users are often forced to passively accept health advice and lack a mechanism for real-time, in-depth communication and feedback with health management systems or professionals. This single interaction method fails to meet users' needs for real-time communication and feedback during the health management process. For example, when users have questions about health advice or encounter difficulties during implementation, they cannot receive timely and targeted answers and guidance, which hinders the effective implementation of health management plans.

[0015] Lack of collaborative health management capabilities: General wearable smart devices are usually used independently and cannot implement the following functions in cluster user mode: managing supplementary health information for other users (such as establishing basic health files, uploading medical and physical examination reports, conducting relevant ability and emotional assessments, etc.), performing device binding operations, and establishing attention and collaborative management of health information between different users. This limitation makes it difficult to meet the complex needs of multi-person health management. In real life, scenarios such as family health management and corporate employee health management have high requirements for collaborative capabilities. For example, when healthy users are unable to complete related operations alone due to old age or physical defects, or for groups, families or even family users, general wearable smart devices cannot meet the needs and scenarios of such clustered health management.

[0016] (3) Insufficiency of existing AI in personal health management

[0017] With the development of artificial intelligence technology, some application explorations have emerged in the field of health management. Some systems use machine learning algorithms to analyze massive amounts of medical data to assist doctors in disease diagnosis. However, most are limited to specific disease categories or single medical procedures, failing to build a comprehensive system that covers all aspects of personal health management. In practical applications such as chronic disease management, these systems fail to comprehensively consider patient lifestyle and psychological factors.

[0018] Furthermore, advancements in AI natural language processing technology have led to the emergence of AI language models or intelligent chatbots, such as Doubao, KIMI, and iFlytek Spark AI, which have become essential tools in people's daily lives and work. However, these general-purpose intelligent chatbots cannot autonomously access users' health monitoring data. They typically require users to proactively provide health information sources before interpreting and responding, rendering them non-professional health management systems. Health management apps like iFlytek Xiaoyi, while allowing users to upload medical reports, cannot automatically access users' health monitoring data or more health information sources, similar in nature to general-purpose intelligent chatbots.

[0019] In summary, after reviewing patent literature on the application of artificial intelligence in health management, no solution was found that focuses on personal health and can solve the defects of traditional medical models and health consultations, as well as the shortcomings of general wearable smart devices in health management.

[0020] For example, the application number is 202110414313.1, and the name is Artificial Intelligence Multidisciplinary Expert Collaborative Health Management System and Method. The invention discloses an artificial intelligence multidisciplinary expert collaborative health management system. The artificial intelligence health management device is equipped with a health monitoring chip, and the health monitoring chip is electrically connected to the display screen, palm sensing area and foot sensing area through wires. A cloud storage module and an information receiving module are provided in parallel in the health monitoring chip, a health management module is provided in the client module, and a module to be consulted and a consultation module are provided in parallel in the doctor-side module; this artificial intelligence multidisciplinary expert collaborative health management system enables patients to grasp their physical health status in a timely manner, has a good therapeutic effect on chronic diseases that have already occurred, and has a good preventive effect on possible chronic diseases. It conducts a comprehensive assessment of health and chronic diseases, and launches a "assessment-follow-up-reassessment-follow-up" health management spiral closed-loop system for the entire health management cycle to achieve health management throughout the life cycle.

[0021] The aforementioned invention discloses a health management system and method. Although it is called a management system, it is essentially designed around specific health management-related devices and primarily collects body data through specific hardware structures, such as foot and palm sensing areas. This system is not a person-centric AI health management system. While it offers a closed-loop system for the entire health management cycle, characterized by a spiral of "assessment-follow-up-reassessment-follow-up," its health information source format is relatively fixed and limited. Beyond basic user information, it fails to comprehensively collect various supplementary health information, such as medical and physical examination reports, emotional and ability assessment results, and lifestyle information. In terms of health management, the lack of comprehensive health information integration prevents users from providing accurate and personalized health management plans. For example, health risk assessments and the development of preventive health measures are difficult to achieve accurately and individually due to the lack of key factors such as lifestyle information. Furthermore, the system is limited in its ability to integrate health information for long-term health management and cannot fully utilize multiple health information sources to optimize health management strategies. Therefore, this system is limited to specific disease categories or a single medical procedure, and has yet to establish a comprehensive and constructive system that covers all dimensions of health management.

[0022] For example, application number 201910062638.0, entitled "A Personalized Physical Health Terminal Service System Based on Artificial Intelligence," belongs to the field of artificial intelligence health management systems, and particularly relates to a personalized physical health terminal service system based on artificial intelligence. The present invention collects parameters related to the user's physical health, such as heart rate, blood pressure, body temperature, and pulse information, performs data analysis on the collected parameters, and then compares the parameters with the system's pre-stored physical model to provide the user with accurate health guidance information. At the same time, the user's periodic physical parameters are structured as data questions, and the physical health management AI performs data analysis on the user's questions and answers. For questions that the user frequently answers incorrectly or has not improved, the system's blind spot warning module performs graphical analysis and warnings. The physical health management AI provides personalized display information and warning information, improving the system's interactivity, detecting and proactively reminding users to monitor and improve their physical condition, allowing users to better understand their physical condition and improvements over the long term.

[0023] The above invention discloses a personalized physical health terminal service system. Although it belongs to the field of artificial intelligence health management system and also enables an intelligent management robot as a virtual robot to be installed on the system terminal, it has some obvious limitations. The system mainly focuses on the physical fitness of students for health management. It is designed around collecting specific physical fitness-related health indicators (such as heart rate, blood pressure, body temperature and pulse information), and then compares them with the system's pre-stored physical fitness model to provide health guidance. It is not a comprehensive health management for ordinary individuals. It lacks the collection of other important health supplementary information, such as medical and physical examination reports, emotional and ability assessment results, lifestyle information, etc., and cannot fully understand the user's health status. The interaction between the user and the system of this invention is mainly through physical fitness management AI to ask and answer questions, but the problem is limited to physical fitness-related parameters and questions generated based on these parameters. The feedback form is mainly rewards or prompts for correct or incorrect answers, as well as warnings for user-prone points of error. It fails to give full play to the natural language understanding and interactive experience characteristics of artificial intelligence. This interaction method is relatively limited and lacks more humane and diversified methods such as natural language interaction to meet users' different health consultation and communication needs.

[0024] For example, the application number is 202410789753.9, and the name is A health record tracking and feedback system based on artificial intelligence and machine learning technology. The invention discloses a health record tracking and feedback system based on artificial intelligence and machine learning technology, including a data acquisition module, a data processing module, a health assessment module, a feedback generation module, a prediction module and a user interface module. The system collects user physiological data in real time through biosensors, and generates personalized health assessments and suggestions by processing and analyzing the data. The system can predict future health status and provide timely health intervention suggestions. The user interface is friendly and intuitive, helping users understand health data and suggestions.

[0025] However, although the above invention adopts artificial intelligence and machine learning technologies, its technical application is mainly limited to the data collection and processing level, which has similarities and limitations with ordinary wearable smart devices. This invention has only been improved in data processing and algorithms due to the use of artificial intelligence technology. For example, the data processing module adopts a specific deep neural network structure and algorithm to perform data cleaning, modeling and other operations, and constructs functions suitable for health record data sets. However, its "health record data set" is actually only limited to the health monitoring data of the acquisition module, and does not fully consider other key health information besides the user's physiological data, such as the user's basic health records, medical and physical examination reports, mood and ability assessment results, and lifestyle information. This single-dimensional data source makes it impossible for the system to conduct a comprehensive and integrated assessment and management of the user's health status from multiple perspectives, which may affect the accuracy, comprehensiveness and effectiveness of health management.

[0026] Although the above invention mentions that the user interface is friendly and intuitive and helps users understand health data and suggestions, it lacks specific explanations and only gives a simple solution concept without elaborating on the specific implementation plan. In addition, it is not clear whether the interactive interface has a mechanism for obtaining supplementary health information from users. At the same time, it does not mention whether there are AI health agents and AI health digital people trained based on artificial intelligence, let alone their working methods and interaction mechanisms. Although it mentions "helping users understand health data and suggestions", it does not indicate whether users are supported to actively supplement health information or conduct real-time health consultations or medical consultations. This situation puts users in a relatively passive health management mode similar to ordinary wearable smart devices during the health management process, and cannot meet the more practical needs of users to actively participate in health management.

[0027] (IV) Innovative measures and practical significance of this invention

[0028] In summary, the present invention fully considers the defects and shortcomings of traditional medical health management methods, wearable smart devices, and existing artificial intelligence health management systems. In response to these problems, the present invention has carefully launched a new interactive health management system based on artificial intelligence and its operation method, which is referred to as the "AI health management system". The present invention is based on the design concept of personal health and designs the system around the comprehensive health status of an individual. This concept not only focuses on the root causes of disease, but also emphasizes disease prevention and health promotion, and is committed to providing users with comprehensive and personalized health management services.

[0029] The personal health management system involved in the present invention collects the user's health information through various channels to build a complete health information source. First, basic health monitoring data and information are collected with the help of collection equipment, which covers many key health indicators, such as health signs data (heart rate, electrocardiogram, blood pressure, blood oxygen saturation, body temperature, etc.), health behavior data (sleep status, exercise status, etc.) and health stress data, etc., and analyzed through professional algorithms to obtain preliminary health monitoring information. Second, the health supplementary information management system is used to gather more detailed health information of users, including users' basic health records, users' medical and physical examination reports, and users' emotions and ability assessment results, and organically integrate this information with previously acquired health monitoring information to build a relatively comprehensive user health information source.

[0030] In addition, the present invention deploys an AI health digital person, which can further collect the user's supplementary health information and user feedback in a manner similar to that of an ordinary doctor's consultation during the process of health consultation with the user, so that the user's health information source is more complete. On this basis, the AI ​​health management system of the present invention uses an AI health agent that has been professionally trained in artificial intelligence and health systems to conduct comprehensive and in-depth analysis and calculations on the user's comprehensive health information source, thereby providing the user with accurate health assessment results. At the same time, with the help of the natural language interaction function of the AI ​​health digital person, it can respond at any time 24 hours a day, meet the user's health consultation needs through a natural language multimodal interaction mode, and answer various consultation questions about health or the use of system equipment. In short, the present invention is committed to obtaining the most comprehensive health information source for users, and using cutting-edge artificial intelligence technology to carry out analysis and calculations, aiming to provide users with evaluation results and health suggestions with great reference value.

[0031] Moreover, the present invention fully considers the general demand for health management of special groups (such as the elderly or people with physical disabilities), innovatively creates a group cluster health management model and a family cluster health management model, and supports mutual health care application scenarios and health management application scenarios between users, which greatly meets the intelligent health management needs of special groups and the centralized intelligent health management needs of multiple users. Furthermore, the health management system involved in the present invention and the AI ​​health digital human deployed therein also have the function of intelligent reminder and guidance for user health supplementary information, health matters, and abnormal use of health equipment, and act as a customer service staff for the system and equipment, greatly improving the autonomy and humanized experience of user intelligent health management. In addition, the system also supports a single user to use multiple collection terminal devices at the same time, making the user's health collection data and information more comprehensive and diversified; the system also supports multiple operation terminal devices, and users can conduct health consultation and communication with the AI ​​health digital human on any operation terminal; the system also supports the export of user personal health data for reference by third-party medical institutions, making up for the lack of long-term health monitoring data of patients in general medical institutions. The numerous innovative measures of this invention have achieved major breakthroughs on multiple levels, aiming to fully meet users' diverse, humanized and individualized needs for health management, helping users to achieve more comprehensive, accurate and convenient intelligent health management, and bringing users an unprecedented health management experience. Summary of the Invention

[0032] In response to the problems encountered in the above background technologies, the present invention aims to fully meet users' diverse, humanized, and individualized needs for health management, helping users achieve more comprehensive, accurate, and convenient intelligent health management. Specifically, the following technical solutions are adopted:

[0033] An interactive personal health management system based on artificial intelligence and its operation method are provided. This system is referred to as the "AI health management system" in the present invention specification. It includes two key and interrelated parts: basic equipment (hardware) and basic framework (software), which together build a complete and efficient health management system. Specifically, the following technical solutions are adopted:

[0034] (1) System hardware architecture

[0035] 1. Hardware Overview

[0036] As shown in Figure 01 [System Basic Equipment Diagram - Equipment Components], basic equipment, or equipment components, serve as the hardware carrier and environmental conditions for system operation. System operation relies on a strong hardware foundation, primarily consisting of data acquisition terminals, operation terminals, and the AI ​​Health Cloud Service Platform hardware system. Operation terminals are a collective term for primary operation terminals (such as smartphones and tablets) and secondary operation terminals (such as smart speakers and smart robots). These hardware components work together to provide a stable operating environment and a physical carrier for data exchange.

[0037] 2. Collection device type and function integration

[0038] The AI ​​health management system of the present invention supports diverse collection devices, which is the key to achieving comprehensive health data collection. For example, wearable smart devices (such as smart rings, smart bracelets, smart watches, etc.) integrate a series of advanced sensor technologies, such as optical heart rate sensors (PPG), electrode electrocardiograms (ECG), blood pressure detection sensors, thermometers, accelerometers, gyroscopes, etc. Through these sensors, a wide range of health signs data of users can be collected in real time, such as heart rate, electrocardiogram, blood pressure, blood oxygen saturation, body temperature, etc., as well as health behavior data that deeply reflects the user's living habits and physical activity status, such as sleep status, exercise status, etc. Home medical testing equipment (such as electronic blood pressure monitors, blood glucose meters, body fat scales, etc.) focuses on the precise measurement of specific health indicators, which further supplements the overall health data.

[0039] Different from some existing health management methods that only obtain limited health monitoring data of users through specific collection channels (such as a single collection device), the collection device of the present invention greatly broadens the breadth and depth of data collection. From the level of collection device categories, it includes but is not limited to ordinary wearable smart devices that have not been certified as medical devices, similar wearable health data collection devices that have been certified as medical devices, personal or home medical devices and health testing equipment that have been certified as medical devices, professionally certified or non-professionally certified work and living environment monitoring data monitoring equipment, etc., which can collect the above-mentioned health supplementary information directly or indirectly related to the health of the user; from the level of collection device usage, it includes but is not limited to a single designated type of collection device, the alternating use of multiple types of collection devices, and the simultaneous use of multiple types of collection devices to collect health supplementary information directly or indirectly related to the health of the user.

[0040] From the perspective of collection device categories, it includes but is not limited to ordinary wearable smart devices that have not been certified as medical devices, wearable-like health data collection devices that have been certified as medical devices, personal or home medical devices and health testing equipment that have been certified as medical devices, work and living environment monitoring data monitoring equipment that have been professionally certified or not, and other collection equipment, and professionally certified or not professionally certified acoustic collection equipment that can collect supplementary health information that is directly or indirectly related to the health of the above-mentioned users.

[0041] The acquisition device uses built-in professional algorithms to perform preliminary processing on the raw sensor data, such as filtering to remove noise interference and applying calibration algorithms to ensure data accuracy, thereby obtaining more reliable health monitoring information. The processed data is then transmitted to a paired operating terminal device (such as a smartphone) via wired or wireless connection technologies (such as Bluetooth and WiFi), enabling timely data transmission and integration, and further upload to the AI ​​Health Cloud Service Platform.

[0042] 3.Operation terminal device characteristics

[0043] The diverse design of operating terminal devices caters to diverse user scenarios and needs. For example, primary operating terminal devices such as smartphones and tablets feature high-performance processors, ample memory, and high-resolution displays. They run intelligent operating systems and are highly open and compatible, allowing users to install applications or related apps specific to the "primary operating terminal (system)." Users can use touch controls to view health data and obtain health consultations, providing a convenient and intuitive interactive experience.

[0044] Significantly different from conventional health management methods, secondary operation terminal devices, such as smart speakers and intelligent robots, feature audio input and output (including microphones and speakers) and network connectivity. Some utilize a non-intelligent operating system MCU solution combined with a real-time operating system (RTOS) to execute the "secondary operation terminal" software. At the user level, the operation terminal primarily receives data from collection devices and transmits it to the AI ​​health cloud service platform. Users can also interact with the AI ​​health digital person through voice commands for health consultations or Q&A.

[0045] A user can use the main operation terminal device and the secondary operation terminal device at the same time; at the same time, the system also supports one user using the main operation terminal device and another user using the secondary operation terminal device, and the two jointly realize the health management model of health hosting.

[0046] 4. Hardware composition of AI health cloud service platform

[0047] The hardware system of the AI ​​health cloud service platform is the core computing and data storage center of the entire system. It is a powerful integrated service system integrating hardware and software. Its important feature is the integration of the AI ​​health intelligent body cloud service system.

[0048] Its hardware architecture includes high-performance AI computing servers with powerful computing capabilities capable of rapidly processing massive amounts of health data. Large-capacity storage devices (such as hard disk arrays and distributed storage systems) are used to store users' health information sources, health assessment results, and various data required for system operation, ensuring secure storage and ready access to data. High-speed network connection devices (such as 10 Gigabit Ethernet and Fibre Channel) ensure rapid data transmission within the platform and efficient interconnection with external devices and systems.

[0049] In terms of computing resources, the platform can process health data calculation tasks for multiple users in parallel, meeting the needs of large-scale users using the platform simultaneously. In terms of storage resources, data redundancy technology and efficient data management strategies are used to ensure data security and scalability. For example, regular backups of user health data are performed to prevent data loss.

[0050] (2) System software architecture

[0051] 1. Overview of software system composition

[0052] As shown in Figure 02 [System Basic Architecture Diagram - Software System], the basic architecture of the AI ​​health management system, based on the software system, consists of four subsystems: the AI ​​health agent, the data collection terminal, the operation terminal, and the AI ​​health cloud service platform. These subsystems work closely together to realize the system's intelligent health management functions. The AI ​​health agent is divided into a client system and a cloud service system, embedded in the operation terminal and the AI ​​health cloud service platform respectively. It also integrates cloud computing capabilities and mobile terminal interaction functions, providing core support for the system's intelligent operation.

[0053] As shown in Figure 02 [Schematic diagram of the basic structure of the system composition - software system], in addition to the above-mentioned core secondary subsystem AI health intelligent body client system, the main operation terminal also covers other secondary subsystems: the health monitoring information management system is responsible for collecting, organizing and analyzing user health monitoring data and information, providing a basis for subsequent evaluation; the health supplementary information management system supports users to manage personal health supplementary information, and integrates it with health monitoring information as the user's AI health information source; other management systems of the main operation terminal are responsible for ensuring the normal operation of the equipment and realizing functional expansion, involving multiple aspects such as equipment settings and permission management.

[0054] As shown in Figure 02, the other management systems of the secondary operation terminal and the AI ​​health intelligent body client system together constitute a complete secondary operation terminal; the other management systems of the cloud service platform and the AI ​​health intelligent body cloud service system constitute a complete AI health cloud service platform; the health data collection management system and the other management systems of the collection terminal constitute a complete collection terminal.

[0055] 2. Composition of functional modules of each subsystem

[0056] As shown in Figure 03 [Schematic diagram of deployment of main functional modules of the system - AI health agent], the AI ​​health agent client system includes the following main functional modules: AI audio-visual dialogue window interaction module, AI audio dialogue interaction module, AI health information source acquisition module (client), AI health assessment result display module, AI health assessment result (I) output module, AI health assessment result (II) output module; the AI ​​health agent cloud server system includes the following main functional modules: AI health information source acquisition module (cloud server), medical and physical examination report AI parsing module, AI health information source processing module, AI health agent preliminary calculation module, AI health agent actuarial module, AI health assessment (I) generation module, AI health assessment (II) generation module.

[0057] As shown in Figure 04 [Schematic diagram of deployment of main functional modules of the system - AI health cloud service platform], in addition to the main functional modules of the AI ​​health intelligent body cloud server system, the AI ​​health cloud service platform's subsystem "cloud service platform other management systems" includes the following main functional modules: system user management module (cloud server), data synchronization storage module (cloud server), network communication and protocol service module, instruction parsing and execution service module, AI health intelligent body development module, and platform system (other) management module.

[0058] As shown in Figure 05 [Schematic diagram of deployment of main functional modules of the system - main operation terminal], in addition to the subsystem AI health intelligent body client system and its main functional modules, the main operation terminal's secondary subsystem health monitoring information management system, health supplementary information management system, and other management systems of the main operation terminal each have their own main functional modules.

[0059] Among them, the main functional modules of the health monitoring information management system are: health monitoring data cleaning module, health monitoring information review module; the main functional modules of the health supplementary information management system are: user health basic file management module, user medical and physical examination report management module, user emotion and ability assessment module, and user health other information management module.

[0060] Among them, the main functional modules of other management systems of the main operation terminal include: user management module (client), personal device binding module (client), data synchronization storage module (client), network communication and protocol terminal module, instruction parsing and execution terminal module, group cluster management module (client), family cluster management module (client), cluster device binding module (client), terminal device system resource call module, main operation terminal (other) application module, and main operation terminal (other) management module.

[0061] As shown in Figure 06 [System Main Functional Module Deployment Diagram - Secondary Operation Terminal], in addition to the subsystem AI Health Agent Client System and its main functional modules, the secondary operation terminal's other management systems share some of the same functional modules as the main operation terminal's other management systems. Furthermore, the secondary operation terminal's other management systems may include a secondary operation terminal (other) interaction module and a secondary operation terminal (other) management module.

[0062] 3. Collaboration mechanism of important functional modules of the system

[0063] It is important to note that the AI ​​health management system deploys an AI health digital human, a virtual human within the "AI Audio-Visual Dialogue Window Interaction Module" and "AI Audio Dialogue Interaction Module" of the operation terminal. This human is a special system program that integrates the functional and interactive modules of the operation terminal. The AI ​​health digital human also serves as the interface and vehicle for interaction between the AI ​​health agent and the user. It presents itself to the user as a digital, lifelike figure, communicating with the user through natural language processing technology, answering health questions and providing health information and advice.

[0064] The operating terminal health monitoring information management system focuses on collecting, organizing, and analyzing real-time health monitoring data from the collection terminals, including key indicators such as health sign data (such as heart rate, electrocardiogram, blood pressure, blood oxygen saturation, body temperature, etc.), health behavior data (such as sleep status, exercise status, etc.), health stress data, and health conclusions drawn through preliminary calculations by the collection equipment. After cleaning the data (removing noise and outliers) and reviewing it (checking the rationality, consistency, and completeness of the data), the system uploads the data to the AI ​​Health Cloud Service Platform and also passes some of the data to the AI ​​Health Intelligent Body Client System to provide the latest monitoring information in a timely manner when users query it.

[0065] The operating terminal health supplementary information management system provides users with a platform for comprehensive management of their personal health supplementary information. In this system, users can establish a basic health profile and enter detailed personal basic information (such as gender, age, height, weight, etc.), work-related information (such as job nature, work schedule, whether there are night shifts and overtime, etc.), lifestyle information (such as exercise status, bad habits, etc.), physical condition (such as whether there are frequent colds, allergies, etc.), basic medical history (such as whether there are underlying diseases, whether surgery has been performed, etc.) and women-specific information (such as menstrual period time, whether pregnant, etc.). Users can also upload medical and physical examination reports, and the system will parse the reports, extract key information and integrate it into the user's health information source. In addition, users can conduct emotional and ability assessments, and the system provides users with personalized health management suggestions based on the assessment results.

[0066] 4. Description of the innovative system architecture

[0067] This differs from some existing health management methods, which consist of a single designated data collection device, an operating terminal device, a common cloud database server, and other hardware and software components in a traditional fixed model. For example, in some existing health management methods, due to the single data collection device, only limited health data can be obtained, which cannot meet the user's needs for comprehensive health monitoring. At the same time, the fixed operating terminal makes it difficult to adapt to different users' usage habits and changing scenarios.

[0068] The health management system involved in the present invention can obtain more and more comprehensive health monitoring data and health supplementary information for the same user through one or more collection devices and multiple channels and methods. The system software architecture consists of components such as collection terminals, operation terminals, and AI health cloud service platforms. Among them, the system has developed AI health intelligent bodies and AI health digital humans based on comprehensive training of artificial intelligence technology and health expertise, and deployed AI health intelligent body clients and AI health digital humans on the operation terminals, and deployed AI health intelligent body cloud servers on the AI ​​health cloud service platform. The system's operation terminals are divided into main operation terminals and secondary operation terminals.

[0069] In addition to the above-mentioned software system, the health management system of the present invention also covers the following hardware systems: acquisition equipment, operation terminal equipment and AI health cloud service platform hardware system. Among them, the operation terminal equipment is divided into main operation terminal equipment and auxiliary operation terminal equipment. The acquisition terminal is installed in the acquisition device, the main operation terminal is installed in the main operation terminal equipment of the user's health management (such as a smart phone, etc.), and the auxiliary operation terminal is installed in the auxiliary operation terminal equipment of the user's health management (such as a smart photo frame or a smart robot, etc.) as an auxiliary tool for user health management.

[0070] In the health management system described in the present invention, the collection device is connected to the operation terminal device to transmit the user's health monitoring data and information; the operation terminal device can communicate bidirectionally with the AI ​​health cloud service platform to achieve the uploading of user health information sources, comprehensive analysis and calculation of the AI ​​health intelligent body, and the reception and output of AI health assessment results. As part of the AI ​​health intelligent body, the AI ​​health digital human is an intelligent health consultant that interacts with the system and the user through natural language, while the AI ​​health intelligent body provides data and decision support for the AI ​​health digital human. The various components of the system work together to jointly promote the operation of health management work.

[0071] A typical data collection device for the health management system described in the present invention is similar to an ordinary wearable smart device, integrating multiple sensors and technologies, including but not limited to an optical heart rate sensor (PPG), an electrode electrocardiogram (ECG), a blood pressure detection sensor, a thermometer, an accelerometer, a gyroscope, etc. Through interaction with these sensors and the application of professional algorithms, the data collection device can drive the sensors in real time to collect user health monitoring data, thereby obtaining key health indicators such as the wearer's health sign data (including but not limited to heart rate, electrocardiogram, blood pressure, blood oxygen saturation, body temperature, etc.), health behavior data (including but not limited to sleep status, exercise status, etc.), health stress data, and preliminary health monitoring information obtained through professional algorithm analysis.

[0072] The acquisition device of the health management system of the present invention transmits health monitoring data and information to the paired operating terminal device through a wired or wireless connection, and the operating terminal device then transmits it to the AI ​​health cloud service platform as the basic data of the user's AI health information source. The health management system supports the acquisition device's timed automatic measurement mode, or the user can actively initiate the measurement of relevant data at any time. As the user cooperates with the continued normal operation of the acquisition device, the user's health monitoring data will continue to accumulate and update, and will be recorded in the user's AI health information source in chronological order.

[0073] In the health management system of the present invention, health monitoring data and supplementary health information complement each other, forming a complete AI health information source for users. Health monitoring data provides the fundamental data support for the system's real-time assessments, while supplementary health information enriches the data dimension. The two work synergistically under the comprehensive analysis and calculation of the AI ​​health agent, making the AI ​​health assessment results more accurate and comprehensive, thereby achieving the goal of providing users with personalized health assessments and recommendations, further demonstrating the innovation of the present invention in the field of health management.

[0074] (3) System data management and security mechanism

[0075] 1. System data transmission technology and communication protocol application

[0076] The system utilizes a variety of wireless transmission technologies to adapt to different scenarios. Bluetooth is suitable for short-range, low-power data transmission, such as between data collection devices and user terminals. It offers convenient connectivity and high stability, but relatively slow transmission speeds. This makes it suitable for transmitting health monitoring data with high real-time requirements but small data volumes, such as heart rate and blood oxygen saturation. This targeted transmission technology is unavailable in traditional health management methods, which may not have such refined data transmission strategies. WiFi is suitable for transmitting larger data volumes in indoor environments, such as between user terminals and the AI ​​health cloud service platform. Its fast transmission speeds and wide coverage enable rapid uploads of user health data and downloads of health assessment results, health recommendations, and other information. Mobile networks (such as 4G and 5G) provide data transmission support for users outdoors or in areas without WiFi coverage, ensuring real-time connectivity to the system, such as receiving real-time health alerts, and enabling data interaction in diverse environments.

[0077] In terms of communication protocols, the HTTP / HTTPS protocol is often used for routine data interaction between operating terminals and cloud service platforms, such as user login, information query, data upload and download, etc. This protocol is based on a request-response model to ensure reliable data transmission in the network. The HTTPS protocol adds an SSL / TLS encryption layer on top of HTTP to ensure the confidentiality and integrity of data transmission and prevent data from being stolen or tampered with during transmission. Traditional health management systems may not have such comprehensive safeguards for data transmission security. The MQTT protocol is mainly used for message push between devices, such as when an acquisition device pushes real-time monitoring data to an operating terminal or cloud service platform. It is lightweight, low-power, efficient and reliable, and can ensure accurate message transmission even in unstable network environments.

[0078] 2. System multi-terminal device data fusion

[0079] The AI ​​health management system of the present invention supports the simultaneous use of primary and secondary operation terminal devices, and the alternating or simultaneous use of multiple types of acquisition devices, thereby ensuring strict corresponding management between user health data and devices, as well as data processing of multiple devices.

[0080] In order to ensure strict corresponding management between user health data and devices, the system of the present invention adopts an advanced data management strategy. On the acquisition device side, the data collected by each device carries unique identification information, such as device serial number, sensor number, etc., which are transmitted to the operation terminal device together with the collected data. After receiving the data, the operation terminal device associates the data with the corresponding user account and device information through the built-in intelligent recognition and matching algorithm. For example, when a smart bracelet and an electronic blood pressure monitor transmit data to a smart phone at the same time, the system can accurately identify which data comes from the bracelet (such as exercise steps, heart rate data) and which data comes from the blood pressure monitor (such as blood pressure value), and integrate it into the user's personal health file to ensure the accuracy and completeness of the data.

[0081] The system also implements a dynamic data update and synchronization mechanism. When users use different devices to collect health data at different times, the system can update the user's health information source in real time and ensure that the health data displayed by each operating terminal device (whether it is the main operating terminal or the secondary operating terminal) is the latest. For example, a user uses a smartwatch to monitor exercise data outdoors, and uses a body fat scale to measure body composition data after returning home. These data will be synchronized to the AI ​​health cloud service platform in a timely manner, and the updated comprehensive health information will be displayed on the user's smartphone and tablet.

[0082] The system also features intelligent data fusion and analysis capabilities. It can integrate data from different types of data collection devices, exploring the inherent connections between them to generate more valuable health assessments and recommendations. For example, by combining exercise and sleep data from a smart bracelet with blood sugar data from a home blood glucose meter, the system can analyze the impact of a user's lifestyle on blood sugar fluctuations and provide personalized exercise and dietary recommendations.

[0083] In summary, the AI ​​health management system framework, through the innovative design of the above-mentioned system hardware architecture, software architecture, and data processing and security mechanisms, the AI ​​health management system of the present invention is significantly superior to existing general health management methods. The innovation of the AI ​​health management system of the present invention in supporting the collaborative work of multiple terminals and multiple devices is not only reflected in the diversified integration of hardware devices and the collaborative operation of software systems, but also in the intelligence and precision of data management and processing mechanisms. Through these innovations, the system can provide users with all-round and personalized health management services, and realize the transformation of health management from the traditional fragmented and single mode to the intelligent and integrated mode. This kind of technological innovation is of great significance in enhancing the user's health management experience and improving the health management effect. It is expected to lead the development trend of the future health management field and provide more powerful guarantees for people's healthy life.

[0084] 3. System data transmission and storage

[0085] In this system, the operation terminal is mainly used as an application computing tool and program, and the acquisition device and the operation terminal device not only serve as acquisition or operation tools, but also play the role of network data transmission tools. When the user's health monitoring data is successfully transmitted to the AI ​​health cloud service platform, the user profile and health information are disassociated from the operation terminal and device. This means that even if the same user replaces the acquisition device or operation terminal device and reinstalls the terminal program, it will not affect the user's AI health information source and AI health assessment results. However, it should be noted that if the user does not fully synchronize the local data to the system before replacing the acquisition device or operation terminal device, some data may be lost.

[0086] Collection devices usually temporarily store health monitoring information for a certain period of time, and the temporary storage time varies from device to device. When the system detects that the user's health monitoring data and information are missing, the AI ​​health digital human will promptly guide the user to find the cause and remind them to upload the data as soon as possible to reduce the risk of data loss. When the operating terminal is connected to the network and working, the health monitoring information and health supplementary information will be quickly and synchronously transmitted to the AI ​​health cloud service platform. The health supplementary information updated when the operating terminal is connected to the network will also be transmitted synchronously. The AI ​​health cloud service platform provides stable and reliable protection for data storage, backup, system security and maintenance.

[0087] In addition, taking into account the special needs of some users or the restrictions of certain regional policies, health data may be restricted from leaving the country or transmitted to a cloud platform (such as the aforementioned AI health cloud service platform). In this case, the health management system can deploy the AI ​​health intelligent body cloud service system to the operating terminal in a manner that optimizes the configuration and reduces the installation volume. Although this deployment method will affect the accuracy of the AI ​​health assessment results to a certain extent, it can still basically achieve a closed-loop process for health management. This implementation method is within the scope of the innovation of the present invention.

[0088] For example, in regions with strict regulations on data transmission, the system can flexibly adjust its deployment method based on local policies, ensuring that users are provided with the most comprehensive health management services possible while maintaining compliance. At the same time, this localized deployment method also provides an option for users who are concerned about data security, allowing them to use the health management system with greater confidence.

[0089] 4. System data management and security protection

[0090] Regarding data encryption and transmission safeguards, the system employs multiple encryption algorithms to ensure data security. Sensitive user information (such as personal identity information and medical records in health records) is encrypted and stored using the AES (Advanced Encryption Standard) algorithm, ensuring data security within the storage medium. Traditional health management systems may not be able to achieve this level of encryption for data storage security. During data transmission, the RSA (Rivest–Shamir–Adleman) algorithm is used for key exchange to establish a secure encryption channel. The transmitted data is then encrypted using the AES algorithm to prevent interception and theft during network transmission. Regarding key management, the system utilizes a centralized key management system and regularly updates keys to ensure their security and effectiveness.

[0091] The system also incorporates comprehensive security measures. Regarding identity authentication, the system verifies users via username and password during registration and login. It also supports biometric technologies such as fingerprint and facial recognition as supplementary authentication methods to ensure user authenticity. Traditional health management systems may rely solely on simple account and password verification, which is less secure. Regarding device access, data collection devices and operating terminals require device authentication when connecting to the system. This authentication uses information such as the device serial number and MAC address to identify the user and prevent unauthorized access. Regarding access control policies, the system sets different access levels based on user roles and permissions. For example, regular users can only access their own health data, while administrators can access and manage the health data of a limited range of users. Appropriate access rights are also set for different functional modules and data resources. For example, the health assessment result generation module is accessible only to authorized professionals or system algorithms. This prevents unauthorized access and data tampering. Traditional health management systems may not implement precise and strict permission management. Regarding data encryption, in addition to encrypting sensitive information, encryption is also applied to the entire database to ensure data security within the storage medium and prevent data leaks.

[0092] (4) System user and device management

[0093] In today's digital health management landscape, user management and interaction mechanisms are crucial for effective system operation and meeting user needs. Traditional health management systems often suffer from numerous shortcomings in user management, such as chaotic user information management, limited and unintelligent interaction methods, and inflexible and inaccurate permission settings. This invention addresses these issues by building a comprehensive and innovative user management and interaction mechanism, aiming to provide users with a more convenient, secure, and personalized health management experience.

[0094] 1. User Management System

[0095] User registration and login process (including multiple authentication methods): When using this system, the user must first open the AI ​​health management system application on the main operating terminal device (such as a smartphone, tablet computer, etc.). After entering the registration interface, fill in basic information such as user name, password, contact information (such as mobile phone number or email address). The system will send a verification code to the contact information filled in by the user, and the user needs to enter the correct verification code to complete the preliminary verification. During the login process, the user enters the registered username and password, and the system verifies it. At the same time, to improve security, the system supports multiple authentication methods, such as fingerprint recognition, facial recognition and other biometric technologies as auxiliary verification means. For example, when logging in, the user can choose to use fingerprint recognition. Just place your finger on the fingerprint recognition area of ​​the device, and the system can quickly verify and log in to ensure the authenticity and security of the user's identity.

[0096] User Information Management (Classification and Update Methods for Basic Personal Information and Health Records): User information management covers basic personal information and health record management. Basic personal information, including name, gender, age, height, weight, and occupation, is entered by the user during registration and can be subsequently modified and updated within the system. The health record provides a comprehensive record of the user's health status, including basic health records, medical and physical examination reports, and emotional and ability assessment results. The basic health record includes work-related information (such as job nature, work schedule, night shifts, and overtime), lifestyle information (such as exercise status and bad habits), physical condition (such as frequent colds and allergies), underlying medical history (such as underlying diseases and surgical procedures), and specific information for women (such as menstrual period duration and pregnancy status). Users can enter basic health record information in the corresponding module of the user terminal, and the system will categorize and store it. Medical and physical examination reports can be uploaded by users in various formats (such as PDF and JPEG). The system automatically recognizes and parses the format, extracting key information and integrating it into the health record. The system provides assessment tools (such as intelligence tests and emotional control tests) for emotion and ability assessment results. Once the user completes the assessment, the results are automatically stored in the user's health record. Users can review and update their health records at any time, and the system will record update history, making it easier for users to track and manage their health information.

[0097] Multiple ways for users to interact with the system (interaction scenarios and implementation technologies such as health consultation, information query, and operation command input): Users can interact with the system in a variety of ways. For health consultation, users can ask health questions to the AI ​​health digital human through text input or voice commands, such as "I've been having headaches lately. What could be the cause?" The AI ​​health digital human uses natural language processing technology to understand the user's question and then provides corresponding answers and suggestions based on the system's knowledge and the user's health information sources. For information query, users can use the operation terminal to query their health monitoring data (such as real-time heart rate, blood pressure, blood sugar, etc.), health assessment results, and health record content. The system uses database query functions to quickly retrieve and display relevant information. For operation command input, users can use touch operations (such as clicking and sliding) on ​​the operation terminal to perform various operations, such as setting health reminders, adjusting data collection device parameters, and registering for health activities. For example, if a user wants to set a daily exercise reminder for 8:00 a.m., they can simply perform the corresponding operation on the reminder setting interface of the operation terminal. The system's user-friendly interface design and efficient interaction technology ensure that users can easily interact with the system and obtain the information and services they need.

[0098] 2. User Rights Management

[0099] Specific permission settings for different types of users (individual users, cluster administrators, custodians, ordinary cluster members, etc.) in different application modes (individual mode, group cluster mode, family cluster mode): In individual mode, individual users have full access to their own health data, including viewing and editing basic health files (such as modifying personal basic information, updating changes in living habits, etc.), viewing health monitoring data (such as viewing heart rate, blood pressure and other data in real time), viewing health assessment results and detailed reports, conducting health consultations (freely communicating with AI health digital people about health issues), and formulating and implementing personalized health management plans based on system recommendations (such as adjusting diet and exercise plans). Individual users can also apply for or cancel health concerns for other users (including individual users and cluster users), but the supplementary health information of the concerned objects will not be collected in their own health information source.

[0100] In the group cluster model, the enterprise administrator has the highest permissions, allowing them to view aggregated health data for all employees (such as overall health indicators and disease distribution), formulate corporate health management policies (such as employee checkup schedules and health event schedules), organize company-wide health events (such as group checkups, health seminars, and health competitions), and assign and manage permissions for subordinate department administrators and employees (such as setting the scope of department administrator permissions and granting or restricting certain employee permissions). Department administrators can view detailed health data for their department's employees (including personal health records, monitoring data, and assessment results), organize health activities within their department, review supplementary health information for their department's employees (such as verifying the authenticity and validity of uploaded medical reports), and provide health monitoring and health management services for their department's employees (with employee authorization). Regular employee members' permissions primarily focus on viewing their own health data and assessment results, participating in company or departmental health events (such as signing up for checkups and attending health seminars), modifying their supplementary health information within certain limits (such as updating changes in personal habits), and consulting the system or administrator on health issues.

[0101] In the family cluster model, the family administrator is responsible for the custody of family members' health information, including custody of supplementary health information for family members (such as establishing basic health files, uploading medical and physical examination reports, conducting emotional and ability assessments, etc.), managing the binding of family members' collection devices and auxiliary operation terminal devices (performing device binding and unbinding operations), viewing family members' health data summaries and analysis reports (understanding the overall health status of the family), and paying attention to the health of family members (actively obtaining family members' health status and the working status of collection devices). Ordinary family members' permissions include viewing their own health data and assessment results, conducting health consultations with AI health digital people, modifying their own supplementary health information within a certain range (such as updating changes in personal living habits, etc.), and viewing some family members' health information (such as immediate family members' health information) with authorization.

[0102] Permission change mechanism (trigger conditions, approval process, etc.): There are various trigger conditions for permission changes. In an enterprise or organization, position change is one of the common triggering factors. For example, when an employee is promoted to a department administrator, the system will automatically trigger the permission change process and grant him the corresponding management authority. When the health custody relationship changes, such as the replacement of the custodian or the adjustment of the custody object, it will also trigger a corresponding change in permissions. In addition, according to the adjustment of the company's health management policy, such as temporary changes to employee health data access rights during certain special periods, the administrator can also manually trigger the permission change process. In terms of the approval process, permission changes caused by position changes are usually approved by the company's human resources department or higher-level administrators. The approval steps include submitting a permission change application (including information such as the reason for the change, a comparison of permissions before and after the change, etc.). After receiving the application, the approver will review it according to the company's regulations and actual situation. If approved, the system will automatically update the user's permissions; if not, the applicant will be notified and the reason will be explained. For permission changes caused by changes in health custody relationships, the original custodian or the custodian object submits an application. After confirmation by the relevant parties (such as the custodian object agrees to the new custodian's custody application, or the original custodian agrees to terminate the custody relationship), the system updates the permissions. Permission change approvals generally have a time limit, such as completing the approval within 3-5 business days of submitting the application. This ensures the timeliness and effectiveness of system permission management and avoids delays in permission changes that affect normal user use or the implementation of corporate health management work.

[0103] The relationship and constraints between permissions (coordinated control of data access permissions and operational permissions): Permissions in the system are closely linked and constrained. Data access permissions and operational permissions are controlled in a coordinated manner. For example, ordinary employees only have access to view their own health data. Therefore, they cannot modify or delete other employees' health data, ensuring the privacy and security of employee health data. While department administrators can view the health data of their employees, their operational permissions are subject to certain restrictions. For example, they cannot freely modify core employee health data (such as medical diagnosis results) and can only modify certain updateable information (such as contact information in the employee's basic health file). These modifications are recorded by the system for traceability and auditability. Enterprise administrators have higher data access and operational permissions, but sensitive operations (such as deleting large amounts of employee health data) require a higher-level approval process (such as approval from senior management or joint approval by multiple people) to prevent abuse of permissions. This constrained relationship between permissions effectively safeguards system security and data accuracy and integrity, ensuring that users use the system legally and compliantly within their permissions. It also provides an effective permission management mechanism for health management within an enterprise or organization, promoting the orderly implementation of health management work.

[0104] 3. System user and device management mechanism

[0105] Given that the number of devices (and expanded monitoring devices) for collecting health monitoring data and health supplementary information is diverse and their usage methods are complex, in order to ensure that the data accurately corresponds to the user, the health management system of the present invention sets up the following user management system and device binding module, as well as its related system operation mechanism.

[0106] The user management system of this system consists of the operation terminal user management module, the group cluster management module, the family cluster management module, and the system user management module of the AI ​​health cloud service platform. These modules work together to provide users with corresponding operations and functional permissions in different health management scenarios.

[0107] The system automatically generates a "User Account Unique Identification Code" and a "User ID Number" for each registered user. These two codes correspond one to one and represent each user, ensuring that each user is unique and clearly identifiable within the entire health management system. The user's name is used only as a basic information attribute and is not used for unique identification. It is normal for multiple users with the same name to exist in the system.

[0108] The system automatically generates a "User Identity QR Code" for each user's "User Account Unique Identification Code" to facilitate interaction between users. For example, a user can use the system's built-in scanning tool or a third-party scanning tool to scan another user's "User Identity QR Code" to apply for health care for that user. Alternatively, a user can enter another user's "User Identity ID Number" in the health care window and apply for health care through search and selection.

[0109] A user can apply individual user health management mode, group cluster health management mode, and family cluster management mode at the same time. The use of each mode will be clearly marked in the user application file, and its role in the relevant cluster health management mode will also be clearly marked in the user application file.

[0110] When a user establishes a health care relationship with another user, their role as the health care provider or health care recipient in this relationship will also be clearly marked in the user's application profile. In addition, other operations and settings performed by the user in the health management system also follow the same principles.

[0111] The system data synchronization storage module (including the client and cloud server) is used to save the user's latest application profile information. At the same time, through the system communication protocol and instructions, the user management modules of the main and secondary operation terminals and the AI ​​health cloud service platform are synchronized to update the user application profile information, thereby ensuring the consistency and integrity of the configuration and data of the entire system, and the system operates or is managed according to the updated configuration conditions.

[0112] The device binding module of the health management system is divided into personal device binding module and cluster device binding module, aiming to enable system users to manage, use and maintain devices in various health management application scenarios.

[0113] When a user registers a user account using a primary operating terminal device, the system automatically reads the "Device Serial Number" and "MAC Address" of the primary operating terminal device, then generates a "Primary Operating Terminal Device Unique Machine Identification Code" and uses this code as the code for the user's primary operating terminal device in the system, recording it in the user's device profile. Similarly, when a user binds a collection device or secondary operating terminal device, the system automatically reads the "Device Serial Number" and "MAC Address" of the corresponding device, generates a "Collection Device Unique Machine Identification Code" or a "Secondary Operating Terminal Device Unique Machine Identification Code" and uses this identification code to represent the user's collection device or secondary operating terminal device in the system, recording it in the user's device profile.

[0114] The corresponding device's unique machine identification code is associated one-to-one with the user's "user account unique identity identification code" to ensure that the primary operation terminal device is used by the associated user, that the health monitoring data and information obtained by the collection device belong to the associated user's own health information source, and that the user of the secondary operation terminal device is the associated user.

[0115] The system automatically generates a "Device QR Code" based on each device's unique machine identification code, making it easy for users to perform on-site or remote binding operations. For example, a user can use the system's built-in scanning tool or a third-party scanning tool to scan the "Device QR Code" of a collection device to bind, or they can search and select the device through Bluetooth device pairing to bind.

[0116] A user's account is unique and can only be logged in and used on one main operation terminal device. When the user changes the main operation terminal device and logs in on the new device, the account on the original main operation terminal device will automatically log out. At the same time, a user can enable multiple collection devices, which can be used interchangeably or simultaneously to collect more health monitoring data for the user. In addition, a user can also enable two or more secondary operation terminal devices. It is also possible to use different secondary operation terminal devices in different time periods for health consultation or other applications with the AI ​​health digital person, or use two or more secondary operation terminal devices to interact with the AI ​​health digital person at the same time.

[0117] The latest user device profile information is saved with the help of the system data synchronization storage module (including the client and cloud server). At the same time, through the system communication protocol and instructions, the device management modules of the main and secondary operation terminals and the AI ​​health cloud service platform are synchronized to update the user device profile information, ensuring the consistency and integrity of the configuration and data of the entire system, and the system operates or is managed according to the updated configuration conditions.

[0118] (V) System user health management interaction

[0119] System user health management interaction refers to the collection, transmission, processing, feedback and operation process based on different permissions and roles of health information between users and various system components (including collection equipment, operation terminal equipment, AI health intelligent bodies, AI health cloud service platforms, etc.) in the AI ​​health management system. It covers various modes such as individual user's own health management, health concerns between users and cluster user health management, aiming to achieve personalized, precise and systematic health management services.

[0120] 1. Basic model of health management

[0121] like Figure 15[System User Health Management and Interaction Diagram - Basic Health Management Model] shows the basic operations and system workflow for an ordinary individual user to perform health management, including the closed loop of health information collection, transmission, processing and evaluation result feedback.

[0122] Network interconnection (such as Figure 15 (As shown in line ④): The user's collection device and the main operation terminal device remain bound (Bluetooth, WiFi, etc.), and the main operation terminal device and the AI ​​health cloud service platform maintain wireless network interconnection to achieve data transmission or related control.

[0123] Health information collection and transmission (such as Figure 15 (Line ②): The user wears (or uses) the data collection device to obtain health monitoring data and health monitoring information, and transmits it to the main operation terminal. At the same time, the user enters supplementary health information into the health supplementary information management system of the main operation terminal, which is then integrated into a health information source and transmitted to the AI ​​health cloud service platform.

[0124] Data processing and health assessment: In the AI ​​health cloud service platform, the data synchronization storage module performs data storage management, and the various functional modules of the AI ​​health intelligent body cloud server system process the health information source, including acquisition, analysis, preliminary calculation and actuarial operations, to generate health assessment results.

[0125] Health assessment result feedback and user interaction (such as Figure 15 As shown by line ③): The AI ​​health cloud service platform feeds back the health assessment results to the main operation terminal, and the user can view the results on the relevant interface of the main operation terminal (as shown in Figure 3). Figure 23 and interact with the AI ​​health digital person in the main operation terminal to obtain health advice, etc. (as shown in Figure 25 shown).

[0126] 2. Health-focused model

[0127] like Figure 16 The [System User Health Management and Interaction Diagram - Health Concern Mode] shows the interaction process for health concerns between two users, an important application mode for system user health management interactions. One user can monitor the health of another user by asking the AI ​​health digital human about the other user's health status and the working status of the data collection device on their own operating terminal.

[0128] In this application mode, this user is the "follower" and the other user is the "followed object". (e.g. Figure 16 As shown by line ④): User is the follow object, and user is the follower.

[0129] As a follower, the user can follow multiple followers at the same time. The follower list will be automatically listed in the AI ​​Health Digital Human dialogue window (such as Figure 25 If no key subject is selected, the AI ​​health digital person is considered to be consulting the health information of the person concerned.

[0130] It should be noted that due to system authority restrictions, if the follower informs the AI ​​health digital person of the health supplementary information of the follower, it will be deemed invalid information and the AI ​​health intelligent body will ignore this information and will not collect it into the health information source of the follower. Figure 16 As shown in the figure, when the user talks with the AI ​​health digital human in the "AI audio-visual dialogue window" of the operation terminal, although the figure uses the connection line ②, it only means that the user consults the AI ​​health digital human about the health issues of the person he is concerned about (only special instructions for special circumstances).

[0131] 3. Health hosting model

[0132] like Figure 17 [System User Health Management and Interaction Diagram - Health Hosting Mode] shows the interaction process of health hosting between two users, which is an important application mode of system user health management interaction. A user uses the main operation terminal device to enable cluster user mode and become a cluster administrator to exercise health hosting rights (such as Figure 33 As shown), become the custodian; another user uses the secondary operation terminal device to become its health custodian, and the two parties establish a "health custodian" relationship (as shown Figure 17 (as shown by line ⑤).

[0133] Users cooperate with each other to complete the user's health custody and health management work. The specific operations are as follows:

[0134] The user exercises health custody authority to bind the collection device to the secondary operation terminal device (or remotely bind), and the user wears (or uses) the collection device and works normally. Figure 17 As shown by line ⑥, ensure that the secondary operation terminal device is connected to the Internet so that it can maintain data transmission and control with the AI ​​health cloud service platform.

[0135] like Figure 30 As shown, when a user exercises health custody authority, he registers a user account. The AI ​​health management system will automatically establish a health custody relationship and record it with other users, supporting the user to enjoy subsequent health custody work and rights for the user. At the same time, the user automatically has the right to pay attention to the user's health.

[0136] like Figure 33As shown, the user manages the supplementary health information of the user, that is, establishes the basic health file of the entrusted object, uploads the medical and physical examination reports of the entrusted object, and evaluates the emotions and abilities of the entrusted object.

[0137] Users can turn on or wake up the AI ​​health digital person through voice on their secondary operation terminal, consult about their health problems through dialogue, or get health reminders from the AI ​​health digital person, or answer the AI ​​health digital person's questions as one of the user's AI health information sources, so that the AI ​​health management system can provide users with more accurate health assessment results.

[0138] As the user's health custodian, in addition to having the right to pay attention to the user's health, the user can also consult the AI ​​health digital person on his or her main operation terminal about the user's health status and the use of related equipment. The user can custodian multiple custodians at the same time, and the list of custodians will be automatically listed in the AI ​​health digital person dialogue window (such as Figure 25 Since users can follow multiple health care objects at the same time, the managed objects and the health care objects they follow will be distinguished by different labels.

[0139] In particular, when consulting with the AI ​​health digital person about the health status of the managed object, the user can also actively provide supplementary health information of the managed object (such as the user), which will become one of the user's health information sources; but if the target is the user's health concern object, the user has no right to provide supplementary health information of the concern object, and even if it is provided, it will be automatically deemed invalid by the system.

[0140] If other users want to pay attention to the user's health, the user can view the other party's application information on his or her main operating terminal device and choose "Agree or Reject" as appropriate.

[0141] If a user wants to follow other users' health, they can apply to the other user on their main operating terminal device. Once the other party agrees, the user will have the right to follow the other user's health. At this time, they can directly tell the AI ​​health digital person "Please help me switch to following the person *** (name or relationship)", and the user will now consult with the AI ​​health digital person about the health of the person they are following. If the user wants to switch back to consulting about their own health, they can tell the AI ​​health digital person "Please help me switch to following myself (or similar natural language expression)".

[0142] If the user's health custody rights are transferred to other users, the user can apply to transfer this right to other users through relevant permission operations. If the other user agrees to accept it, the other user and the user will become a new health custody relationship. The custody relationship between the user and the user will be terminated, and the user will no longer have the right to pay attention to the user's health (unless a normal application is made to pay attention to the user's health and the consent of the custodian is obtained).

[0143] This application mode can be used in the home, where the elderly may not be proficient in operating smart devices and cannot independently complete health management-related settings. For example, younger family members can act as custodians and use their own primary operating terminal devices to manage health information for the elderly (custodian objects). Young family members can register accounts for the elderly, establish basic health files, upload medical and physical examination reports, conduct mood and ability assessments, etc. At the same time, bind the collection devices (such as smart bracelets) and secondary operating terminal devices (such as smart speakers) used by the elderly. The custodian can also use the AI ​​health digital human to pay attention to the health status of the custodian and the working status of the collection equipment. When the elderly have any abnormalities in their body, they can detect and take measures in time. In addition, the elderly can also use the secondary operating terminal devices (such as smart speakers) to consult and communicate with the AI ​​health digital human for health.

[0144] This application mode is also suitable for remote operations, such as remote registration of user accounts, remote binding of user devices, remote health management operations, and remote monitoring of others' health. It adapts to more application scenarios and needs of current health management users.

[0145] (6) System workflow of AI health management

[0146] like Figure 18 [AI Health Management - System Workflow Diagram] shows the workflow steps of the AI ​​health management system and its interaction with users when users actually apply AI health management.

[0147] First, the collection terminal acquires the user's health monitoring information to form an AI health information source and transmits it to the AI ​​health cloud service platform. The platform stores, parses, and processes the AI ​​health information source. The AI ​​health agent's preliminary calculation module performs preliminary calculations based on this information to generate the AI ​​health assessment result (I), which mainly reflects the status under the basic health monitoring data. If the user does not complete the supplementary health information or does not communicate with the AI ​​health digital human to supplement the health information, the health assessment result will be based on this.

[0148] Next, based on the results, the system may remind the user to supplement their health information. After the user performs the relevant operations, the operation terminal transmits and updates the AI ​​health information source again. The platform reprocesses the information, and the AI ​​health agent actuarial module integrates more factors to perform precise calculations and generate the AI ​​health assessment results (II). As users continue to add information and devices continue to collect data, this process repeats itself, and each cycle may involve additional processing such as information source verification and adjustment of the calculation basis. Through this cyclical dynamic process, the health assessment results are continuously updated and optimized, approaching accuracy and comprehensiveness, reflecting the core mechanism of the entire AI health system.

[0149] It is particularly important to note that the AI ​​health management system distinguishes between AI health assessment results (I) and AI health assessment results (II), primarily based on considerations of computing power costs in actual project commercial operations. These two assessment results are generated by the AI ​​health agent's preliminary calculation module and actuarial calculation module, respectively. Relatively speaking, the actuarial calculation module utilizes significantly higher computing power costs than the preliminary calculation module. Specifically, when the system provides AI health assessment results (I), because the user has not yet provided supplementary health information, the system only possesses the user's basic health data, such as limited health monitoring data acquired by a single collection device and brief basic user information. This data lacks comprehensiveness and is formatted and templated uniformly, resulting in a relatively simple health information source. In this case, regardless of whether the AI ​​health agent's preliminary calculation module or the actuarial calculation module is used, the generated AI health assessment results and their accuracy do not differ significantly, and the accuracy is relatively low. From the perspective of actual project commercial operations, using the AI ​​health agent's preliminary calculation module, which has lower computing power costs, to generate AI health assessment results (I) is a wise choice. However, if cost factors are not considered, or in order to simplify the system, this system can also uniformly adopt the AI ​​health intelligent actuarial module to generate AI health assessment results, that is, no longer distinguish between AI health assessment results (I) and AI health assessment results (II). Without engaging in creative work, the embodiments of this situation obtained by ordinary technicians in this field all fall within the scope of protection of this invention.

[0150] To better understand the workflow steps of the AI ​​health management system and its interactions with users, we first explain three important specialized terms: "AI health consultation," "AI health information source," and "AI health assessment results."

[0151] 1. AI health consultation

[0152] In the AI ​​health management system, AI health consultation refers to the process in which users interact with AI health digital humans in natural language through operating terminals to obtain professional health advice, AI health assessment results, and comprehensive health assessment reports (or special content).

[0153] During the AI ​​health consultation process, the AI ​​health digital person may conduct a "diagnosis" with the user, that is, asking the user behavioral questions related to the user's health (generally information unknown to the AI ​​health information source), such as "How do you feel about your mental state recently?", "Have you been constipated recently?", "Are you pregnant, and how long is the pregnancy?" and other questions that may be related to the user's current health status or information that needs to be supplemented with the customer's consultation content, so that the AI ​​health digital person can analyze and evaluate the user's health more accurately.

[0154] At the same time, during the AI ​​health consultation process, the AI ​​health digital human may also remind users of abnormal situation reports when using collection equipment, operational problems that users may encounter in filling out or uploading information in the "Health Supplementary Information Management System" (including missing, abnormal, expired, etc.), and medical appointment event reminders (such as postoperative review reminders, medication reminders, etc., which require users or custodians to upload relevant medical documents in the Health Supplementary Information Management System).

[0155] In addition, the custodian in the cluster user mode can provide health consultation to the AI ​​health digital person for the custodian object, and answer the AI ​​health digital person's questions on the custodian object's behalf.

[0156] 2. AI Health Information Source

[0157] The AI ​​health information source refers to the collection of all information related to user health obtained through various channels within the AI ​​health management system. It is the source of raw data for AI health agents to conduct AI analysis. The AI ​​health information source is a comprehensive and systematic database of raw health information, providing the data foundation for the system to conduct accurate health analysis, risk assessment, develop personalized health management plans, and make decisions for the AI ​​health agent. The comprehensiveness and quality of the AI ​​health information source depend on the different types of health data that can be collected by different collection devices, as well as the completeness and authenticity of the information submitted or uploaded by users to the AI ​​health supplementary information management system.

[0158] 3. AI health assessment results

[0159] In the AI ​​health management system, AI health assessment results, also known as "health assessment results," are conclusive reports drawn from a comprehensive analysis of all available user health information sources. These reports also include reports on anomalies in user data collection equipment, reports on issues with data that users may have submitted or uploaded to the "Health Supplementary Information Management System" (including missing, abnormal, expired, and other information), and management reports on user medical appointment events obtained through analysis.

[0160] Part of the evaluation results will be displayed in the relevant interface position of the "Health Information Display Interface Interaction Module" (such as Figure 23 ①②③ in the figure) as AI health analysis reports or suggestions; and relevant reminder information is presented on the relevant reminder interface, such as abnormalities in the collection device, abnormalities in health supplementary information, medical appointment events, etc. (such as Figure 24 In the process of AI health consultation with users, AI health digital people will summarize or extract “AI health assessment results” to answer users’ consultation questions (such as Figure 25 ⑧), or further explain and suggest to the user, or provide the user with the above-mentioned related reminder information.

[0161] The generation of AI health assessment results depends on the AI ​​health information source, and the comprehensiveness and quality of the AI ​​health information source depends on the different types of health monitoring data that can be collected by different collection devices, as well as the completeness and authenticity of the information reported or uploaded by users in the AI ​​health supplementary information management system.

[0162] The AI ​​health assessment results are divided into two types: AI health assessment results (I) and AI health assessment results (II), depending on their AI health information sources or whether they contain additional health supplementary information.

[0163] In the AI ​​health management system, the AI ​​health assessment result (I) is defined as the health assessment result obtained without the user performing any operations on the supplementary health information. Specifically, (1) no operations are performed in the supplementary health information management system (such as establishing a basic health profile for the user, uploading medical and physical examination reports for the user, or evaluating the user's emotions and abilities); (2) the user or custodian does not communicate with the AI ​​health digital person or provide the AI ​​health digital person with any information related to the user's own health. In this case, the health assessment result obtained by the user or custodian is the AI ​​health assessment result (I).

[0164] In the AI ​​health management system, AI health assessment results (II) are defined as the health assessment results obtained when a user updates their supplementary health information. Specifically, (i) the user performs the initial or secondary update of health information in the supplementary health information management system (such as establishing a basic health profile, uploading user medical and physical examination reports, and conducting mood and ability assessments on users); (ii) the user or custodian communicates with the AI ​​health digital person and provides the AI ​​health digital person with one or more pieces of information related to the user's own health. In this case, the health assessment result obtained by the user is the AI ​​health assessment result (II).

[0165] 4. AI health management operation process

[0166] like Figure 18[AI Health Management - System Workflow Diagram] shows that in the process of the AI ​​health management system obtaining absolute AI health assessment results (Ⅰ), there is a rigorous working mechanism and process sequence, which follows the three-stage principle of "data input, data calculation, and result output". First, at the operation terminal, the data is obtained through the acquisition module, and then transmitted to the cloud platform after being cleaned, reviewed and processed by the storage module. After receiving it, the cloud platform acquires, stores and processes the data, calculates it by the preliminary calculation module, and then obtains the result by the generation module. Finally, the result output module transmits it back to the operation terminal, and the supplementary information reminder module determines whether additional information is needed. The relevant modules then remind the user at the operation terminal. Each step is closely connected to form a complete process. The details are as follows:

[0167] ⑴ Operation terminal related work steps (inputting AI health information source into the cloud platform)

[0168] Step 1: AI Health Information Source Acquisition Module (Client): After the client acquisition device and the operation terminal update data synchronization, this module is responsible for acquiring data related to the user's health monitoring information, providing the raw data basis for subsequent processing. This data may come from information collected by various sensors in the acquisition device.

[0169] Step 2, health management data cleaning management module: clean the original health monitoring information obtained, including removing noise and outliers in the data, processing possible missing values, and standardizing the data to make it meet the requirements of subsequent system processing and improve data quality.

[0170] Step ⑶, health monitoring information review management module: review health monitoring information from multiple dimensions, including checking the rationality of the information, such as whether the monitored physiological indicators are within a reasonable range; the consistency of the information, that is, whether the information from different sources matches each other; and the integrity of the information, ensuring that key information is not missing, in preparation for the integration of health information sources.

[0171] Step 4, data synchronization storage module (client): The cleaned and reviewed health monitoring information is stored locally, and it also prepares for data transmission to the AI ​​health cloud service platform, ensuring the security and integrity of the data locally, and enabling timely and accurate transmission to the cloud platform.

[0172] ⑵Related work steps of AI health cloud service platform (AI health computing)

[0173] Step 5, AI health information source acquisition module (cloud server): obtain health information source data transmitted from the operation terminal on the cloud server. These data contain health monitoring information that has been preliminarily processed by the client, providing a data basis for subsequent further processing on the cloud platform.

[0174] Step 6, data synchronization storage module (cloud server): store and manage the acquired health information source data, use efficient data storage technology and algorithms to ensure the security, integrity and accessibility of the data, and classify, store and index the data for subsequent processing and query.

[0175] Step 7, Medical and Experience Report AI Parsing Module: In this step, uploaded medical and physical examination reports are parsed, including format recognition, content extraction, semantic understanding, and standardization, providing data support for subsequent evaluations. However, if the customer obtains an absolute AI health assessment result (I), this step is skipped because no relevant operations are involved (for example, the user did not upload a medical and physical examination report).

[0176] Step 8, AI health information source processing module: This module further processes the acquired health information source, uses advanced algorithms and technologies to deeply clean, organize and standardize the data, extract key information, and provide a more accurate and standardized data foundation for AI operations.

[0177] Step 9, AI Health Agent Preliminary Calculation Module: Based on processed health information source data, the AI ​​large model and related machine learning algorithms perform preliminary calculations. This calculation primarily provides a preliminary assessment of the user's health status based on basic health monitoring data. Since it does not involve supplementary health information or interaction with the AI ​​health digital human, the assessment is relatively basic and preliminary.

[0178] Step ⑽, AI Health Assessment (I) Generation Module: Based on the preliminary calculation results, this module generates the AI ​​Health Assessment Result (I). This result mainly reflects the user's health status assessment based on basic health monitoring data, including some basic health indicator analysis and risk warnings.

[0179] ⑶ Operation terminal related work steps (receiving AI health assessment results output by the cloud platform)

[0180] Step ⑾, AI health assessment result (I) output module: responsible for receiving and transmitting the AI ​​health assessment result (I) generated by the AI ​​health cloud service platform to the relevant display and interaction modules of the operation terminal, ensuring that the assessment result can be accurately delivered to the operation terminal.

[0181] Step ⑿, AI Health Supplementary Information Reminder Module: Based on the obtained AI health assessment results (I), this module determines whether the user needs to supplement their health information. If so, it will trigger a subsequent reminder operation, prompting the user to perform relevant operations in the health supplementary information management system to complete their health information.

[0182] Step 7: The operating terminal reminds the user to supplement health information in the following management module

[0183] AI health assessment result display module: Display the relevant content of AI health assessment results (I) on the operation terminal (such as Figure 23 (As shown in ①②③), including but not limited to health indicators, risk assessment and other information, so that users can intuitively understand the preliminary assessment results of their health status, and remind users that they may need to supplement their health information. AI audio-visual dialogue window interaction module, that is, on the main operation terminal, the AI ​​health digital human interacts with the user for AI health consultation, reminding the user to supplement health information (such as Figure 24 The AI ​​audio dialogue interaction module is generally used on the secondary operation terminal. The AI ​​health digital human interacts with the user through audio dialogue to provide AI health consultation or remind the user to supplement health information.

[0184] 5. AI health management system integration and progressive mechanism and process

[0185] like Figure 18 [AI Health Management - System Workflow Diagram] shows that in the AI ​​health management system, the mechanism of AI health assessment results (Ⅰ) is based on a preliminary assessment of basic health monitoring data. The mechanism of AI health assessment results (Ⅱ) is based on this. When the user operates on the health supplementary information or adds health supplementary information after communicating with the AI ​​health digital person, the health assessment results are further generated. The combination and progressive mechanism of AI health assessment results (Ⅰ) and AI health assessment results (Ⅱ) is intended to make the system's health assessment results for users infinitely more accurate and comprehensive through an infinite loop process, as the user's health supplementary information and device collected data are updated. The specific combination and progressive mechanism and process are as follows:

[0186] ⑴ Steps ⑴-⒀ of the AI ​​health assessment results (Ⅰ) operation workflow

[0187] Step 7: The user supplements health information in the following management module

[0188] User basic health file management module: Users operate their personal basic health files in this module, including supplementing or correcting personal basic information (such as age, gender, occupation, etc.) and family medical history, etc., to provide more comprehensive and accurate background information for subsequent evaluations.

[0189] User medical and physical examination report management module: Users upload medical and physical examination reports, which contain detailed physical examination data (such as various physiological indicators, disease diagnosis results, etc.) and doctor's diagnostic recommendations and other information, providing key basis for the system's accurate evaluation.

[0190] User situation and ability assessment module: Users conduct assessments of their own situation and abilities, such as evaluating their psychological state (such as anxiety and depression levels) and life abilities (such as self-care ability and athletic ability), so that the system can understand the user's health status from multiple dimensions.

[0191] AI audio-visual dialogue window interactive module (i.e., AI health digital human dialogue window): Users conduct AI health consultation dialogues with the AI ​​health digital human through this module and provide additional information related to their own health. This information may be a further explanation of previously uploaded information or supplement new health status details.

[0192] ⑵AI health management system related work steps (repeat steps ⑴-⑻, and execute steps A1 and A2)

[0193] Repeat steps ⑴-⑻: This part of the process is similar to steps ⑴-⑻ when obtaining the AI ​​health assessment results (Ⅰ). This includes the acquisition, cleaning, review, and storage of health monitoring information by the operation terminal, and the acquisition, storage, and processing of health information sources by the AI ​​health cloud service platform, providing an accurate data foundation for subsequent actuarial calculations.

[0194] Step A1, the AI ​​Health Agent Actuarial Module: Based on processed and supplemented health information, this module applies more sophisticated AI algorithms and models to perform actuarial calculations. It comprehensively considers multiple factors, including uploaded medical and physical examination reports, information obtained through conversations with the AI ​​Health Agent, and basic health monitoring data, to provide a more in-depth and accurate assessment of the user's health status.

[0195] Step A2, AI Health Assessment (II) Generation Module: Based on the actuarial results, this module generates the AI ​​Health Assessment Result (II). This result is more comprehensive and accurate than the AI ​​Health Assessment Result (I), including more detailed health indicator analysis, risk assessment, and personalized recommendations for the user's health status.

[0196] ⑶ Operation terminal related work steps (receiving AI health assessment results output by the cloud platform)

[0197] Step A3, AI health assessment result (II) output module: responsible for receiving and transmitting the AI ​​health assessment results (II) generated by the AI ​​health cloud service platform to the relevant display and interaction modules of the operation terminal, ensuring that the assessment results can be accurately delivered to the operation terminal.

[0198] In step A4, the user obtains the assessment results (II) in the following management module, or receives further health supplementary information reminders. AI health assessment result display module: displays the relevant content of AI health assessment results (II) on the operation terminal (such as Figure 23The AI ​​health digital person interacts with the user for health consultation on the main operation terminal, or further reminds the user to supplement health information (such as Figure 24 The AI ​​audio dialogue interaction module is generally used on the secondary operation terminal. The AI ​​health digital human interacts with the user through audio dialogue to provide AI health consultation or remind the user to provide additional health information.

[0199] like Figure 18 [AI Health Management - System Workflow Diagram] As shown in the above, through the detailed introduction and description of the above user acquisition of AI health assessment results (Ⅰ) and AI health assessment results (Ⅱ), the system-related workflow and steps, combined with Figure 18 From the "Process Description" in the lower left corner, we know:

[0200] First health management workflow: (1-13) sections (or possibly without step 7)

[0201] This refers to the entire process of obtaining AI health assessment results (I) when performing health management operations, including the relevant work of the AI ​​health management system (which has been previously described in detail and will not be repeated here). If the customer obtains the absolute definition of AI health assessment results (I), this step is skipped because no relevant operations are involved (the user has not uploaded medical and physical examination reports, etc.).

[0202] Second health management workflow: Step (14) + repeat (1-8) + (A1-A4)

[0203] That is, users obtain AI health assessment results (II) when performing health management operations, and the entire process of related work of the AI ​​health management system (which has been introduced in detail before and will not be repeated here).

[0204] The third health management workflow: repeat step (14) + repeat segment (1-8) + segment (B1-B4); the fourth health management workflow: repeat step (14) + repeat segment (1-8) + segment (C1-C4); and so on, in an infinite loop.

[0205] This means that the combination and progressive mechanism of AI health assessment results (I) and AI health assessment results (II) is intended to make the system's health assessment results for users more accurate and comprehensive through an infinite cycle process as the user's health supplementary information and device collection data are updated. Figure 18The specific (B1-B4) and (C1-C4) sections are not marked, but from the example of "(A1-A4) sections" and the operating mechanism of "and so on, infinite loop", ordinary people can infer from the principle that it follows a model similar to the second health management workflow.

[0206] (VII) Acquisition and Collection of AI Health Information Sources

[0207] Different from some existing health management methods that only obtain limited health monitoring data and brief personal basic information (such as gender, age, height, weight, etc.) of users through specific collection channels (such as a single collection device), and use simple data models or individual artificial intelligence to obtain rough health assessment results of users; the present invention is applicable to a variety of collection devices, channels and methods to obtain more supplementary health information of the same user, and together with the health monitoring data and brief personal basic information obtained by the above-mentioned specific collection channels, a comprehensive AI health information source for the user is constructed. The AI ​​health information source is comprehensively and comprehensively analyzed and calculated using artificial intelligence health algorithms to quickly obtain the user's AI health assessment results, and the result information can be fed back to the user through page display on the operating terminal, intelligent reminders, and AI health digital people answering user health consultations. Once the user's AI health information source changes, its AI health assessment results will also be adjusted accordingly. Through this circular mechanism, users are provided with accurate and comprehensive personalized health assessments and suggestions.

[0208] The supplementary health information includes, but is not limited to, the following user health information from the content level: basic health file information, medical and physical examination report information, emotion and ability assessment results, etc. completed by the above-mentioned users on the operating terminal devices of the system (such as smart phones, etc.); supplementary health information of the user obtained by the AI ​​health digital person deployed on the operating terminal of this system during the health consultation or diagnosis with the user; more health monitoring data of the above-mentioned users obtained by alternating or simultaneous use of more collection devices; user daily meal records, work and living environment monitoring data, social activity records and other information obtained through special collection devices or monitoring devices, algorithms or other means; and user medical history, condition and health-related information obtained from the relevant management systems of one or more third-party diagnosis or physical examination institutions through data interfaces and communication protocols.

[0209] The basic health file, that is, the health information file filled out by the above-mentioned user (or health agent) on the operating terminal according to the system questionnaire, covers but is not limited to the basic information of the above-mentioned user (such as gender, age, height, weight, etc.), work-related information (such as nature of work, work and rest schedule, whether there are night shifts and overtime, etc.), eating habits (such as food types, intake, food preferences, etc.), lifestyle information (such as exercise status, bad habits, etc.), physical condition (such as whether there are frequent colds, allergies, etc.), basic medical history (such as whether there are underlying diseases, whether surgery has been performed, etc.), female-specific information (such as menstrual period time, whether pregnant, etc.), and other information directly or indirectly related to the health of the user.

[0210] The medical and physical examination report is a report file generated by a regular hospital after a medical examination and assessment of the physical condition of the above-mentioned user, and then uploaded to the system by the user (or health agent) through the operation terminal. It covers but is not limited to blood tests, urine tests, various imaging examination reports (such as X-ray, CT, etc. reports), pathological examinations, microbial cultures, drug concentration tests and other inspection reports, as well as electrocardiogram reports, echocardiogram reports, endoscopy reports, surgical reports, diagnosis reports, treatment reports, follow-up reports and health assessment reports, medication guidelines, relevant pre-operative and post-operative notices, review notification documents and other information directly or indirectly related to the health of the user.

[0211] The emotion and ability assessment is a system that uses scientific assessment models widely recognized by the medical and health communities to obtain relevant assessment results of the user through questionnaire feedback from the above-mentioned user on the operation terminal, including but not limited to intelligence tests (such as the Wechsler Adult Intelligence Test, etc.), emotion control tests (such as the Emotion Regulation Questionnaire, etc.), cognitive ability tests (such as the Montreal Cognitive Assessment Scale, etc.), personality trait test results (such as the Big Five Personality Inventory, etc.), memory tests (such as the Wechsler Memory Scale, etc.), attention tests (such as the Attention Network Test, etc.), language ability tests (such as the Boston Naming Test, etc.) and other assessments to obtain relevant assessment results or conclusions of the user.

[0212] The daily meal records are obtained by the system through special collection equipment, algorithms or other means, including but not limited to meal time, food type, intake, dining environment and other information.

[0213] The working and living environment monitoring data is the environmental monitoring data of the above-mentioned users obtained by the system through special collection equipment, algorithms or other means, including but not limited to environmental air pressure, temperature, humidity, light intensity, ultraviolet intensity, noise intensity, air quality and other information, as well as environmental safety, comfort and other information.

[0214] In addition, the present invention further expands the scope of the health supplementary information, including but not limited to:

[0215] From the perspective of collection channels, the system expands from using only specific collection channels to collecting all collection channels and methods, without specifying dedicated or specific collection equipment, to collecting supplementary health information directly or indirectly related to the user's health;

[0216] From the perspective of collection device categories, this includes but is not limited to ordinary wearable smart devices that have not been certified as medical devices, similar wearable health data collection devices that have been certified as medical devices, personal or home medical devices and health testing equipment that have been certified as medical devices, professionally certified or non-professionally certified work and living environment monitoring data monitoring equipment, and other collection devices that can collect health supplementary information directly or indirectly related to the health of the above-mentioned users;

[0217] From the aspect of the use of collection devices, including but not limited to the use of a single designated type of collection device, the alternating use of multiple collection devices of different types, and the simultaneous use of multiple collection devices of different types to collect supplementary health information directly or indirectly related to the user's health;

[0218] From the perspective of data information collection methods, the data collection has expanded from collecting health data through collection devices to directly or indirectly obtaining the medical records, conditions and health-related information of the above-mentioned users from the relevant management systems of one or more third-party diagnosis or physical examination institutions through data interfaces and communication protocols;

[0219] In summary, this invention has achieved significant breakthroughs and innovations in the field of health management. By comprehensively expanding the scope of health information collection, diversifying collection methods and device usage, and combining advanced artificial intelligence algorithms, it builds a comprehensive and accurate user health profile, continuously optimizes health assessment results, and provides users with highly personalized and dynamic health management services. This leads health management into a new era of AI intelligence and precision, and strongly promotes the development and progress of the health industry.

[0220] (8) Multi-format output of AI health assessment results

[0221] like Figure 21 As shown in the [AI Health Management - AI Health Assessment Result Output Diagram], the AI ​​health agent plays a core role in the health management system. As the computing and processing center for health analysis, the diagram systematically and comprehensively displays its computational output results, namely the types and output methods of AI health assessment results, including but not limited to the following:

[0222] The relevant health assessment results that users can obtain during health consultation and dialogue with AI digital people (such as Figure 21 When consulting health information, users can also obtain it through shortcuts (such as Figure 25 ③), the system will automatically switch to the user's questioning method (as shown in Figure 25 In addition, the system should be able to provide health data or assessment results to third-party health or medical institutions in accordance with user needs (such as Figure 21 (as shown in ⑨).

[0223] like Figure 21 As shown in ⑥ Figure 25 Figures ③ and ⑧ illustrate the specific forms and categories of health assessment results from AI health consultations, including but not limited to, through structural and interactive diagrams. The following are common health assessment output formats.

[0224] Comprehensive health assessment report: The AI ​​health digital human analyzes the user's health information source and issues a comprehensive, systematic and individualized AI health assessment result to the user based on the user's needs. Figure 21 The comprehensive health assessment report includes but is not limited to health index, health risks, physiological indicators, sleep quality, daily routine insights, stress measurement, aging trends, health recommendations, etc.

[0225] The Health Index is a comprehensive quantitative indicator used to comprehensively assess an individual's health status within an AI health management system. It is derived through analysis and weighted calculation of multiple health information sources, including but not limited to physiological indicators, lifestyle factors, and medical history. The Health Index aims to convey a concise and intuitive overview of the user's health status, providing a quick reference for understanding their overall health status.

[0226] Health risk: Health risk refers to the likelihood that a user will develop a disease or health problem in the future. In AI health management systems, risk prediction models and algorithms are used to analyze user health information sources and assess the potential risk of different diseases. For example, cardiovascular disease risk assessments may consider factors such as age, blood pressure, blood lipids, and smoking history, and assign corresponding risk levels (low, medium, or high) to help users promptly understand potential health threats and take appropriate preventive measures.

[0227] Physiological indicators: Physiological indicators refer to objective data reflecting the human body's physiological state, measured through various physiological testing methods. In AI health management systems, common physiological indicators include basic vital signs such as heart rate, blood pressure, body temperature, and blood oxygen saturation, as well as more complex professional indicators such as electrocardiograms, electroencephalograms, and blood biochemical indicators (such as blood sugar, blood lipids, and liver function indicators). These physiological indicators can reflect the body's physiological function status in real time or on a regular basis, and are of great significance for disease diagnosis, monitoring, and health assessment.

[0228] Sleep quality: A comprehensive evaluation of a user's individual sleep status. In AI health management systems, sleep quality assessments are typically based on multiple data points, including sleep duration, sleep depth, sleep stage distribution (such as light sleep, deep sleep, and rapid eye movement), and the number of awakenings during sleep. It may also be combined with user self-reported information during AI health consultation conversations, such as whether they feel sleepy and their mental state, to comprehensively judge the quality of their sleep.

[0229] Work and rest insights: refers to an in-depth understanding and analysis of the user's individual daily work and rest patterns. By collecting and analyzing the user's work and rest related information, such as waking time, bedtime, working time, rest time, exercise time, etc., the system can understand whether the user's work and rest pattern is regular and reasonable. In the AI ​​health management system, work and rest insights can help identify possible problems in the user's work and rest, such as staying up late for a long time and irregular work and rest. These problems may have a negative impact on health, thereby providing users with targeted work and rest adjustment suggestions to maintain good health.

[0230] Stress measurement: In AI health management systems, this quantitatively assesses an individual's stress level. The AI ​​health agent uses specific algorithms and models to analyze the user's AI health information source and health stress data, converting the resulting data into a quantifiable stress value. This helps users understand their stress status and take appropriate mitigation measures.

[0231] Aging Trends: This is targeted health guidance provided to users within the AI ​​health management system based on their health assessment results, health information sources, and individual characteristics. It covers multiple aspects, including lifestyle adjustments, disease prevention, and treatment recommendations. For example, a user with high blood pressure might be advised to control salt intake, maintain moderate exercise, and regularly measure their blood pressure. For those experiencing poor sleep quality, they might be advised to improve their sleeping environment and establish a regular sleep schedule. Health recommendations are designed to help users improve their health and prevent the occurrence and development of disease.

[0232] like Figure 21As shown in "AI Health Digital Human Free Question and Answer" in Section 7, users can use natural language (text or voice) to consult the AI ​​Health Digital Human on issues related to personal health (as mentioned above, not limited to health index, health risks, physiological indicators, sleep quality, etc.). Users can also make secondary consultations on the AI ​​Health Digital Human's answers, asking for further explanations, or explain them in a simple and understandable way (if the user's health supplementary information is sufficient, the AI ​​Health Digital Human will automatically use appropriate expressions and language to communicate with the user). Users can also ask the AI ​​Health Digital Human about the statistical data and information of the user's health monitoring data for a certain period of time, or the comparison, changes, trends, etc. of data from a certain period of time. During the health consultation process, the system can support multiple rounds of dialogue and mutual questions and answers. Users can correct and supplement their questions just like normal conversations with ordinary doctors. The AI ​​Health Digital Human will automatically judge and organize and give users relevant answers.

[0233] like Figure 21 As shown in "AI Health Digital Human Free Q&A" in the figure, users can also ask the AI ​​Health Digital Human health questions about health knowledge. Based on the health expertise learned by the AI ​​big model and the health expertise based on the professional training of the AI ​​health agent, as well as the relevant health information retrieved, the AI ​​Health Digital Human health human ...

[0234] In short, the AI ​​health management system, with its advanced intelligent technology and comprehensive assessment system, provides users with precise, efficient, and personalized health protection. It not only visualizes health data and intelligently diagnoses issues, but also enables customized health recommendations, significantly enhancing users' control and management of their health. In the future, with the continuous iteration and improvement of technology, it is expected to become an indispensable health guardian and personal family doctor for everyone's healthy life.

[0235] (IX) Artificial Intelligence Functions and Characteristics of AI Health Management System

[0236] 1. Intelligent consultation function of AI health digital human

[0237] The AI ​​health digital human deployed by the AI ​​health management system has an intelligent consultation function, which can ask users behavioral issues or subjective self-perceptions related to their health.

[0238] The questions the AI ​​Health Digital Human asks users aren't questionnaires with a fixed format or content. When a user asks a health question, if it finds information that's missing from the user's AI Health Information Source and should be supplemented, the AI ​​Health Digital Human will ask questions like, "How are you feeling mentally lately?" "Have you been constipated recently?" "Are you pregnant, and how far along are you?"

[0239] The AI ​​health digital person's intelligent medical consultation has a scientific strategy mechanism. Based on the user's comprehensive AI health information source, it uses a fusion analysis algorithm based on the artificial intelligence deep learning framework to extract features through a neural network model and conduct correlation analysis to determine the consultation strategy. Specifically, it focuses on the user's health consultation questions and proceeds in order from important factors to secondary factors, and from direct factors to indirect factors related to the user's health problems. At the same time, the user's health profile is updated according to the user's answers, and the consultation process and language are adjusted in real time to achieve personalized consultation, ensure the smooth acquisition of key health supplementary information, and provide users with more accurate health assessments and suggestions.

[0240] Once the user answers the medical questions, the AI ​​health digital human will immediately clean, filter, organize, and standardize the answers, extract key information, and promptly add it to the user's AI health information source. As the two sides continue to answer questions, the AI ​​health information source is updated in an infinite loop.

[0241] The AI ​​Health Digital Human's intelligent consultation features a self-learning optimization mechanism. Based on multi-source data such as consultation records, health assessment feedback, and changes in health status, it uses deep learning algorithms such as recurrent neural networks or long-short-term memory networks to learn user response patterns, optimize consultation strategies using reinforcement learning, and discover new risk patterns and group characteristics through unsupervised learning, thereby timely updating the knowledge base and strategies.

[0242] 2. AI health digital person (or system) health supplement information intelligent reminder function

[0243] The AI ​​health management system features intelligent reminders for supplemental health information. This helps ensure that the user's AI health information source remains relatively complete and accurate, providing the system with more comprehensive basic data, enabling the system to provide users with more accurate health assessments and more targeted health recommendations.

[0244] The system summarizes the user's health consultation conversations and interactions to determine the user's most pressing health issues or analyze possible health issues. If it finds that these health issues are closely related to missing content in the user's health information source, the system will activate intelligent reminder mechanisms and programs.

[0245] The reminder content of this reminder function mainly focuses on the missing health information in the health supplementary information management system of the operating terminal, including but not limited to the user's unfinished filling of relevant important information in the system's basic health file management module, the user's unfinished relevant important assessments in the system's emotion and ability assessment module, and the user's unuploaded relevant important medical or physical examination reports in the system's medical and physical examination report management module.

[0246] The implementation methods of this intelligent reminder function include but are not limited to informing the user through the AI ​​health digital human in its dialogue window, popping up a reminder window or reminder label on the relevant display interface of the operation terminal, sending text messages or push notifications to the user's registered contact information, etc. The reminder content clearly points to the health information categories and supplementary entries that the user needs to supplement, such as reminding the user to fill in relevant important information items in the system's basic health file management module. At the same time, the reminder content may mention that the health information items that need to be supplemented are related to a health problem that the user is concerned about or a health problem that may occur in the user's body.

[0247] This intelligent reminder function is dynamic and timely. As the system continuously analyzes the user's health information sources and the user's health status changes, the system can promptly trigger reminders if new health information supplementation needs arise.

[0248] The smart reminder function can be optimized based on user habits and behavior patterns. If the system finds that users are more likely to respond to reminders and provide additional health information during specific time periods or in specific scenarios, the system will prioritize sending reminders during those times or scenarios to increase the efficiency and motivation of users to provide additional health information.

[0249] 3. AI health digital person (or system) intelligent reminder function for user health matters

[0250] The AI ​​health management system has an intelligent reminder function for health matters. When the system learns from the user's health information source that the user has a medical appointment event, a review event, or a medication event, the system will activate the intelligent reminder mechanism for health matters, reminding the user to pay attention to the relevant event content and recommending appropriate measures. Alternatively, when the system finds that the user may have corresponding health risks through a comprehensive analysis of the user's health information source, the system will also activate the reminder mechanism. For example, when it is found that abnormal changes in certain physiological indicators bring potential serious risks, it is recommended that the user go to a relevant professional institution for a physical examination or a medical institution for examination; when it is detected that the user should pay attention to relevant work and rest, the user is reminded to adjust his or her work and rest, etc.

[0251] One way the system reminds users of health issues is through its AI health digital human. This AI health digital human will proactively remind users of health issues within its dialogue window, not just when users seek health advice. The system can also provide reminders via pop-up reminder windows and reminder labels on relevant display interfaces on the user terminal.

[0252] The system's intelligent reminder function is dynamic and timely. As the system continuously monitors and analyzes the user's health information and the user's health status continues to change, once a new health issue arises that requires reminding the user, the system can respond quickly and initiate reminders in a timely manner to ensure that the user can understand their health status and the actions they need to take at the first time.

[0253] 4. Automatic adjustment of AI health digital person's communication style

[0254] The AI ​​health digital human deployed in the AI ​​health management system can automatically adjust its communication style, whether professional or popular. Both professional and popular methods are designed to provide different users with a better and more suitable health management interactive experience.

[0255] For users with a professional background, the AI ​​Health Digital Human will adopt a relatively professional communication method, using professional terminology and in-depth medical knowledge to communicate more efficiently, so as to discuss health issues and provide professional advice. The user's background information can be obtained from the user's basic health file. If the user shows that the user has a medical-related education background or works in the medical industry, the system will mark this user as having a professional background. At the same time, if the user frequently uses professional terminology or demonstrates a high degree of familiarity with professional medical knowledge, the AI ​​Health Digital Human will also determine that the user has a certain professional background.

[0256] For ordinary users without professional backgrounds, the AI ​​Health Digital Human switches to a popular communication method, using easy-to-understand language and avoiding complex professional terms to ensure that users can easily understand health information and advice.

[0257] 5. AI health digital human (or system) reminds you of abnormal equipment conditions

[0258] The AI ​​health digital human deployed in the AI ​​health management system can alert users to any abnormalities in the use of their data collection devices or operating terminals and provide guidance and suggestions. Through the AI ​​health digital human's timely reminders and effective guidance on abnormal usage of user data collection devices and auxiliary operating terminals, users can promptly identify and resolve device issues, ensuring the stable operation of the health management system and the accurate collection and transmission of health data, thereby improving the user's health management effectiveness and experience.

[0259] When the user's health monitoring data or information is missing or abnormal for a long period of time, or when it detects that the data transmission between the user's collection device and the operation terminal is interrupted, delayed or lost, the AI ​​health digital human will remind the user of the relevant phenomenon and suggest the user to check whether the connection between the collection device and the terminal is stable, or ask the user whether the collection device is used normally, or check whether the relevant equipment has sufficient power, or provide detailed troubleshooting steps based on possible reasons.

[0260] When health monitoring data collected by a device's sensors (such as an optical heart rate sensor) shows abnormal fluctuations or significantly deviates from the user's normal range, the AI ​​Health Digital Human will promptly issue a warning and provide targeted troubleshooting suggestions based on the characteristics of different sensor types and common problems. If the user confirms that the data anomaly is not a device issue, it will be converted to a user health issue, prompting the user to pay attention or recommending that the user consult a professional doctor or seek medical attention promptly.

[0261] 6. AI health agents have optimization mechanisms and adaptive learning capabilities

[0262] The AI ​​health management system and its deployed AI health agent possess optimization mechanisms and adaptive learning capabilities. Based on a large AI model, the AI ​​health agent is capable of acquiring and learning health knowledge. By connecting to professional medical databases, it continuously scans and collects the latest and most comprehensive medical and health knowledge from across China and around the world, including cutting-edge health research findings, the latest disease treatments, and advanced health management concepts. This mechanism enables the AI ​​health agent to continuously update and refine its knowledge system, providing users with the most cutting-edge and accurate health advice and services.

[0263] This health management system can debug and optimize the AI ​​health agent system based on the continuously accumulated user health data and user feedback information. Developers can use the system's AI health agent development module to perform system debugging and optimization of the AI ​​health agent.

[0264] The learning content of the AI ​​health agent covers multiple aspects. From the perspective of health data, the system will learn the relationship between different health indicators; from the perspective of user behavior patterns, the system will learn the user's acceptance and implementation of health management suggestions; from the perspective of user feedback information, the system will adjust its own evaluation methods and management strategies based on the user's satisfaction with the health assessment results and improvement suggestions. At the same time, the AI ​​health agent itself has the ability to self-adjust and can automatically adjust its own algorithms and model parameters to better adapt to the health status and demand changes of different users, thereby significantly improving the accuracy of health assessment and management. (10) System customer service work of AI health digital people

[0265] The AI ​​health digital human deployed in the AI ​​health management system can serve as the system's customer service representative. When users have questions about the use of the system and related equipment, the AI ​​health digital human can promptly provide accurate instructions and professional guidance. Whether it's the functional operation process of the operating terminal, the correct way to wear the data collection device, or daily maintenance points, the AI ​​health digital human can provide clear and easy-to-understand answers. In this way, the AI ​​health digital human helps users better understand how to use the system and equipment, improving the user experience and convenience during the health management process.

[0266] 1. AI health digital human-style AI customer service

[0267] Users can ask questions directly to the AI ​​health digital human in the dialogue window, which is similar to asking health questions. However, this time the AI ​​health digital human does not answer the user's health questions, but the operational questions or related equipment usage issues encountered by the user when using this system.

[0268] For example, a user recently purchased a body fat scale and wanted to know what to pay attention to when using it. He asked the AI ​​Health Digital Human: "I just bought a body fat scale. Is there anything special I should pay attention to when using it?" The AI ​​Health Digital Human replied: "When using a body fat scale, please make sure to place it on a hard, flat surface. Avoid using it on carpets or uneven surfaces to avoid affecting measurement accuracy. Before measuring, please take off your shoes and socks, stand steadily on the scale with both feet, and try to keep your body balanced and not shake. In addition, for more accurate measurement results, it is recommended to measure at the same time every day and on an empty stomach." After understanding these precautions, the user used the body fat scale correctly and obtained more reliable body fat data.

[0269] 2. AI customer service in the form of chat tools and friends

[0270] On the customer service contact interface of the operating terminal, the AI ​​customer service page will display a QR code (using WeChat as an example) for adding friends to the AI ​​customer service instant messaging tool. The AI ​​customer service WeChat account added by the user is actually the WeChat account of the AI ​​health digital person. However, please note that the unauthorized impersonation of a WeChat account risks violating WeChat platform regulations and laws and regulations. In actual use, it is recommended to actively communicate with WeChat officials to explore legal and compliant cooperation channels and technical solutions to ensure the sustainable development of the business and protect user rights.

[0271] To enable AI digital humans to have WeChat accounts, automatically approve friend requests, and provide customer service, the following technical steps must be taken (for reference only; actual operations must ensure legality and compliance):

[0272] WeChat account simulation registration and login: Utilizing automated scripts and simulation software to build an account system similar to a WeChat account, the process must ensure compliance with relevant platform usage rules and laws and regulations, and must not violate privacy policies and terms of service. This involves simulating the real user registration process, such as generating a virtual mobile phone number (if permitted by the platform) or using a legally obtained test number, and filling in the necessary registration information to complete the account creation and login process.

[0273] Automatic friend request processing: Develop a program to monitor WeChat friend request notifications. When a user sends a friend request, automatically accept the request using the WeChat API (if a legally available interface is available) or by simulating a click. This requires a deep understanding of the WeChat client's operating mechanisms and communication protocols, accurately identifying and responding to friend request events, while also ensuring security and stability, and preventing misuse or abnormalities detected by the WeChat platform.

[0274] Customer Service Integration: Connecting the AI ​​customer service module with WeChat accounts enables the AI ​​digital human to receive user messages in the WeChat chat window and intelligently respond based on a pre-set knowledge base and conversational logic. This requires the development of a message processing system capable of converting WeChat messages into a format understandable to the AI ​​digital human and converting responses into WeChat messages for the user. Simultaneously, based on the positioning and training of the AI ​​health digital human customer service role, the AI ​​customer service conversational capabilities will be continuously optimized, encompassing natural language understanding, semantic analysis, question classification, and answer generation, thereby providing a high-quality customer service experience.

[0275] Security and Compliance: Security and compliance are crucial throughout the entire process. We must ensure the confidentiality and security of user data and prevent any disclosure of private information. We must also strictly adhere to WeChat platform usage guidelines and relevant laws and regulations to prevent account bans or legal risks due to violations. We regularly conduct system security audits and vulnerability detection, promptly remediate potential security risks, and ensure stable and reliable service operation.

[0276] 3. AI customer service management mechanism

[0277] ⑴Basic learning and summary of common problems

[0278] AI customer service first conducts an in-depth study of detailed product or service manuals, covering core information such as the system's functional architecture, operational procedures, technical specifications, and various business rules. Using natural language processing technology and text analysis algorithms, AI extracts, categorizes, and structures key knowledge points from the manuals, building a preliminary knowledge base.

[0279] At the same time, we thoroughly analyze historical customer inquiry data, using data mining techniques to identify frequently occurring problem types and patterns. For example, in a health management system, common issues may include device connection failures, confusion over data interpretation, and difficulty with function operation steps. For these issues, AI customer service generates standard answer templates and associates them with the corresponding question keywords, enabling quick and accurate responses in subsequent conversations.

[0280] ⑵Problem reporting and manual guidance

[0281] When AI customer service encounters a question that cannot be answered in a real-time conversation with a user, it will first politely apologize to the user, admit that the problem mentioned by the user is temporarily beyond the scope of its ability to solve, inform the user that it will feedback to the development team and seek a solution, and hope that the user will come back to consult related issues after a while.

[0282] AI customer service will use built-in problem identification and classification models to extract and describe the problem in detail. These problem descriptions will be organized into structured data reports and submitted to the system's management backend.

[0283] After receiving a report, the administrator conducts an in-depth analysis of the issue, drawing on their expertise and experience. For complex technical issues or those involving ambiguous business rules, the administrator directly intervenes in the conversation, providing accurate answers through manual guidance. This ensures that the user's issue is resolved promptly, while also providing a learning model for AI customer service.

[0284] ⑶ Autonomous decision-making support in the mature stage

[0285] As AI customer service accumulates conversational experience and knowledge, and gradually matures, it can conduct deeper analysis and understanding of new issues. When encountering unresolved issues, AI will use its learned knowledge reasoning and language generation capabilities to generate multiple possible reference responses.

[0286] These responses are generated based on past solutions to similar issues, knowledge in related fields, and semantic understanding models. For example, for a compatibility issue regarding a new health monitoring device, AI might generate different perspectives based on the device's technical specifications, common compatibility solutions, and the specific circumstances provided by the user. These suggestions might include checking for software updates or trying specific connection settings, allowing administrators to select the optimal solution based on their specific circumstances, further improving the AI ​​customer service's ability and efficiency in handling complex issues.

[0287] ⑷ Feedback on difficult issues and R&D collaboration

[0288] For questions that the AI ​​customer service team cannot answer after multiple attempts, the system will record the problem in detail and provide feedback to the R&D team. The R&D team will analyze and research the problem from multiple perspectives, including technical implementation and product design. This may involve checking system code, optimizing database queries, and expanding and improving the knowledge graph.

[0289] After finding a solution, the R&D team will feed the answer back to the AI ​​customer service system and update the knowledge base and related model parameters at the same time, so that the AI ​​customer service can learn new knowledge and response strategies, so that when encountering similar problems in the future, it can independently provide users with accurate responses, continuously improve the intelligence level and service quality of the entire customer service system, and realize a closed-loop optimization process from problem discovery to solution, providing users with a better quality and more efficient customer service experience.

[0290] 4. Features and effects of AI health digital human customer service

[0291] In traditional health management services, when users encounter problems and contact customer service, they often have to wait in line on the human customer service hotline, wasting a lot of time. However, AI health digital human customer service offers instant response and 24 / 7 service guarantee, completely eliminating this situation. Whether during busy weekdays, holidays, or late at night, users can ask questions to the AI ​​health digital human at any time.

[0292] Traditional manual customer service often requires constant questioning and troubleshooting to identify the problem. However, AI health digital humans, as customer service personnel, are themselves part of the system and can quickly detect abnormal issues through system monitoring, thereby improving customer service efficiency.

[0293] AI health digital humans perform customer service tasks and can handle multiple user issues in parallel, improving overall service efficiency and reducing customer service costs. In real-world applications, especially when health management services are provided to a large number of users, traditional manual customer service often faces problems with long queues and inefficient service. Each user's issue must be handled individually by a human customer service representative, and the processing time for each issue is long, causing other users to wait longer for a response. However, AI health digital humans can handle consultation requests from multiple users simultaneously, without compromising service quality as the number of users increases.

[0294] As the customer service staff of the AI ​​health management system, the AI ​​health digital human plays an important role in helping users solve problems in using the system and equipment, effectively improving the user experience and promoting users' active participation in health management. It is an important part of the system and provides strong support for the widespread application of the system and the improvement of user health management effects.

[0295] (11) Group Cluster User Management and Application

[0296] A family or group using an AI health management system and devices is called a cluster, and all users in the cluster are members of the cluster. This user model is called cluster user mode, which is divided into group cluster mode and family cluster mode. The two cluster modes are compatible, and a user can use both group cluster mode and family cluster mode simultaneously.

[0297] 1. Management mechanism of group clusters

[0298] A cluster is the original unit of a group-based cluster structure created by individual users. A cluster administrator's health management targets its natural cluster members, and it can also accept more individual users to join and become members of its cluster. Cluster members are directly under the administrator and are managed by the administrator. A cluster administrator may also merge other clusters and their subclusters. Direct members of other clusters will be directly under the administrator, and subclusters of other clusters will also be transferred and become subclusters of the current cluster (their structure remains unchanged). Cluster members have the right to create one-level subclusters downward and manage subcluster members. Subcluster members are not direct members of the cluster administrator. The cluster administrator can only manage the health of their direct cluster members, and subcluster members can only be managed by the subcluster administrator.

[0299] A cluster member can create and manage first-level subclusters. Subclusters are hierarchical within the cluster, with clusters further down the hierarchy, including first-level subclusters, second-level subclusters, and so on. Subcluster members are only direct members of the subcluster, and health management rights can only be exercised by the subcluster administrator.

[0300] A federated cluster is an associative framework. A federated cluster has no directly subordinate cluster members; clusters and subclusters have their own directly subordinate cluster members. A cluster administrator can apply to "federate" with another cluster to form a first-level federated cluster. The cluster administrator who agrees to the federation becomes the first-level federated cluster administrator. They can then apply to join with other first-level federated clusters to form second-level federated clusters, and so on. The highest-level federated cluster is relatively called the "top-level federated cluster."

[0301] When an individual user creates a new group cluster, they automatically become the cluster administrator of that cluster. The cluster administrator has the right to manage the health of other secondary operation terminal users, who are natural members of the cluster. The cluster administrator has the right to monitor the health of direct members of the cluster and members of subordinate sub-clusters (without the consent of cluster members).

[0302] A cluster member can create first-level subclusters and manage subcluster members. This cluster member is the administrator of the first-level subcluster they created. Subclusters are hierarchical within the cluster, with clusters further divided into first-level subclusters, second-level subclusters, and so on. Subcluster administrators are a general term for administrators at all levels of subclusters. Subcluster members are only direct members of that subcluster, and only the subcluster administrator can exercise health management rights.

[0303] A cluster administrator can apply to "federate" with another cluster to form a first-level federated cluster. The cluster administrator who agrees to the federation becomes a first-level federated cluster administrator. They can then apply to federate with another first-level federated cluster to form a second-level federated cluster, and so on. The highest-level federated cluster is relatively called the "top-level federated cluster." The term "federated cluster administrator" is a general term for administrators of all levels of federated clusters.

[0304] All users within a cluster or subcluster are members of that cluster or subcluster, known as cluster members. Cluster administrators are also considered cluster members. Cluster members can view all cluster members of the entire cluster or top-level federated cluster and submit health monitoring requests to them. Cluster members are divided into direct members (direct members of the cluster) and indirect members (members of subclusters at all levels below). Administrators can only perform health monitoring for direct members. Cluster member health monitoring is naturally communicated to subcluster administrators at all levels (cluster administrators).

[0305] like Figure 34 As shown in the figure, a cluster is a basic unit system. It has direct cluster members, including cluster administrators, first-level sub-cluster administrators, and cluster members. First-level sub-clusters are branches of the cluster, and first-level clusters also have their own direct cluster members, including first-level sub-cluster administrators, second-level sub-cluster administrators, and first-level sub-cluster members. As can be seen, first-level sub-cluster administrators are both direct members of the cluster and direct members of the second-level sub-clusters they create. This mechanism is similar for lower-level branches.

[0306] like Figure 34 As shown in the figure, the joint clusters are in a tree-like form. We call this system a "joint cluster tree system". A cluster is joined with other clusters to form a first-level joint cluster. The administrator of the agreed cluster becomes the administrator of the first-level joint cluster, and so on for the second-level joint clusters until a top-level joint cluster is reached. Figure 34 As shown by line ⑵, before being combined with the secondary connection cluster (B), the secondary connection cluster (A) is the top-level combined cluster of this system; if the secondary connection cluster (A) is combined with the secondary connection cluster (B), the upper-level combined cluster formed becomes the top-level combined cluster of this system.

[0307] like Figure 34As shown in the figure, the joint cluster is just a framework. The joint cluster has no direct cluster members. Clusters and subclusters have their own direct cluster members. The cluster is a basic unit system and a basic unit of the joint cluster. Figure 32 As shown by line ⑴.

[0308] 2. Principles to follow for group clusters

[0309] Federation principle: All federated clusters, clusters, and subclusters within a top-level federated cluster cannot be federated with each other a second time; all federated clusters and clusters within a top-level federated cluster can be federated with other top-level federated clusters (or clusters), and after the federation, the other party becomes part of the top-level federated cluster (its structure remains unchanged).

[0310] Merger Principles: All clusters (subclusters) at the same level within a top-level federated cluster can be merged. The administrators and cluster members of the merged cluster (subcluster) become members of the newly merged cluster. All clusters (subclusters) at different levels within a top-level federated cluster can be merged. The administrators and cluster members of lower-level subclusters become members of the upper-level cluster (or subcluster). All clusters (subclusters) at the same level within a tree-like structure can merge with external independent clusters (i.e., without federation). After the merger, the administrators and cluster members of the external cluster become members of the newly merged cluster (subcluster), and the subclusters of the external independent cluster are also transferred in (with their organizational structure unchanged).

[0311] Sharing principle: All lower-level cluster members within the top-level joint cluster can view and apply for health attention to any cluster member within the system within the top-level joint cluster (unlike individual users, who need to separately request the other party's identity ID number or scan the ID QR code to query or follow).

[0312] It should be noted that the administrator is only an identity with management responsibilities. The administrator is actually an individual user. The administrator is only an identity with management authority. An individual user can "hold multiple positions", that is, he can serve in any position without conflict.

[0313] As an individual user, a cluster administrator can participate in health-related activities with other users, join other clusters or sub-clusters, and create more clusters or sub-clusters. As a cluster administrator, you exercise relevant cluster management rights, including but not limited to approving others to join the cluster, inviting others to join the cluster, health management, cluster federation, cluster merger, transfer of cluster administrator rights, and disbanding the cluster.

[0314] like Figure 35 As shown in the continuous ①② in Figure 1, a cluster administrator ⑴ and an individual user outside the cluster perform mutual health care interaction operations. Figure 35The diagram ③ in the figure is a diagram of the individual user applying to the cluster administrator ⑴ to join the cluster and getting approval. Figure 35 The diagram ④ in the figure is a schematic diagram of the cluster administrator ⑴ sending an invitation to the individual user to join the group. Figure 35 As shown in Figure 5, another cluster administrator (2) applies to cluster administrator (1) to join the cluster under its jurisdiction and is approved. Figure 35 The diagram ⑥ in the figure is a diagram of a cluster administrator ⑴ sending an invitation to another cluster administrator ⑵ to join a group. Figure 35 As shown in Figures ⑦ and ⑧, this is a diagram of the interactive operation between cluster administrators ⑴ and ⑵ for cluster union. 3. Health hosting related permission operations and processes

[0315] Device binding and permission activation: Figure 36 As shown by lines ② and ③ in the middle, for health hosting, the administrator binds the device to the designated managed object on the main operation terminal. After obtaining the relevant information, the AI ​​Health Cloud Service Platform verifies the identity of the cluster administrator. Once verified, the system creates a profile for the managed object and automatically generates a unique user account identification code for the managed object. This unique user account identification code is then associated with the cluster administrator's unique user account identification code, granting the cluster administrator management rights over the managed object.

[0316] Custodial operations and the rights of the custodial subjects: After this, the administrator can perform health custodial actions on the custodial subjects as a health custodian during the management process. At the same time, the custodial subjects themselves also retain the right to conduct normal health management, such as seeking health consultations from the AI ​​health digital human.

[0317] 4. Permission interaction process between cluster members and users

[0318] Permission agreement setting and communication: Figure 36 As shown in steps 4 and 6, cluster members or users within and outside the cluster (such as A, B, and C) interact with each other through their own permission settings and operations, or through permission "agreements" with other users. These operations are transmitted to the AI ​​Health Cloud Service Platform in the form of application instructions or confirmation instructions.

[0319] Platform Verification and Adjustment Execution: Upon receiving the relevant instructions, the AI ​​Health Cloud Service Platform verifies the identity of a single user or multiple users. Once verified, the system reviews the permissions "agreement" reached between users, re-registers them, and performs the corresponding adjustments, as shown in steps g and h of the platform process.

[0320] Information update and system execution: After the above process, the system will save the latest user profile information and synchronize the update with the user management module of the main operation terminal and the auxiliary operation terminal through communication protocols and instructions, such as Figure 36 After the update is completed, the AI ​​health management system and each operation terminal will perform management operations based on the new user rights "agreement".

[0321] (12) Family Cluster User Management and Application

[0322] A family or group using an AI health management system and devices is called a cluster, and all users in the cluster are members of the cluster. This user model is called cluster user mode, which is divided into group cluster mode and family cluster mode. The two cluster modes are compatible, and a user can use both group cluster mode and family cluster mode simultaneously.

[0323] 1. Management mechanism of family-style clusters

[0324] A family is a social group composed of people related by blood (including direct and collateral relatives) or marriage. Family members typically share a surname or a common origin, have a common ancestor, and carry on the family's culture, values, and traditions through family traditions. The family can extend from the nuclear family (parents and children) to the extended family encompassing grandparents, uncles, aunts, cousins, and other relatives. It is a form of social organization based on kinship ties and plays a crucial role in social structure and cultural inheritance.

[0325] A family tree, also known as a genealogy or clan genealogy, is a special document that records the family's lineage and the deeds of its key figures in written form. It details the family's origins, migrations, branches, names, pseudonyms, dates of birth and death, marital status, information about children, as well as family rules and precepts, assets, and ancestral halls. A family tree is a crucial vehicle for family history and culture. It allows us to trace the family's development, understand the blood relationships and succession order among family members, and maintain family cohesion and identity. It also provides rich material for research in history, demography, sociology, and folklore.

[0326] The AI ​​health management system constructs and applies a cluster model, cleverly utilizing the family's group nature, structural characteristics, various scenarios of family health management, and the characteristics of family marriages being similar to cluster alliances, to innovatively design a family cluster health management model.

[0327] In the family cluster model, the system's operations and interactions are designed to reflect the behaviors and communication styles of the original family. Family members can easily create family clusters, invite others to join, and remove others from the family, all of which are similar to the organization and management within the family. Furthermore, the sharing and exchange of health information among family members also follows the family's communication patterns, making health management operations more natural and smooth.

[0328] The family cluster model is a health management model based on family members. A family is similar to a cluster, a household is a subcluster, and a union of households is similar to a joint cluster, with slight differences. Within a family cluster, there are two roles: the family tree creator (default administrator) and family members. The family tree creator is limited to family tree management. Any family member (or any other individual user) can monitor each other's health; within a family, certain rules define the household, and each family member automatically has the right to manage the health of all other family members.

[0329] 2. Definition of relevant roles of family clusters

[0330] Genealogy creator: Any family member who creates a genealogy for the first time is the creator of the genealogy and is responsible for maintaining and modifying the genealogy. He or she can also assign permissions to other family members to share management responsibilities. Figure 37 "A Man 2" shown in ⑤ in [Family Cluster Structure - User Role] has the administrator label, that is, he is the creator of the family tree and is also the administrator of the tree by default.

[0331] Family lineage person: Family lineage person determines his position in the family based on blood relationship. Each family lineage person carries the family gene and is a link in the family reproduction chain. (such as father and son in a patrilineal family, mother and son in a matrilineal family). Figure 37 The "G male 1", "A male 1", "B male 1", etc. shown in the figure are the family lineage members.

[0332] Family members: All members of a family are considered family members, including all family members within the family line. A family member is also a member of the paternal family line, a member of the maternal family line, and, if married, a member of the spouse's family line.

[0333] Family: In the AI ​​health management system, "family" refers to a relative unit in a family or genealogy, consisting of parents and children (and their spouses). According to the actual application of the AI ​​health management system, it is mainly for the health management of family members with relatively close blood relations, so it has certain differences from the traditional genealogy. We call the family lineage at the highest level of the genealogy built in the AI ​​health management system "branch ancestor", such as Figure 37The family shown in ① is the "family branch ancestor family", which is also the first-level family in the family. For example Figure 37 As shown, in the AI health management system, the family cluster presents a tree-like branch structure. Below the "family branch ancestor family", according to the genealogy, it can be called the first-level sub-family, the second-level sub-family, and so on. For example Figure 37 In ② is the first-level sub-family, ③ and ⑨ are the second-level sub-families, ③ is the third-level sub-family, and ④ is the fourth-level sub-family.

[0334] Family members: The family members of a family are the genealogical people of the family, their spouses, children and their spouses. For example Figure 37 In ⑤, the father, ⑥ the mother, ⑦ the son, and ⑧ the daughter-in-law form the "⑨ - A male 2" family.

[0335] 3. Other explanations of the family-style cluster

[0336] It is worth emphasizing that, as Figure 37 shown, in the family cluster mode, the family, as a concept of member composition, has non-complete fixity, and it presents the characteristics of relativity and overlap in actual situations. Specifically, the four dotted-line boxes ①, ②, ③, and ④ in the figure respectively correspond to four families, and there is an overlapping phenomenon among these four dotted-line boxes, which is a normal situation. Taking the genealogical person ⑤ "A male 2" as an example, he and his spouse, son, and daughter-in-law together form a family; at the same time, as the son of "G male 3", he also forms a family with his parents and brothers; in addition, he also forms a family with his wife and his wife's parents. In the family-style cluster mode of the AI health management system, ⑤ "A male 2" has corresponding rights related to the health concern and health trusteeship of family members in these three families.

[0337] In the family cluster mode of the AI health management system, it strictly follows the family structure principle based on the blood relationship of father-son or mother-son relationship. In terms of the identification of family members and the management of relevant permissions, it breaks through the limitation of traditional families that only focus on the male genealogy, and implements the equal concept of treating male and female members equally. Specifically, regardless of male or female members, even after a woman gets married, in the family-style cluster mode of this system, her family relationship still continues in the original family genealogy, which is different from some concepts of traditional genealogies.

[0338] Generally, when a user is unmarried, according to the principle of family blood relationship attribution, he belongs to both the paternal family and the maternal family at the same time. After the user gets married, the spouse's family will be added. For Figure 38Take a "user" in [Family Cluster Structure - Marriage Relationship among Family Members] as an example, where line ① represents the marriage relationship between the user's parents, line ② shows the position of the user's father in the paternal family and maternal family; line ④ reflects the marriage relationship between the user and his spouse, and lines ③⑤ show the position of the user in the paternal family, maternal family, and spouse family. In addition, Figure 39 As shown, based on the genealogical structure, taking the user as an example and with the user as the central perspective, the standard titles of the members of the three families are referenced to reflect the characteristic mechanism of family-style cluster management.

[0339] If the user also has legally related adoptive parents, this situation is also suitable for the family cluster model of the AI ​​health management system. In this case, the user can add adoptive father-family and adoptive mother-family, and their management mechanism is consistent with the biological parents' family.

[0340] Any family member who first creates a family tree (and is responsible for maintaining and modifying it) is the creator and administrator of the family tree. This can also be managed by assigning permissions to family members. Figure 40 As shown in ⑦, family member ⑴, as the creator of the family tree, assigns family tree management permissions to family member ⑵, which is generally divided into full family management or partial branch management. When creating a family tree, you must indicate the birthdays and genders of family members (the system automatically generates family relationships and mutual titles, such as Figure 39 shown).

[0341] It should be emphasized that after the family tree is created, the AI ​​health management system will automatically generate a [user account unique identification code] for each family member, and will automatically mark the position of each family member in the family and family tree, and clarify their relative relationship with other family members or family members.

[0342] Under the family cluster management mode, the AI ​​health management system automatically grants family members the right to follow each other's health by default. The health follower or followee can also choose to cancel the follow-up based on the actual situation and needs. To follow other family members, the consent of the family member or the custodian must be obtained. Figure 40 As shown in ③④.

[0343] Under the family cluster management model, the AI ​​health management system assumes that a family member has the right to take care of the health of another family member in the same family; if a child takes care of the health of his or her parents, he or she can terminate the care or transfer the care to a sibling; to take care of other family members, the care must be authorized by the family member who has the right to take care of him or her.

[0344] like Figure 41As shown in [Family Cluster User Rights Management Mechanism], under the family cluster user mode, the "agreement" reached between the genealogy creator, genealogy administrator, family members, and family members through relevant management rights operations will be re-registered and executed through the AI ​​Health Cloud Service Platform system to ensure the accuracy and effectiveness of the permission settings.

[0345] 4. Health custody-related authority operations and processes

[0346] Device binding and permission activation: Figure 41 As shown by lines ②③ in the middle, in health hosting, family member A binds a device to another family member, C, on the main operation terminal. After obtaining the relevant information, the AI ​​Health Cloud Service Platform verifies the identities of family members A and C. Once verified, the system creates health hosting profiles for family members A and C and grants the cluster administrator hosting permissions for the managed objects.

[0347] Custody operations and the rights of the custodian: After this, family member A can perform health custodian actions for the custodian family member C as the custodian during the management process. At the same time, the custodian family member C also retains the right to conduct normal health management, such as seeking health consultation from the AI ​​health digital human.

[0348] 5. Permission interaction process between family members and users

[0349] Permission agreement setting and communication: Figure 41 As shown in steps 4 and 6, family members or users within and outside the cluster (e.g., A, B, and C) interact with each other through their own permission settings and operations, or through permission "agreements" with other users. These operations are transmitted to the AI ​​Health Cloud Service Platform in the form of application instructions or confirmation instructions.

[0350] Platform Verification and Adjustment Execution: Upon receiving the relevant instructions, the AI ​​Health Cloud Service Platform verifies the identity of a single user or multiple users. Once verified, the system reviews the permissions "agreement" reached between users, re-registers them, and performs the corresponding adjustments, as shown in steps g and h of the platform process.

[0351] Information update and system execution: After the above process, the system will save the latest user profile information and synchronize the update with the user management module of the main operation terminal and the auxiliary operation terminal through communication protocols and instructions, such as Figure 40 After the update is completed, the AI ​​health management system and each operation terminal will perform management operations based on the new user rights "agreement".

[0352] In addition to the above features, family clusters allow all family members of a user's own family to enjoy the system-default right to pay attention to each other's health and the right to take care of their health (the relevant rights mechanism is the same as the group cluster model), unless the person being followed cancels the corresponding person being followed; a user can directly view all family members in his or her family tree (except members of his or her own family) and make requests for their health attention or health care (unlike individual users, who need to separately ask for the other party's ID number or scan the ID QR code to query or pay attention).

[0353] It should be noted that the creator and administrator of the family tree is only an identity with family tree management responsibilities. The administrator is actually an individual user, and the administrator is only the identity with the authority to manage the family tree. As an individual user, any family member can not only participate in the family cluster health management, but also carry out health care activities with other individual users who are not family members as individuals, such as Figure 40 If the individual user is a family member who has not joined the family cluster, he or she can also apply to join the family tree from a family member with family tree management rights, or be invited to join the family tree, such as Figure 40 As shown by the connecting lines ⑤⑥.

[0354] A user can enable health management applications in both group cluster mode and family cluster mode at the same time, and naturally enjoy the relevant permissions of group cluster mode and family cluster mode.

[0355] (13) Diversification of data collection equipment in AI health management systems and expansion into third-party data access methods

[0356] The expansion and compatibility capabilities of an AI health management system are crucial. They not only determine whether the system can adapt to diverse user needs and complex health management scenarios, but also directly impact its practicality and sustainability. By connecting and integrating with various types of devices and third-party systems, the system can acquire more comprehensive health data and provide users with more accurate health assessment and management services. Expansion and compatibility methods include connecting with wearable smart devices, personal home medical devices, and third-party diagnostic or physical examination institution systems. Each method utilizes specific technical means and standard protocols to ensure accurate data transmission and stable system operation. This comprehensive expansion and compatibility aims to break down information silos, achieve the interconnection and interoperability of health data, and ultimately, be user-centric, enhance the user's health management experience, and drive the health management field towards a more efficient and intelligent direction.

[0357] 1. The expansion compatibility of acquisition equipment includes but is not limited to the following aspects.

[0358] Wearable Smart Devices: The AI ​​health management system is designed to be compatible with a wider range of wearable smart devices, from common smart bracelets and watches to emerging smart rings and anklets. These wearable smart devices integrate a variety of sensors, such as optical heart rate sensors, electrode ECG sensors, blood pressure sensors, thermometers, accelerometers, and gyroscopes. The system accurately captures health data collected by these devices, including heart rate, electrocardiogram (ECG), blood pressure, blood oxygen saturation, body temperature, and exercise status, providing users with comprehensive health monitoring services.

[0359] Personal home medical devices: The system's data collection equipment includes not only wearable smart devices but also personal home medical devices. The system is compatible and integrated with a variety of personal home medical devices. For example, common home medical devices such as electronic blood pressure monitors, blood glucose meters, and body fat scales can all be connected to the system. When users use these devices to measure relevant health indicators, the data is transmitted to the system in real time. This integration allows the system to collect more comprehensive health data for more accurate health assessments.

[0360] Special collection equipment for healthy eating: that is, the user's daily meal records are obtained by the system through special collection equipment, algorithms or other means, including but not limited to meal time, food type, intake, dining environment and other information.

[0361] Work and living environment monitoring equipment: that is, the user's work and living environment monitoring data is the environmental monitoring data of the above-mentioned users obtained by the system through special collection equipment, algorithms or other means, including but not limited to environmental air pressure, temperature, humidity, light intensity, ultraviolet intensity, noise intensity, air quality and other information, as well as environmental safety, comfort and other information.

[0362] Acoustic collection equipment: This system performs high-fidelity recording of the user's own voice, that of those around them, and the acoustic environment. It then applies cutting-edge audio processing algorithms and professional emotion recognition models to precisely extract speech features from the recorded audio data. Using sentiment analysis algorithms built through in-depth training of large AI models, it accurately identifies the speaker's emotional state and tendencies. Furthermore, it integrates health monitoring data, such as physiological indicators indirectly related to the user's emotions, from other collection devices within the system for comprehensive and integrated analysis, resulting in scientific and objective assessment results. For example, it can accurately distinguish between aggressive and ferocious emotions and gentle and friendly attitudes, thereby achieving deep insights into the emotional dimension of voice and enabling precise quantitative analysis. Based on this, the system presents the user or their guardian with a detailed and comprehensive analysis report and recommends targeted adjustments. These measures not only play a key role in ensuring the user's personal safety, but also have significant positive value and significance in maintaining their mental health and promoting social well-being.

[0363] 2. Expand the scope of health supplementary information, including but not limited to the following aspects

[0364] From the perspective of collection channels, the system expands from using only specific collection channels to collecting all collection channels and methods, without specifying dedicated or specific collection equipment, to collecting supplementary health information directly or indirectly related to the user's health;

[0365] From the perspective of collection device categories, this includes but is not limited to ordinary wearable smart devices that have not been certified as medical devices, wearable-like health data collection devices that have been certified as medical devices, personal or home medical devices and health testing equipment that have been certified as medical devices, professionally certified or uncertified work and living environment monitoring data monitoring equipment and other collection devices, professionally certified or uncertified acoustic collection equipment, and other health supplementary information that is directly or indirectly related to the health of the above-mentioned users;

[0366] From the aspect of the use of collection devices, including but not limited to the use of a single designated type of collection device, the alternating use of multiple collection devices of different types, and the simultaneous use of multiple collection devices of different types to collect supplementary health information directly or indirectly related to the user's health;

[0367] From the perspective of data information collection methods, it has expanded from collecting health data through collection equipment to directly or indirectly obtaining medical records, conditions and health-related information about the above-mentioned users from the relevant management systems of one or more third-party diagnosis or physical examination institutions through data interfaces and communication protocols.

[0368] 3. Connect with third-party diagnosis and treatment or physical examination institutions to expand the concept of data collection equipment to system data access

[0369] System Interconnection: The system can be expanded to integrate with third-party diagnostic or medical examination institutions. This integration is achieved through standardized data interfaces and encryption technology to protect data privacy and security. For example, HL7 (Health Level 7) or other relevant data exchange standards can be used to ensure accurate and secure data transmission between different systems. Furthermore, encryption technology is used to protect data privacy and security during transmission.

[0370] Rich health information sources: Data obtained from third-party institutions is diverse. This includes, but is not limited to, historical diagnostic results, treatment plans, disease progression records, laboratory test reports (such as blood and urine test results), and imaging examination data (such as X-rays and CT scans). This data enriches the system's health information sources and provides important reference for health assessments.

[0371] Improve the accuracy of health assessments: When assessing a user's health status, data from third-party institutions plays a key role. For example, when assessing a user's risk of cardiovascular disease, in addition to considering the current health monitoring data (such as heart rate, blood pressure) obtained from the collection device and the user's supplementary health information (such as lifestyle, family medical history), the system will also refer to the historical diagnosis results of cardiovascular disease from third-party institutions, the use of drugs in the treatment plan, and the changes in blood pressure and blood lipid levels in the disease progression record. By comprehensively considering these aspects, the system can more comprehensively analyze the user's disease risk factors and develop a more personalized health management plan.

[0372] Disease Monitoring and Tracking Applications: For users with chronic diseases, data from third-party organizations is valuable for disease monitoring and tracking. The system can obtain information on the user's disease progression at different time points, such as blood sugar control for diabetic patients and changes in renal function indicators for patients with chronic kidney disease. It also provides information on treatment effectiveness, such as symptom improvement after medication. Based on this information, the system can promptly adjust health management strategies and provide more timely and effective health interventions, helping users better control disease progression and improve their quality of life.

[0373] Improved user-centric experience: By integrating with third-party diagnostic and medical examination institutions, the system provides a user-centric, centralized health information and health management experience and services. Users no longer need to frequently switch between different systems to obtain comprehensive health information and personalized health management services. This one-stop service model improves user satisfaction and convenience.

[0374] Expansion and optimization of system functions: From the perspective of system functions, this docking expands the functional categories of the acquisition terminal, so that it is not limited to obtaining data from the acquisition equipment. At the same time, it also optimizes the health assessment and management capabilities of the entire AI health management system. The system can use richer information sources for more accurate analysis and decision-making, providing users with better health management services. (14) Diversification of the auxiliary operation terminals of the AI ​​health management system

[0375] In the field of modern health management, people are increasingly focusing on using a variety of smart devices to conveniently manage their personal health. The AI ​​health management system aims to achieve compatibility with multiple types of secondary operation terminal devices through innovative design, such as smart robots, smart speakers, digital photo frames and other smart devices. These secondary operation terminal devices can be developed based on the communication protocol of the health management system, or the system can actively be compatible with common smart devices on the market. Users can purchase secondary operation terminal devices that are compatible with the system, or they can make full use of the resources of the existing secondary operation terminal devices. After simply installing the operation terminal program of this system, they can use the relevant functions and services of this health management system. In special cases, the secondary operation terminal device can also integrate some functional modules of the collection terminal. In this case, the secondary operation terminal device combines the functions of the operation terminal and the collection terminal in one. The user health monitoring data or environmental monitoring information collected by it can be directly transmitted to the AI ​​cloud health service platform.

[0376] 1. Smart speakers as secondary operation terminal devices

[0377] Health information query and voice interaction: If the smart speaker is connected to the AI ​​health management system as a secondary operation terminal device, the user can interact with the smart speaker through voice commands to query health information. For example, the user says "Dr. Xiaokang, check the number of steps I took yesterday". After the smart speaker receives the voice command, it transmits the command to the AI ​​health cloud service platform through the connection with the AI ​​health management system. The cloud service platform identifies the relevant health data of the user based on the user account, and then feeds back the query results (such as "Your number of steps yesterday was 8,000") to the user in the form of voice through the smart speaker. Users can also ask for other health information, such as "What is the average value of my blood pressure in the past week?", etc. The smart speaker can accurately understand and obtain system feedback to provide users with the required information.

[0378] Health reminders and push notifications: The system pushes health reminders to users via smart speakers based on their health management plans and pre-set reminder rules. For example, at 7:00 a.m. every day, the smart speaker will automatically announce, "Good morning! You need to take your blood pressure medication today. Please remember to take it on time." If the user has a physical examination appointment that day, the smart speaker will remind them before the appointment, "You have a physical examination appointment at 10:00 a.m. today. Please prepare in advance and bring relevant documents to the physical examination center." These reminders help users develop good health habits and ensure they don't miss important health matters.

[0379] Emergency response (special function integration): In special circumstances, if the smart speaker integrates some acquisition terminal function modules, such as being equipped with a simple ECG monitoring sensor (realized through external devices or built-in sensors). When the user suddenly feels heart discomfort at home, the user can activate the ECG monitoring function on the smart speaker, collect the user's ECG data, and transmit it to the AI ​​cloud health service platform in real time. After the platform receives the data, the AI ​​health agent quickly analyzes the data to determine the user's heart health status. If an abnormality is found, the system automatically triggers the emergency rescue mechanism. On the one hand, it provides emergency response guidance to the user through the smart speaker (such as "Please stay calm, sit down and rest, and avoid strenuous exercise"), and on the other hand, it immediately sends an alarm message to the preset emergency contact (such as family, doctor or emergency center) to inform the user's location and health status so that rescue measures can be taken in time.

[0380] 2. Intelligent robots as auxiliary operation terminal devices

[0381] Health information query and voice interaction: If the intelligent robot is connected to the AI ​​health management system as a secondary operation terminal device, the user can interact with the intelligent robot through voice commands to query health information. For example, the user says "Dr. Xiaokang, check the number of steps I took yesterday". After receiving the voice command, the intelligent robot transmits the command to the AI ​​health cloud service platform through the connection with the AI ​​health management system. The cloud service platform identifies the relevant health data of the user based on the user account, and then feeds back the query results (such as "Your number of steps yesterday was 8,000 steps") to the user in the form of voice through the intelligent robot. Users can also ask for other health information, such as "What is the average value of my blood pressure in the past week?", etc. The intelligent robot can accurately understand and obtain system feedback to provide users with the required information.

[0382] Health companionship and interactive services: In daily life, intelligent robots interact with users as health companions. For example, when a user is sitting in the living room to rest, the robot will proactively approach the user and ask, "How are you feeling today? Do you need a health check?" The user can communicate with the robot through voice. For example, if the user answers, "I'm a little tired today," the robot may reply, "You can take a proper rest, drink a glass of water, and relax. If you want to learn more about how to relieve fatigue, I can provide you with relevant information." The robot can also recommend suitable health activities or entertainment content based on the user's health status and interests, such as "Based on your physical condition, today is suitable for some relaxing yoga exercises. I can play yoga music for you," to encourage users to actively participate in health management.

[0383] Health data collection and transmission (functional integration): Assume that this intelligent robot integrates environmental monitoring modules (such as air quality sensors, temperature and humidity sensors, etc.). During daily activities, the intelligent robot can collect real-time information such as the air quality index, temperature, and humidity of the surrounding environment, and automatically transmit it to the AI ​​cloud health service platform, providing data support for the system to analyze the user's health status and environmental impact.

[0384] 3. Digital photo frame as a secondary operation terminal device

[0385] Health information query and voice interaction: If the digital photo frame is connected to the AI ​​health management system as a secondary operation terminal device, the user can interact with the digital photo frame through voice commands to query health information. For example, the user says "Dr. Xiaokang, check the number of steps I took yesterday". After receiving the voice command, the digital photo frame transmits the command to the AI ​​health cloud service platform through the connection with the AI ​​health management system. The cloud service platform identifies the relevant health data of the user based on the user account, and then feeds back the query results (such as "Your number of steps yesterday was 8,000 steps") to the user in the form of voice through the intelligent robot. Users can also ask for other health information, such as "What is the average value of my blood pressure in the past week?", etc. The digital photo frame can accurately understand and obtain system feedback to provide users with the required information.

[0386] Visual display of health data: Digital photo frames are primarily used to display users' health data and related information in an intuitive and aesthetically pleasing manner. For example, the digital photo frame's screen displays a chart showing the user's weight change trend, blood pressure fluctuation curve, and other physiological indicators over the past month, allowing users to clearly understand changes in their health status. At the same time, the photo frame also displays health assessment results generated by the system based on the user's health data, such as health risk level prompts and health advice summaries. For example, when the system analyzes the user's weight data and discovers that the user's weight has recently increased, the photo frame displays a prompt such as "Your weight has increased recently. Please pay attention to controlling your diet and increasing your exercise." This reminds the user to pay attention to health issues.

[0387] Health Knowledge Push and Education: The system regularly pushes health information and popular science articles to the digital photo frame, displaying them on the screen with pictures and text. These articles cover topics such as disease prevention, healthy eating, exercise and fitness, and mental health, with examples like "How to Prevent Cardiovascular Disease," "Summer Dietary Considerations," and "Simple Exercises for Office Workers." Users can easily access this health information while browsing the digital photo frame in their free time, improving their health awareness and self-care skills.

[0388] 4. The secondary operation terminal device may integrate some functional modules of the acquisition terminal

[0389] In special cases, the secondary operation terminal device can also integrate some functional modules of the collection terminal. In this case, the secondary operation terminal device combines the functions and properties of the operation terminal and the collection terminal in one. The user health monitoring data or environmental monitoring information collected by it can be directly transmitted to the AI ​​cloud health service platform. In this case, the secondary operation terminal can not only collect user health monitoring data or environmental monitoring information, but also the user can directly consult with the AI ​​health digital human through the secondary operation terminal.

[0390] (15) Layered Architecture and Special Compatibility Mode of AI Health Management System

[0391] In today's digital health management field, with the rapid development of technology and the increasing diversification of user needs, AI health management systems are facing the challenge of how to achieve efficient operation, wide compatibility and provide high-quality services in a complex equipment environment. In order to meet these challenges, the present invention innovatively proposes a layered architecture and a special compatibility mode, aiming to further optimize the system design, improve system performance, expand the scope of system application, and bring users a more convenient, comprehensive and personalized health management experience. This architectural model not only reflects the deep integration and innovation of the system at the technical level, but also reflects the in-depth thinking on the applicability of health management services in different equipment and user scenarios. It is an important exploration and breakthrough of the present invention in solving practical health management problems.

[0392] 1. Architecture layering principle

[0393] In terms of development and compatibility, the AI ​​health management system adopts a layered architecture to adapt to diverse device environments and enhance system flexibility and scalability. This architecture is primarily divided into a host computer and a narrowly defined "AI health management system (without a host computer)." The host computer plays a key role in device coordination and communication within the entire system. It specializes in handling complex tasks such as communication protocol adaptation and control command exchange between acquisition devices and secondary operation terminals. By parsing and converting the communication protocols of various acquisition devices (such as smart bracelets, smart watches, and home medical testing equipment) and secondary operation terminals (such as smart speakers and smart robots), the host computer ensures accurate data transmission and effective control between devices. For example, when an acquisition device transmits health monitoring data using the Bluetooth Low Energy (BLE) protocol, the host computer accurately identifies and receives the data, converting it into a unified data format within the system for subsequent processing. The host computer is also responsible for sending control commands to the acquisition device, such as activating specific sensors for data collection and adjusting the collection frequency, and managing the connection status and function calls of the secondary operation terminals.

[0394] Corresponding to this is the "AI Health Management System (without host computer)", which uses H5 as a client AI health management system at the pure software system level. This design makes the system more independent and portable at the software level. H5 technology is based on Web standards and can run on a variety of operating systems and devices without the need for native development for specific platforms. The "AI Health Management System (without host computer)" uses the cross-platform characteristics of H5 to build core functional modules such as user interface display, health data display and preliminary analysis, and user interaction. For example, users can access the AI ​​health management system in H5 form through browsers on mobile phones, tablets and other devices to view their own health monitoring data trends, receive health reminder notifications, and conduct simple health consultation interactions with AI health digital people.

[0395] 2. Nesting mechanism with ordinary wearable smart device APP

[0396] Nesting implementation method: The nesting between the H5 integrated system of the "AI health management system (without host computer)" and the APP of ordinary wearable smart devices is achieved through carefully designed technical means. Among them, the implementation of communication protocols and controls is crucial. The common method is to develop docking protocols through a software development kit (SDK) or an application programming interface (API). Taking the SDK as an example, developers of wearable smart device APPs can integrate the SDK provided by the AI ​​health management system into their own applications. The SDK contains a series of predefined functions and interfaces for realizing communication with the H5 integrated system. For example, through the data transmission interface in the SDK, the APP can send the collected health data (such as heart rate, exercise steps, etc.) to the H5 integrated system in the prescribed format; at the same time, using the control interface, the APP can receive instructions sent by the H5 system, such as adjusting collection parameters, starting specific health function modules, etc. The application method of the API is similar. It provides a set of callable methods to enable data interaction and functional collaboration between the APP and the H5 integrated system.

[0397] Functional Integration and Expansion: Once embedded and communication protocols and control are implemented, apps for standard wearable smart devices will incorporate some of the core functions of an AI health management system, such as preliminary analysis and display of health data, selected health advisory services based on natural language processing, and health reminders. Regarding health data management, apps can leverage the capabilities of the H5 integrated system to conduct more in-depth analysis and visualization of collected health data. For example, heart rate data over a period of time can be displayed as a chart to help users more intuitively understand their heart rate trends. Regarding user interaction, apps can integrate some of the functions of an AI health digital human, providing users with health advisory services based on natural language processing. The AI ​​health digital human in the H5 integrated system will analyze existing health data and knowledge base, attempt to answer users' questions, and provide preliminary health advice. Furthermore, apps can leverage the H5 system to implement health reminders, such as reminders to take medication and exercise on time, thereby improving user compliance with health management plans.

[0398] 3. The significance and value of special forms in the system

[0399] Improved System Compatibility: This specialized form factor significantly enhances the AI ​​health management system's compatibility with existing devices. The market for standard wearable smart devices is diverse, with different brands and models exhibiting differences in hardware configuration, operating systems, and communication protocols. By embedding the H5 integration system within its app, it is possible to integrate some of the AI ​​health management system's functions without requiring extensive hardware and underlying software modifications for each device. This means that more existing wearable smart devices can be integrated into the AI ​​health management system, expanding the system's potential user base and improving its market applicability.

[0400] Accelerate function promotion and application: For developers, using common wearable smart device apps as a vehicle to promote some of the core functions of the AI ​​health management system can accelerate the popularization of these functions. Users do not need to download and install independent AI health management applications; they can experience new health management functions within the familiar wearable device app. This lowers the threshold for users to obtain health management services and increases their willingness to try system functions. At the same time, because wearable devices are often closely integrated with users' daily lives, users can more conveniently access health data and related services, thereby increasing their attention to and participation in health management and helping to cultivate their health management habits.

[0401] Enriching the system ecology and data sources: From the perspective of the system as a whole, this special form enriches the ecology of the AI ​​health management system. The access of more wearable smart devices means more diversified data sources. These data can further enrich the system's health information library and provide support for more accurate health assessments and personalized health management plan formulation. For example, wearable devices of different brands may collect health data of different dimensions (such as some devices focus on sleep monitoring, while others focus on motion analysis). Integrating this data into the AI ​​health management system can achieve a more comprehensive health portrait construction, enhance the system's ability to understand the user's health status, and thus provide users with better quality and more personalized health management services.

[0402] 4. Synergy with the invention as a whole

[0403] Follow the system design concept: This special form fully follows the design concept of the present invention that is user-centric and provides comprehensive and accurate health management services. By nesting with ordinary wearable smart device APPs, the system can be closer to the user's daily usage scenarios and integrate health management services into the user's existing device usage habits. Whether in the process of exercise (such as obtaining exercise-related health advice through the smart bracelet APP) or in daily life (such as receiving health reminders through the smart watch APP), users can get the support of the system anytime and anywhere, which further reflects the system's original design intention of all-round and full-scenario care for user health.

[0404] Enhanced system function system: It complements the functional modules described in other parts of the invention content and together builds a more powerful health management system. For example, in terms of obtaining health information sources, the data collected and transmitted by ordinary wearable smart device APP after the H5 integrated system is nested becomes part of the overall health information source of the system, and is combined with other collection devices (such as home medical testing equipment) and health supplementary information entered by users on the operating terminal (such as basic health files, medical and physical examination reports, etc.), providing a richer data foundation for the system's AI health intelligent body, thereby making the health assessment results more accurate and comprehensive. At the same time, at the user interaction level, the implementation of the interactive function with the AI ​​health digital person in the wearable device APP enriches the channels and methods of user interaction with the system, and forms a multi-dimensional interactive mode with the interaction on the main operating terminal (such as an independent application on a smartphone) and the secondary operating terminal (such as a smart speaker), meeting the needs of users in different scenarios.

[0405] Expand the scope of system application: From the perspective of the scope of system application, this special form further expands the application boundaries of the AI ​​health management system. It enables the system to run not only on specially equipped hardware devices (such as acquisition equipment and operation terminals that are deeply integrated with the system), but also to extend health management services to a wider user group with the help of existing ordinary wearable smart device platforms. This helps to improve the adaptability of the system in different user groups and usage scenarios. Whether it is a user with a strong sense of health and the pursuit of professional health management, or an ordinary user who has a preliminary demand for health management and relies on daily wearable devices, they can all benefit from the AI ​​health management system of the present invention on devices that are familiar and convenient to them, thereby realizing the effective expansion of the scope of application of the system from professional fields to mass consumer fields.

[0406] 5. The key significance of layered architecture and special compatibility modes within the scope of patent protection

[0407] Uniqueness and protectability of innovative architecture: The layered architecture proposed in the present invention divides the AI ​​health management system into a host computer and an "AI health management system (without host computer)" in the form of H5, which is a unique design and has significant innovation in the field of health management technology. The host computer focuses on processing device communication protocols and control functions, and the way the H5 integrated system uses cross-platform features to build client functional modules is completely different from the traditional single architecture or simple integration method. This unique architectural combination has not yet been widely used in the industry and constitutes a significant feature that is different from the existing technology. According to the requirements of the patent law for the creativity of inventions, that is, the invention has outstanding substantive features and significant progress, this layered architecture obviously meets this standard. It solves the problems of multi-device compatibility and flexible functional expansion through a new design idea, provides a unique technical path for the development of health management systems, has the basic conditions for obtaining patent protection, can stand out among many similar technologies, and become one of the core protection points of the present invention.

[0408] The technical contribution and protection value of the special compatibility mode: In the special compatibility mode, the nesting mechanism of the H5 integrated system of the "AI Health Management System (without host computer)" and the ordinary wearable smart device APP achieves communication and functional integration through the development of docking protocols via SDK or API, which is another important innovation of the present invention. This compatibility mode greatly expands the application scope of the system, enabling the AI ​​health management system to quickly promote some of its core functions with the help of the existing wearable device ecosystem. This is of great significance in terms of both technical implementation and market application. From the perspective of technical contribution, it overcomes the compatibility difficulties faced by traditional health management systems when integrating with different types of wearable devices. Through standardized docking methods, it achieves efficient collaboration between different software systems and improves the overall flexibility and adaptability of the system. Within the scope of patent protection, this special compatibility mode is a non-obvious technical improvement that has a positive role in promoting the development of industry technology and should be protected by patent law. It not only protects the innovative achievements of the present invention in specific technical means, but also prevents other competitors from using the same or similar compatibility methods without authorization, thereby maintaining the technical advantage of the present invention in market competition.

[0409] The close connection between the overall architecture and the invention's objectives ensures complete protection: The layered architecture and special compatibility mode are closely linked to the overall health management objectives of this invention, together forming a complete technical solution. This architecture and compatibility mode enable a comprehensive innovation process, from device data collection, transmission, and processing to providing personalized health management services to users. The collaborative operation of the host computer and H5 integration system ensures accurate acquisition, efficient transmission, and intelligent analysis of health data, while compatibility with wearable device apps seamlessly integrates health management services into users' daily device usage, enhancing user experience and convenience. This close connection ensures that the entire invention forms a cohesive technical whole, meeting the patent application requirements for completeness and consistency of the technical solution. During the patent protection process, this integrity helps ensure comprehensive and effective protection for the entire invention, preventing others from circumventing patent infringement liability by imitating or improving upon certain technical features. Any attempt to undermine the integrity of this architecture or replicate the special compatibility mode will be considered an infringement of the invention's patent rights. This ensures that the invention's innovative achievements in the field of health management receive adequate legal protection, encourages inventors to continue investing in innovative research and development, and promotes continuous technological advancement in the industry.

[0410] (16) Self-learning mechanism of AI health management system

[0411] The self-learning mechanism of the AI ​​health management system is one of the core advantages of the system. This mechanism is based on the collaborative work of multiple components of the system, covering hardware devices and software systems. Various types of health data collected by the acquisition terminal and the supplementary health information and feedback of users obtained by the operation terminal provide rich materials for learning. The system can learn the associations between health data, user behavior patterns and feedback information, and can also involve new knowledge, theories and methods in the field of health. Based on these learning contents, the system continuously adjusts the algorithm and model parameters to improve the accuracy of the prediction of user health status, provide more effective suggestions in disease prevention and management, and improve user satisfaction by comprehensively learning user feedback information, thereby fully demonstrating the intelligence and adaptability of the system and reflecting the innovation and superiority of the present invention.

[0412] 1. Learning foundation at the system level

[0413] The AI ​​health management system is a complex whole, encompassing multiple components, including hardware devices (such as data collection terminals and operation terminals) and software systems (such as AI health agents). These components all play an indispensable role in the self-learning mechanism.

[0414] The terminal device continuously collects various health data, including but not limited to health vital signs (such as heart rate, blood pressure, and body temperature) and health behavior data (such as sleep and exercise). Through system learning, the system can also learn about a wider range of health factors, and further expansion of the system, such as the possibility of monitoring data related to the user's work and living environment (such as air quality and noise level). This data provides the foundation for the system's learning.

[0415] The user terminal device not only serves as an interface for interaction with the system, but also collects supplemental health information input by the user (such as medical reports, lifestyle descriptions, emotional state, etc.). Furthermore, user feedback on the health assessment results and management recommendations provided by the system on the user terminal device is also an important basis for system learning.

[0416] The AI ​​Health Cloud Service Platform's hardware system provides strong support for data storage and processing. Its high-performance computing capabilities and large-capacity storage devices ensure that large amounts of health data can be effectively processed and stored, providing a material foundation for the system's self-learning.

[0417] 2. Synergy in the learning process

[0418] During the self-learning process, different components work together. The data collected by the collection terminal device is first transmitted to the operation terminal device, and then uploaded to the AI ​​health cloud service platform after preliminary processing.

[0419] The AI ​​health agent runs on the AI ​​health cloud service platform, using its own algorithms and models to analyze uploaded data. Simultaneously, it adjusts its algorithm and model parameters based on user feedback from operating terminal devices. The AI ​​health agent development module within the AI ​​health cloud service platform plays a key role here. This module provides the AI ​​health agent with an efficient algorithm development and optimization environment, enabling it to better process and analyze data and more accurately adjust algorithm and model parameters.

[0420] For example, if a user reports that a health assessment is inaccurate, the system will re-acquire detailed data for the relevant time period from the collection terminal device, combine it with other supplementary health information entered by the user on the operating terminal device, and use the computing power of the AI ​​health cloud service platform to re-analyze this data and adjust the relevant algorithms and model parameters of the AI ​​health agent.

[0421] 3. Learning based on multiple data types

[0422] What the system learns includes not only the health data itself, but also the user's behavior patterns and feedback information.

[0423] From the perspective of health data, the system will learn the relationship between different health indicators. For example, through long-term observation, it is found that high blood pressure may be related to poor sleep quality, reduced exercise and certain eating habits.

[0424] From the perspective of user behavior patterns, the system will learn how users accept and implement health management suggestions. For example, if the system recommends that a user increase their exercise volume, the system will observe whether the user actually increases their exercise volume and the impact of the increased exercise volume on health indicators.

[0425] From the perspective of user feedback information, the system will adjust its own assessment methods and management strategies based on the user's satisfaction with the health assessment results and improvement suggestions.

[0426] 4. Learn new theories, knowledge, and methods

[0427] The AI ​​health agent also has the ability to learn new theories, knowledge, and methods that are constantly emerging in the health field. It can quickly access new knowledge in related fields, learn and understand new health theories, and continuously refine its algorithms and models by validating and integrating them with its existing knowledge system and data to better adapt to the evolving needs of health management. The AI ​​health agent development module also supports this process, enabling the agent to better integrate new knowledge and optimize its algorithms and models.

[0428] 5. Comprehensive learning feedback to improve satisfaction

[0429] The feedback information obtained through interaction with users, including the accuracy of responses, monitoring of equipment operation, and users' general evaluation and trust, can all be worthy of comprehensive learning by AI agents.

[0430] Regarding the accuracy of responses, if users point out that the answers to certain health consultations are inaccurate, the AI ​​health agent can use the AI ​​health agent development module to reanalyze relevant data and knowledge and improve its response strategy.

[0431] Regarding the monitoring of equipment operation, if users report that the equipment has a malfunction or abnormality, the system can use the development module to analyze the relevant data and take corresponding measures to improve it.

[0432] Regarding users' general evaluation and trust, if users have a high overall satisfaction with the system, the system can analyze the reasons and continue to maintain and optimize related aspects; if users' satisfaction is low, the system needs to understand the reasons for users' dissatisfaction and make targeted improvements to achieve the best user satisfaction.

[0433] 6. Demonstration and application of learning outcomes

[0434] As the system continues to learn, its predictions about the user's health status become more accurate.

[0435] In terms of disease prevention, the system can more accurately predict a user's risk of developing a particular disease and provide preventive advice in advance. For example, for users with a family history of cardiovascular disease, the system can more accurately predict their risk of cardiovascular disease by learning from their family medical history, personal health data, and lifestyle information, and provide targeted dietary, exercise, and lifestyle adjustment recommendations.

[0436] In terms of disease management, the system can promptly adjust management plans based on the progression of the user's condition. For example, for diabetic patients, the system will continuously adjust the diabetes management plan to improve treatment effectiveness based on information such as changes in the patient's blood sugar levels, diet and exercise habits, and the effectiveness of medication.

[0437] Furthermore, the system's learning outcomes are also reflected in improved user experience. Through more accurate health assessments and more reasonable management recommendations, users have increased their trust in the system and are more willing to interact with it, further promoting system learning and optimization.

[0438] By elaborating on the self-learning mechanism of the AI ​​health management system from multiple aspects, the role of the system can be more comprehensively demonstrated, reflecting the innovation and superiority of the present invention.

[0439] (17) Export and sharing of health information source data of AI health system

[0440] In modern health management, the effective use and sharing of data is crucial for providing comprehensive and accurate health services. As an advanced health management tool, the AI ​​health management system's ability to export and share health information source data is of great significance and purpose. This function not only allows users to independently manage and use their own health data, but also involves collaboration between the system and third-party diagnosis, treatment, or physical examination institutions to achieve higher-quality health care services. Methods and approaches include user-initiated export and download, as well as secure protocol-based connection and sharing between the system and third-party institutions. These methods aim to break down data barriers, promote the flow of health information, improve the accuracy and effectiveness of health assessments and medical services, and ultimately enhance users' health management experience and well-being.

[0441] 1. Users can export and download data independently

[0442] like Figure 21As shown in Figure 9, the AI ​​health management system fully embodies a user-centric design concept. Users have control over their own health data or information in the system and can export and download relevant health data according to their needs. The system will organize this data into files that conform to a certain format, such as the common CSV format, to facilitate reading and analysis by third-party organizations. This user-controlled data export and download function not only gives users greater autonomy but also facilitates their use in different health management scenarios, allowing users to better participate in their own health management process.

[0443] 2. The system connects with third-party organizations to share data

[0444] At the same time, if Figure 21 As shown in Figure ②, the AI ​​health management system boasts robust scalability and compatibility, enabling the sharing of health data and information through network and protocol integration with third-party medical or physical examination institution information systems. With explicit user authorization, the relevant health systems of third-party medical or physical examination institutions can access the user's AI health management system's relevant health data and information for reference.

[0445] This data sharing method is based on strict network security and data protection protocols. For example, encrypted network transmission channels are used to ensure the security and integrity of data during transmission. Furthermore, the system implements strict authentication and authorization management for transmitted data to prevent unauthorized access or tampering.

[0446] When a third-party organization obtains a user's health data, it will combine its own expertise and medical resources to provide customers with relevant health care services. For example, if the third-party organization is a professional cardiovascular disease diagnosis and treatment center, after obtaining the user's long-term monitoring data such as heart rate and blood pressure, as well as other relevant health information, it will combine its own clinical experience and advanced diagnosis and treatment technology to provide users with more accurate disease diagnosis, more personalized treatment plans, and more comprehensive health management recommendations. This system's cooperation model with third-party organizations fully integrates the advantages of both parties' resources, provides users with a higher-quality health service experience, and further expands the application scope and value of AI health management systems in the field of health care. BRIEF DESCRIPTION OF THE DRAWINGS

[0447] Figure 1 System composition basic equipment diagram - equipment components

[0448] Figure 2 System composition basic architecture diagram - software system

[0449] Figure 3 Schematic diagram of the deployment of the system's main functional modules - AI health intelligent body

[0450] Figure 4 System main functional module deployment diagram - AI health cloud service platform

[0451] Figure 5 System main functional module deployment diagram - main operation terminal

[0452] Figure 6 System main functional module deployment diagram - auxiliary operation terminal

[0453] Figure 7 System operation basic structure diagram - main operation terminal mode

[0454] Figure 8 System operation basic structure diagram - (main + auxiliary) operation terminal mode

[0455] Figure 9 System operation basic architecture diagram - multi-operation terminal interactive mode

[0456] Figure 10 System user application mode diagram - single user basic application mode

[0457] Figure 11 System user application mode diagram - single user main and secondary integrated application mode

[0458] Figure 12 System user application mode diagram - dual-user health care application mode

[0459] Figure 13 System User Application Mode Diagram - Dual-User Health Hosting Application Mode (Cluster User Mode 1)

[0460] Figure 14 System User Application Mode Diagram - Multi-User Integrated Management Application Mode (Cluster User Mode 2)

[0461] Figure 15 System user health management and interaction diagram - basic health management model

[0462] Figure 16 System user health management and interaction diagram - health attention mode

[0463] Figure 17 System user health management and interaction diagram - health hosting model

[0464] Figure 18 AI Health Management-System Workflow Diagram

[0465] Figure 19 AI Health Management - AI Health Data Input and Output Diagram

[0466] Figure 20 AI Health Management-Schematic Diagram of AI Health Information Source Acquisition

[0467] Figure 21 AI Health Management-Schematic Diagram of AI Health Assessment Result Output

[0468] Figure 22 Schematic diagram of AI health management interaction on the main operation terminal - basic interface and association

[0469] Figure 23 Schematic diagram of AI health management interaction on the main operation terminal - AI health assessment result display interface

[0470] Figure 24 Schematic diagram of AI health management interaction on the main operation terminal - AI health digital human reminder interface

[0471] Figure 25 Schematic diagram of AI health management interaction on the main operation terminal - standard dialogue window of AI health digital human

[0472] Figure 26 Schematic diagram of AI health management interaction on the main operation terminal - AI health digital human audio-visual dialogue window

[0473] Figure 27 Schematic diagram of AI health management interaction on the main operation terminal - user medical and physical examination report management interface

[0474] Figure 28 AI Health Management Interaction Diagram for the Main Operation Terminal - Medical and Physical Examination Report Management Operation Guide

[0475] Figure 29 User registration and device binding mechanism diagram - individual user single device association scenario

[0476] Figure 30 User registration and device binding mechanism diagram - individual user multi-device association scenario

[0477] Figure 31 User Management-Health Management Permissions Diagram

[0478] Figure 32 User Management-Health Concern Permissions Diagram

[0479] Figure 33 User Management-Health Hosting Permissions Diagram

[0480] Figure 34 Schematic diagram of group cluster structure

[0481] Figure 35 Diagram of group cluster permission management

[0482] Figure 36Schematic diagram of group cluster user rights management mechanism

[0483] Figure 37 Family cluster structure diagram - user roles

[0484] Figure 38 Diagram of family cluster structure - marriage relationship between three clans of family members

[0485] Figure 39 Schematic diagram of family cluster structure - family member (perspective) three-family standard names

[0486] Figure 40 Schematic diagram of family-style cluster permission management

[0487] Figure 41 Schematic diagram of the family cluster user rights management mechanism DETAILED DESCRIPTION

[0488] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is further explained in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0489] Therefore, the following detailed description of the embodiments of the present invention is not intended to limit the scope of the invention as claimed, but merely represents some embodiments of the present invention. All other embodiments obtained by persons of ordinary skill in the art based on the embodiments of the present invention without engaging in creative work shall fall within the scope of protection of the present invention.

[0490] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features and technical solutions therein may be combined with each other.

[0491] In the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right" and the like indicate an orientation or positional relationship, they are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, the terms describing the positional relationship in the accompanying drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For those of ordinary skill in the art, the specific meanings of the above terms can be understood according to the specific circumstances. In addition, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0492] It should be noted that like reference numerals and letters denote like items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined or explained in the subsequent drawings.

[0493] It should be noted that the specification of the present invention uses "special term codes" and "special term glossary" as important auxiliary tools, the purpose of which is to provide the examiner or reader with a way to understand the present invention, solutions and embodiments as systematically as possible, so as to minimize misjudgments and ambiguities. The specification of the present invention involves professional expressions in specific multiple fields, and the special terms therein are key elements in conveying the core content of the present invention. However, it should be clear that special terms and related descriptions exist only as a guiding tool. The core content of the present invention should not be improperly restricted due to the naming method or completeness of the explanation of special terms. For ordinary technicians in this field, based on their own professional knowledge and specific technical background, they can reasonably understand the specific meaning of the above-mentioned special terms.

[0494] It should be noted that the specification of the present invention uses "functional module code" and "functional module table" as important auxiliary tools, which are intended to provide the examiner or reader with a method for understanding the present invention and its embodiments as systematically as possible, and to minimize misjudgment and ambiguity. The specification of the present invention involves professional expressions in specific multiple fields, in which the names of the functional modules and the related descriptions (introductions) are key elements for conveying the core content of the present invention. However, it should be clear that the names of the functional modules and the related descriptions (introductions) exist only as a guiding tool. The core content of the present invention should not be improperly restricted due to the naming method of the functional modules or the completeness of the descriptions (introductions). For ordinary technicians in this field, based on their own professional knowledge and specific technical background, they can reasonably understand the specific meanings of the above-mentioned functional modules.

[0495] This embodiment involves an interactive personal health management system based on artificial intelligence and its operation method. It is an advanced health management system developed based on artificial intelligence technology and health professional knowledge training, and is named here as "AI health management system" (or simply referred to as "system" in the specification of this invention).

[0496] This specification provides a comprehensive and in-depth introduction to the AI ​​health management system, one of the embodiments of the present invention. The AI ​​health management system is a complex entity, integrating advanced artificial intelligence technologies with new health management concepts. It encompasses multiple layers of technology and functionality, from hardware devices to software systems, from individual user applications to cluster user management. To ensure a clear and accurate understanding of this system, we have organized the content in a systematic and logically coherent manner.

[0497] During this introduction, we'll begin with the system's basic architecture and gradually delve into the various functional modules and their interoperability. For key concepts and complex operational processes, we may first present the core points and overall logic, but may not immediately elaborate on some details and technical terminology. This is because we want readers to first establish a broad understanding of the system and grasp the connections and operational mechanisms between its various components. This way, readers can form a preliminary understanding of the system's general functions and operation based on the overall logic and contextual coherence.

[0498] As the content progresses, we will use a progressive approach to further explain and supplement the concepts and information mentioned earlier. This progressive presentation method can prevent readers from being overwhelmed by too much detail at the beginning, and help guide readers to gradually deepen their understanding and memory of the system as a whole.

[0499] We consider the diverse readership of this manual, including medical professionals, technical experts, and individuals seeking general health management services. Professionals, drawing on their expertise and experience, are able to understand and infer aspects of content that is not fully explained. For non-professional readers, we strive to provide a clear, logical structure and accessible language, enabling them to gradually understand the system's operating principles and application methods as they read.

[0500] (1) System composition basic equipment (equipment components)

[0501] The AI ​​health management system consists of two key components: basic equipment and infrastructure. As shown in Figure 01 [System Basic Equipment Diagram - Equipment Components], basic equipment, or equipment components, serve as the hardware carrier and environmental conditions for system operation. It primarily consists of data acquisition terminals, operating terminals, and the AI ​​health cloud service platform hardware system.

[0502] The acquisition device is short for "acquisition terminal device" and is used to collect health data. It can be a wearable smart device (such as a smart ring, smart bracelet, smart watch, etc.). This type of device integrates advanced sensor technologies such as optical heart rate sensors (PPG), electrode electrocardiogram (ECG), blood pressure detection sensors, thermometers, accelerometers, gyroscopes, etc., and can collect a variety of health data in real time. By combining sensors with professional algorithms, key health indicators such as health signs data (such as heart rate, electrocardiogram, blood pressure, blood oxygen saturation, body temperature, etc.), health behavior data (such as sleep status, exercise status, etc.), and health stress data can be monitored to obtain preliminary health monitoring information. At the same time, using wireless connections such as Bluetooth or WiFi, health monitoring data and health monitoring information are transmitted to the paired operating terminal device for subsequent AI calculations and analysis. In addition, the acquisition device can also refer to all related equipment such as other home or commercial medical devices, health detection equipment, etc. that can provide health data.

[0503] Operation terminal devices include various intelligent terminal devices equipped with intelligent operating systems (such as smartphones, tablets, etc.) or intelligent terminal devices without intelligent operating systems (such as smart speakers, etc.). Operation terminal devices are a general term for primary operation terminal devices and secondary operation terminal devices.

[0504] Main operation terminal devices include various smartphones and tablets equipped with intelligent operating systems. In terms of hardware, these devices are equipped with high-performance processors, ample memory, and high-resolution displays, enabling smooth operation of various complex applications. In terms of software, the operating systems of these devices are highly open and compatible, allowing users to install applications or related apps for the "main operation terminal (system)."

[0505] The secondary operation terminal device is an intelligent terminal device with audio input and output (including microphone and speaker) and network connection functions. It comes in two types: one is with a display screen, such as tablet computers, digital photo frames, and smart robots with screens; the other is without a display screen, such as smart speakers and smart robots without screens. There are two operating schemes: one is to have an intelligent operating system that can support the installation of "secondary operation terminal" applications; the other is to use an MCU solution with a non-intelligent operating system and combine it with a real-time operating system (RTOS) to execute the software program of the "secondary operation terminal". At the user level, the main application scenarios include receiving data from the collection device and transmitting it to the AI ​​health cloud service platform, as well as users interacting with the AI ​​health digital people for health consultation or Q&A. Due to its particularity, in addition to being suitable for some individual users as an auxiliary device for the main operation terminal device, it is more suitable for cluster user mode. For example, in a cluster, when the elderly are unable to use the main operation terminal device (such as a smartphone), family members can use their own mobile phones to install the main operation terminal, complete the elderly’s health supplementary information (such as establishing a basic health file, uploading medical and physical examination reports, conducting mood and ability assessments, etc.), and bind the collection device and the secondary operation terminal device on their behalf (or remotely bind), and at the same time consult the AI ​​health digital human on the elderly’s health status and the working status of the collection device.

[0506] The AI ​​health cloud service platform hardware system specifically refers to all hardware systems involved in the AI ​​health cloud service platform system.

[0507] (2) System infrastructure (software system)

[0508] As shown in Figure 02 [System Basic Architecture Diagram - Software System], the basic architecture of the AI ​​health management system is based on the software system. The AI ​​health management system can also be referred to as its software system. The system consists of four subsystems: the AI ​​health agent, the data collection terminal, the operation terminal, and the AI ​​health cloud service platform. The AI ​​health agent is divided into a client system and a cloud service system, embedded in the operation terminal and the AI ​​health cloud service platform, respectively. It also integrates cloud computing capabilities and mobile terminal interaction. The entire system is a highly integrated, multi-dimensional, and multi-faceted whole composed of these four subsystems. Each subsystem and functional module collaborates to provide users with comprehensive, personalized, and interactive health management services and experiences.

[0509] The AI ​​Health Agent, or "AI Health Agent (System)," is a first-level subsystem of the AI ​​Health Management System. The AI ​​Health Agent consists of the AI ​​Health Agent cloud server system and the AI ​​Health Agent client system. This system integrates advanced artificial intelligence technology, cloud computing capabilities, and mobile terminal interaction to provide users with comprehensive, personalized health management services. The AI ​​Health Agent (System) is based on an AI computing server and application backend server, integrating a large AI model trained with professional health knowledge. Equipped with powerful AI computing and data analysis capabilities, it can deeply analyze and rapidly process massive amounts of health data from user terminals, providing users with personalized health assessments, predictions, and personalized health recommendations. Furthermore, the AI ​​Health Agent (System) also offers an "AI Health Digital Human" instant health dialogue mode, offering users a more intuitive and engaging health interaction experience. Through the collaborative work of the cloud server and client, the AI ​​Health Agent system implements a full-process service from health data collection and analysis to health management.

[0510] The collection terminal, or the software system installed in a collection device (such as a smart ring, smart bracelet, or smartwatch), is a first-level subsystem of the AI ​​health management system. The collection terminal works closely with the sensor technologies integrated into the collection device, including optical heart rate sensors (PPG), electrode electrocardiogram (ECG), blood pressure sensors, thermometers, accelerometers, and gyroscopes. By interacting with these sensors and applying specialized algorithms, the collection terminal can drive the sensors in real time to collect user health monitoring data. This data captures key health indicators, including vital signs (such as heart rate, electrocardiogram, blood pressure, blood oxygen saturation, and body temperature), health behavior data (such as sleep patterns and exercise patterns), and health stress data. It also analyzes these key health indicators using specialized algorithms to generate preliminary health monitoring information. Furthermore, the collection terminal utilizes wireless connectivity technologies such as Bluetooth or WiFi to transmit health monitoring data and information to a paired operating terminal device. The term "collection device" can also refer to other home or commercial medical systems or health monitoring systems that provide health data to users, or it can generally refer to any software system that provides user health data.

[0511] The operation terminal, or "operation terminal system," is abbreviated as "operation terminal" or "operation terminal (system)." It is a first-level subsystem of the AI ​​health management system and is a collective term for the main operation terminal (system) and the secondary operation terminal (system). The user downloads and installs the application (APP) of the operation terminal on the user's operation terminal device (such as a smartphone), and the user manages their personal health by registering and logging in. The user can view AI health analysis information and AI health reminder information on the relevant interface of the operation terminal. At the same time, the operation terminal is responsible for obtaining the user's supplementary health information (including basic health record information, medical and physical examination report information, emotional and ability assessment information, etc.), and integrating it with the health monitoring information obtained by the collection terminal to form the user's health information source; on the other hand, the operation terminal integrates the AI ​​health intelligent body client system and is equipped with an AI health digital human functional module. Users can communicate and interact with the AI ​​health digital human in real time using natural language, which is equivalent to answering questions raised by the AI ​​health digital human in the same way as a doctor's consultation, further supplementing the user's health information source, so that the AI ​​health management system can more accurately and comprehensively analyze the user's health status.

[0512] The AI ​​health cloud service platform, or "AI health cloud service platform (system)", is a first-level subsystem of the AI ​​health management system. It is a comprehensive service system that integrates the AI ​​computing server and the application background server into one. The AI ​​health cloud service platform integrates the server-side system of the AI ​​health intelligent body. The AI ​​health intelligent body server and the AI ​​health intelligent body client integrated in the operation terminal work together to perform AI health computing and AI health management tasks, as well as maintain and support the normal operation of the "AI health digital human". The AI ​​health cloud service platform is also the management background of the system user operation permissions, strictly controlling the access and operation permissions of different users to the platform to ensure the security of user data and privacy. The AI ​​health cloud service platform has a complete system software architecture, advanced hardware configuration and high-speed network configuration, with powerful computing, storage and servo capabilities to ensure the ability to serve multiple users at the same time.

[0513] (3) AI Health Management System Secondary Subsystem

[0514] As shown in Figure 02 [System Basic Architecture Diagram - Software System], at the software level, the AI ​​health management system's first-level subsystem includes: AI health agent, data collection terminal, primary operation terminal, secondary operation terminal, and AI health cloud service platform. The primary and secondary operation terminals are collectively referred to as operation terminals. The AI ​​health agent is divided into a client system and a cloud service system. The client system is embedded in the primary and secondary operation terminals, while the cloud service system is organically embedded in the AI ​​health cloud service platform, integrating cloud computing capabilities and mobile terminal interaction functions. The AI ​​health management system's second-level subsystem is a subdivision of the first-level subsystem.

[0515] As shown in Figure 2, the AI ​​Health Agent's secondary subsystem consists of the AI ​​Health Agent client system and the AI ​​Health Agent cloud server system. The client system is responsible for acquiring and preprocessing user information, while the cloud server system performs in-depth analysis and calculations. The two systems collaborate to process data, perform calculations and evaluations, provide health advice, and interact with each other. They also closely cooperate in version control, maintenance support, and other aspects to jointly implement health management functions.

[0516] The AI ​​health agent client system, or "AI health agent (system) client," is both an important secondary subsystem of the AI ​​health agent (system) and a core secondary subsystem of the operation terminal. It is an organic combination of the AI ​​health agent (system) and the operation terminal (system). It is integrated into the operation terminal system and then installed (or implanted) into the operation terminal device, working in conjunction with other management systems of the operation terminal. At the same time, it also works in conjunction with the AI ​​health agent cloud server system, responsible for obtaining and pre-processing the user's health monitoring information and health supplementary information, and transmitting the health assessment results obtained from the cloud server to the user through a window interface or an "AI health digital human" dialogue. The AI ​​health agent client system is also the AI ​​interaction system between the "AI health service system" and the user (including the AI ​​health digital human, AI health dialogue window, etc.), supporting users to conduct AI health consultations. Users can interact with the "AI health digital human" through natural language to obtain professional health advice and answers, as well as health reminders.

[0517] The AI ​​health intelligent body cloud server system, or "AI health intelligent body (system) cloud server", is the core secondary subsystem of the AI ​​health cloud service platform. It carries powerful functions developed based on professional training of AI large models. It uses advanced artificial intelligence algorithms and AI large models to conduct in-depth analysis and calculations on user health monitoring data and health monitoring information obtained from operating terminal devices. By working in collaboration with the AI ​​health intelligent body client system integrated in the operating terminal, it jointly performs AI health calculations and AI health management tasks, providing users with individualized health assessments, personalized health recommendations, and maintenance and support for "AI health digital people". The AI ​​health intelligent body cloud server system continuously learns and optimizes from a large amount of health knowledge to improve its own analysis capabilities and service quality.

[0518] As shown in Figure 2, the health data collection and management system, along with other management systems on the collection terminal, form a complete collection terminal. The health data collection and management system, also known as the "collection terminal health data collection and management system," is a key subsystem of the collection terminal, primarily responsible for collecting and managing various health monitoring data from users. It integrates deeply with the sensor technologies integrated into the collection terminal, including optical heart rate sensors (PPG), electrode electrocardiogram (ECG), blood pressure sensors, thermometers, accelerometers, and gyroscopes. Using specialized algorithms and drivers, these sensors are activated in real time to collect health monitoring data, including vital signs (such as heart rate, electrocardiogram, blood pressure, blood oxygen saturation, and body temperature), health behavior data (such as sleep status and exercise activity), health stress data, and other key health indicators. Preliminary health monitoring information is then analyzed using specialized algorithms.

[0519] The other management system for the collection terminal is a key subsystem of the collection terminal. Together with the health data collection and management system, it forms a complete collection terminal system. The other management system for the collection terminal plays a key role in the collection terminal, primarily responsible for various management tasks beyond health monitoring data collection, including device setup management, wireless connection management, transmission control of health data and information, and power management.

[0520] As shown in Figure 02, in addition to the above-mentioned core secondary subsystem AI health intelligent body client system, the main operation terminal also includes other secondary subsystems, which work together to realize the system functions: the health monitoring information management system is responsible for collecting, organizing and analyzing user health monitoring data and information, providing a basis for subsequent evaluation; the health supplementary information management system supports users to manage personal health supplementary information, integrate it with monitoring information, and improve analysis accuracy; other management systems of the main operation terminal are responsible for ensuring the normal operation of the equipment and functional expansion, covering equipment settings, permission management and other aspects.

[0521] The health monitoring information management system is a secondary subsystem of the main operating terminal. The health monitoring information management system focuses on the collection, organization and analysis of user health monitoring data and health monitoring information. It works closely with the collection terminal to receive real-time health monitoring data transmitted from the collection terminal (such as a smart ring), including health sign data (such as heart rate, electrocardiogram, blood pressure, blood oxygen saturation, body temperature, etc.), health behavior data (such as sleep status, exercise status, etc.), health stress data and other key health indicators, as well as preliminary health monitoring information obtained through professional algorithm analysis, and then uploads these health monitoring data and health monitoring information to the AI ​​health cloud service platform.

[0522] The Supplementary Health Information Management System is a secondary subsystem of the main operating terminal. It provides users with a comprehensive platform for managing their supplementary health information. It allows users to establish basic health profiles, upload important health documents such as medical and physical examination reports, and perform emotional and ability assessments. By integrating this supplementary health information and combining it with health monitoring information, the AI ​​health management system provides a richer and more comprehensive AI health data source, enabling more accurate health analysis and assessment. The system also supports a cluster user mode, allowing authorized users to act as administrators and manage supplementary health information for another user.

[0523] The main operation terminal's other management systems, along with its other secondary subsystems, form a complete main operation terminal. Within the AI ​​health management system, the main operation terminal's other management systems play a crucial role in ensuring the normal operation of the main operation terminal equipment and its functional expansion. These systems encompass multiple aspects, including device configuration management, permissions management, data storage and backup management, and system updates and maintenance.

[0524] As shown in Figure 2, the auxiliary terminal's other management system and the AI ​​health agent client system form a complete auxiliary terminal. The auxiliary terminal's other management system plays an important auxiliary management role within the AI ​​health management system. This system is primarily responsible for managing the specific functions of auxiliary terminal devices and ensuring their operation. This includes audio input and output management, ensuring that devices like microphones and speakers can accurately receive user voice commands and provide clear voice feedback. For different types of auxiliary terminal devices, this system also coordinates their unique functional characteristics, such as intelligent robot movement control and interaction management. Regarding operational assurance, it covers tasks such as device power management and connection management, ensuring stable and efficient operation of the auxiliary terminal devices.

[0525] As shown in Figure 2, the cloud service platform's other management systems and hardware components, excluding the AI ​​Health Agent cloud server system, form the complete AI Health Cloud Service Platform. These management systems encompass hardware devices and a range of management functions to ensure the efficient and effective operation of the AI ​​Health Cloud Service Platform. Hardware components include high-performance AI computing servers, application backend servers, reliable storage devices, and high-speed network connectivity, providing a robust foundation for the stable operation of the entire platform. Management functions include user rights management, data storage and backup, network connectivity management, and system monitoring and maintenance.

[0526] (IV) Deployment of main functional modules of the system

[0527] The first-level subsystem of the AI ​​health management system is an important architectural layer of the AI ​​health management system. The second-level subsystem is a further subdivision of the first-level subsystem, and its functional modules are more focused on specific functional areas. Within the framework of the first-level subsystem, the functional modules of the second-level subsystem refine and deepen related functions. They not only work closely with other second-level subsystem functional modules within the first-level subsystem to which they belong, but also exchange data and coordinate functions with related functional modules in other first-level subsystems, realizing cross-subsystem data flow and functional collaboration, and jointly building a complete and efficient health management system.

[0528] 1. Deployment of main functional modules of AI health intelligent body

[0529] As shown in Figure 03 [Schematic diagram of deployment of main functional modules of the system - AI health agent], the AI ​​health agent (system) is divided into the AI ​​health agent client system and the AI ​​health agent cloud server system.

[0530] (1) As shown in Figure 03, the AI ​​health agent client system includes the following main functional modules:

[0531] AI Audio-Visual Dialogue Window Interaction Module: Serving as the key interface between users and the AI ​​health agent client system, this module integrates natural language processing, speech recognition, and video display technologies. It receives user voice commands and text input, parses and processes them in real time, and accurately conveys user needs to the system. It also presents system feedback to users through a combination of audio and visual means, such as displaying conversation content using the AI ​​health digital human, providing an intuitive interactive experience.

[0532] AI Audio Conversation Interaction Module: This module focuses on audio interaction. It utilizes speech recognition technology to identify user voice commands, converts them into text, and passes them to the system for processing. Furthermore, it utilizes speech synthesis technology to convert system feedback into voice output, enabling voice conversations between the user and the system's AI health digital human. This module also provides convenient user interaction on devices that lack visual capabilities, such as smart speakers.

[0533] It's worth noting that the AI ​​Health Digital Human refers to the virtual human within the "AI Audio-Visual Dialogue Window Interaction Module" and "AI Audio Dialogue Interaction Module" of the operating terminal. This is a special system program within the operating terminal. It primarily communicates with the user regarding the health status of the device's owner (user). The AI ​​Health Digital Human serves as the interface and vehicle for interaction between the AI ​​Health Agent and the user. Presenting itself to the user as a digital, lifelike figure, it communicates with the user through natural language processing technology, answering health questions and providing health information and advice. The AI ​​Health Digital Human translates the complex analysis results and professional advice generated by the AI ​​Health Agent into easily understandable language and formats, delivering them to the user, thereby enabling efficient interaction between the user and the system. The AI ​​Health Agent can be thought of as a behind-the-scenes research team responsible for processing and analyzing large amounts of health data and information, while the AI ​​Health Digital Human serves as the team's front desk, communicating the AI ​​Health Agent's research findings to the user. The AI ​​Health Digital Human's interaction modalities vary depending on the hardware configuration of the operating terminal device (such as whether it has a display and touchscreen input) and the operating system (intelligent or non-intelligent). Generally, when an intelligent operating system has a screen (such as a tablet computer), the AI ​​health digital human in the "AI audio-visual dialogue window interaction module" can interact through text dialogue, voice dialogue, and video dialogue; when a non-intelligent operating system does not have a screen (such as a screenless smart speaker), the AI ​​health digital human in the "AI audio dialogue interaction module" can only conduct voice dialogue.

[0534] AI Health Information Source Acquisition Module (Client): This module is primarily responsible for acquiring user health-related information from the client. It collects supplementary health information entered by users on the operating terminal device, such as self-completed basic health profiles and uploaded medical and physical examination reports. It also receives health monitoring information transmitted by the collection device and integrates this information into the client's health information source, providing data support for subsequent health assessments.

[0535] AI Health Assessment Results Display Module: This module displays the AI ​​health agent's assessment of the user's health status. It presents the user with intuitive information, such as health indexes, health risks, and physiological indicators, through charts and text descriptions. Depending on the type of assessment result (such as AI Health Assessment Results (I) and AI Health Assessment Results (II)), the module accurately displays the corresponding result information to help users understand their health status.

[0536] AI Health Assessment Results (I) Output Module: When the system generates AI Health Assessment Results (I), this module is responsible for accurately outputting the results to the user. It ensures that the results are presented in an appropriate format and manner, such as displaying relevant physiological indicator data and health risk warnings in specific interface areas. This module adheres to relevant standards and specifications during the result output process to ensure accuracy and readability.

[0537] AI Health Supplementary Information Reminder Module: This module primarily provides reminders for users' supplementary health information. It monitors user operations within the health supplementary information management system, such as whether there are any uncompleted health profiles or unuploaded medical and physical examination reports. When such situations are detected, the module promptly reminds users to take appropriate actions to ensure the system captures complete health information and improves the accuracy of health assessments.

[0538] AI Health Assessment Results (II) Output Module: Similar to the AI ​​Health Assessment Results (I) Output Module, this module is responsible for accurately outputting AI Health Assessment Results (II) to the user when the system generates them. Based on more comprehensive health information sources and more accurate assessment results, it presents relevant information in a more detailed and accurate manner, such as a more comprehensive physiological indicator analysis and health risk assessment, helping users better understand changes and details in their health status.

[0539] (2) As shown in Figure 03 [Schematic diagram of system main functional modules deployment - AI health agent], the AI ​​health agent cloud server system includes the following main functional modules:

[0540] AI Health Information Source Acquisition Module (Cloud Server): This module is primarily responsible for acquiring users' health monitoring data and information from operating terminal devices. It utilizes advanced communication technologies and network connections to ensure stable data transmission. By collaborating with acquisition and operating terminals, it collects key health indicators, including vital signs, health behavior data, and health stress data, as well as preliminary health monitoring information, providing the raw data foundation for subsequent analysis and processing.

[0541] Medical and Physical Examination Report AI Parsing Module: This module focuses on parsing user-uploaded medical and physical examination reports. It uses artificial intelligence to analyze and extract key information from various test results (such as blood tests and imaging tests), laboratory reports (such as pathology and microbial cultures), diagnostic reports, and treatment reports. This parsed information is integrated into the user's health information source, providing support for more comprehensive and accurate health assessments.

[0542] AI Health Information Source Processing Module: This module processes acquired health information sources. It uses advanced algorithms and data processing techniques to clean, organize, and standardize health data from various channels. For example, it denoises vital sign data and classifies and quantifies health behavior data to ensure data quality and consistency. It also extracts and selects features from the data to better provide effective input for subsequent AI operations.

[0543] AI Health Agent Preliminary Calculation Module: Based on processed health information sources, this module performs preliminary AI calculations. It utilizes large AI models and related machine learning algorithms to conduct a preliminary assessment and analysis of the user's health status. For example, it analyzes the user's basic vital signs to determine if any abnormalities exist; it also performs pattern recognition on health behavior data to understand the user's lifestyle habits. These preliminary calculation results will serve as the basis for further actuarial calculations, providing a reference for generating more accurate health assessment results (I).

[0544] AI Health Actuary Module: Building on the initial calculation module, this module performs more precise AI calculations. It comprehensively considers additional factors, such as the analysis of the user's medical and physical examination reports, the comprehensiveness and accuracy of health information sources, and more. Through more complex algorithms and models, it provides in-depth assessments and predictions of the user's health status. For example, it can more accurately predict the user's disease risk and more finely customize health management plans, providing a more reliable basis for generating the final health assessment results (II).

[0545] AI Health Assessment (I) Generation Module: When a user does not perform specific actions on supplementary health information (such as establishing a basic health profile or uploading medical and physical examination reports) or does not fully communicate with the AI ​​health digital human, this module generates the AI ​​Health Assessment Result (I) based on existing health information sources and preliminary calculations. It primarily reflects the user's health status assessment based on basic health monitoring data, providing the user with a preliminary health assessment conclusion.

[0546] AI Health Assessment (II) Generation Module: When a user updates supplemental health information (such as establishing a basic health profile or uploading medical and physical examination reports), engages in conversation with the AI ​​Health Digital Human, and provides relevant health information, this module generates the AI ​​Health Assessment (II) based on a more comprehensive health information source and more accurate calculation results. This module provides a more comprehensive and accurate health assessment conclusion, taking into account a variety of health-related factors.

[0547] 2. Deployment of main functional modules of AI health cloud service platform

[0548] As shown in Figure 04 [System Main Functional Module Deployment Diagram - AI Health Cloud Service Platform], the AI ​​Health Cloud Service Platform (system) is divided into the AI ​​Health Agent Cloud Server System and the Cloud Service Platform Other Management Systems. The main functional modules of the AI ​​Health Agent Cloud Server System have been described in the AI ​​Health Agent Functional Modules section (so I won't repeat them here). The Cloud Service Platform Other Management Systems include the following main functional modules.

[0549] System User Management Module (Cloud Server): This module is primarily responsible for managing users of the AI ​​health management system. It covers functions such as user registration, login verification, and permission settings. By verifying user identities and allocating permissions, it ensures appropriate access to and use of platform resources for different users. It also maintains basic user information and account status, ensuring the security and integrity of user data.

[0550] Data Synchronization and Storage Module (Cloud Server): This module focuses on data synchronization and storage. It ensures timely and accurate synchronization of users' health data, assessment results, and other related information across different devices and systems. Using efficient data storage technology, it categorizes, stores, and manages large amounts of health data for easy query, analysis, and use. It also provides data backup and recovery capabilities to prevent data loss and corruption.

[0551] Network Communication and Protocol Service Module: This module is responsible for establishing and maintaining network communications within the AI ​​health management system. It utilizes advanced network communication technologies and protocols to ensure stable connections and data transmission between data collection devices, operating terminals, and the cloud service platform. By managing and optimizing network protocols, it improves data transmission efficiency and reliability, ensuring the normal operation of the system. For example, it handles data transmission across various network connection methods, such as Bluetooth, WiFi, and mobile networks.

[0552] Command Parsing and Execution Service Module: This module primarily parses and executes commands from the operator terminal or other systems. When the operator terminal sends a command to the AI ​​Health Cloud Service Platform, this module analyzes the command to determine its intent and action content. Then, based on the command, it calls the corresponding functional module or performs the corresponding action, such as initiating health assessment calculations or updating user information. This ensures that the system accurately responds to commands from the operator terminal and implements the system's interactive functions.

[0553] AI Health Agent Development Module: This module is used for the development and maintenance of the AI ​​Health Agent. It provides a development environment and tools to support developers in improving and optimizing the AI ​​Health Agent's algorithms and models. By continuously updating and upgrading the AI ​​Health Agent, its health data analysis capabilities and the accuracy of health assessments are enhanced. It also manages the version control and release of the AI ​​Health Agent, ensuring the system uses the latest and most stable version.

[0554] Platform System (Other) Management Module: This module manages other aspects of the AI ​​Health Cloud Service Platform. This includes monitoring and maintaining the platform's hardware, such as server performance monitoring and storage device management. It is also responsible for updating and maintaining the platform's software system to ensure system stability and compatibility. Furthermore, it may involve managing the platform's resource allocation and scheduling to improve operational efficiency and service quality.

[0555] In order to facilitate an overview of the main functional modules of the AI ​​Health Cloud Service Platform, the following table is specially prepared:

[0556]

[0557]

[0558] 3. Deployment of main functional modules of the main operation terminal

[0559] As shown in Figure 5 [System Main Functional Module Deployment Diagram - Main Operation Terminal], the main operation terminal includes not only the aforementioned core secondary subsystem, the AI ​​Health Agent Client System, but also a health monitoring information management system, a health supplementary information management system, and other management systems for the main operation terminal. The main functional modules of the AI ​​Health Agent Client System have been described in the AI ​​Health Agent Functional Module section (so they will not be repeated here).

[0560] (1) As shown in Figure 05, the health monitoring information management system includes the following main functional modules:

[0561] Health Monitoring Data Cleansing Module: This module operates on health monitoring data. It uses specialized algorithms to process key indicators, such as real-time health sign data (such as heart rate) and health behavior data, collected from data collection terminals. By removing noise, processing missing values, and standardizing data, it improves data quality and provides a reliable data foundation for subsequent analysis and processing within the health monitoring information management system.

[0562] Health Monitoring Information Review Module: This module reviews health monitoring information from the perspectives of rationality, consistency, and completeness. It checks whether data from collection devices conforms to medical common sense and physiological laws, ensures that information from different sources matches, verifies that all necessary information has been collected, and categorizes and labels the information to ensure effective management.

[0563] (2) As shown in Figure 05, the health supplement information management system includes the following main functional modules:

[0564] User Health Basic File Management Module: This module manages the basic health files created by users on the main operating terminal. It includes functions for entering, editing, storing, and querying basic user information (such as gender, age, height, weight, etc.), work-related information, lifestyle information, physical condition, and basic medical history. Effective management of this information provides basic data support for subsequent health assessments and management.

[0565] User Medical and Physical Examination Report Management Module: This module focuses on managing medical and physical examination reports uploaded by users. It receives, stores, parses, and queries reports obtained from various examination methods (such as blood tests, urine tests, and X-rays), as well as test reports such as pathology and microbial cultures, as well as electrocardiogram and echocardiogram reports. Through the effective management of these reports, key information is integrated into the user's health information source, providing a basis for more accurate health assessments.

[0566] User Emotion and Ability Assessment Module: This module is used to assess the user's emotions and abilities. It includes a variety of assessment tools, such as intelligence tests, emotion control tests, cognitive ability tests, personality trait tests, memory tests, attention tests, and language ability tests. These assessment tools obtain, analyze, and store data related to the user's emotions and abilities, providing support for the system to understand the user's psychological and cognitive state, so that the user's physical and mental condition can be comprehensively considered in health management. At the same time, the AI ​​health digital human will adopt appropriate language communication methods based on the user's actual situation, such as the appropriate use of professional or popular language, and properly soothe the user's negative emotions or reactions.

[0567] ⑶ As shown in Figure 05, the other management systems of the main operation terminal include the following main functional modules:

[0568] User Health Other Information Management Module: This module manages other user health information. This information may include personal health information provided by users during conversations with the AI ​​health digital human, as well as other health-related information that cannot be categorized within basic health records, medical and physical examination reports, or emotional and ability assessments. This information is collected, organized, and stored to ensure its integrity and availability, providing supplementary data for comprehensive health assessments.

[0569] User Management Module (Client): This module primarily manages users on the main operation terminal. It includes functions such as user registration, login, and permission settings. By verifying user identities and allocating permissions, it ensures appropriate access and use of terminal resources by different users. It also maintains basic user information and account status, ensuring the security and integrity of user data.

[0570] Personal Device Binding Module (Client): This module is used to bind a user's personal device (such as a data acquisition device or secondary operation terminal) to the primary operation terminal. It uses a specific binding mechanism, such as recording device serial number, MAC address, and other information, and establishing an association with the user account to ensure the correct device-user correspondence. After binding, data transmission and interaction can occur between the device and the primary operation terminal, ensuring the normal operation of the AI ​​health management system.

[0571] Data Synchronization and Storage Module (Client): This module is responsible for synchronizing and storing data on the operating terminal. It ensures timely and accurate synchronization of the user's health data, assessment results, and other related information between the main operating terminal and other devices (such as data collection devices and the AI ​​Health Cloud Service Platform). Using appropriate storage technologies, this data is categorized, stored, and managed to facilitate subsequent query, analysis, and use. It also provides data backup and recovery capabilities to prevent data loss and corruption.

[0572] Network Communication and Protocol Terminal Module: This module is responsible for establishing and maintaining network communications between the operating terminal and other devices (such as data collection equipment and the AI ​​Health Cloud Service Platform). It uses advanced network communication technologies and protocols, such as Bluetooth, WiFi, and mobile networks, to ensure stable data transmission. By managing and optimizing network protocols, it improves the efficiency and reliability of data transmission and ensures the normal operation of the system.

[0573] Command Parsing and Execution Terminal Module: This module primarily parses and executes commands from other devices (such as cloud service platforms) or users. Upon receiving a command, it analyzes it to determine its intent and action. Then, based on the command, it calls the corresponding functional module or performs the corresponding action, such as initiating AI health assessment calculations or updating user information. This ensures that the main operation terminal can accurately respond to commands from other devices or users, enabling the system's interactive functionality.

[0574] Group Cluster Management Module (Client): This module manages group cluster users (such as cluster administrators and members) on the primary operation terminal. It includes functions such as cluster user registration, login, and permission settings. By verifying cluster user identities and allocating permissions, it ensures appropriate access and use of operation terminal resources by different cluster users. It also maintains basic cluster user information and account status, ensuring the security and integrity of user data. It also manages health care relationships and health escrow among cluster members.

[0575] Family Cluster Management Module (Client): This module manages family cluster users (such as the creator of the family tree and family members) on the main operation terminal. It covers functions such as cluster user registration, login, and permission setting. By verifying cluster user identities and allocating permissions, it ensures appropriate access and use of main operation terminal resources by different cluster users. It also maintains basic information and account status of cluster users, ensuring the security and integrity of user data. It also manages health care relationships and health escrow among cluster members.

[0576] Cluster Device Binding Module (Client): This module is used to bind cluster devices (such as data acquisition devices and secondary operation terminals) to primary operation terminals. It uses a binding mechanism similar to that of the personal device binding module, recording device serial numbers, MAC addresses, and other information, and establishing associations with cluster user accounts to ensure the correct correspondence between devices and cluster users. After binding, data transmission and interaction can occur between the device and the primary operation terminal, ensur...

Claims

1. An interactive personal health management system based on artificial intelligence, characterized by: The system obtains supplementary health information from users through various types of collection devices or monitoring devices, and through various collection channels and methods. Together with basic health monitoring data obtained through other channels, it builds a dynamically updated AI health information source that is bound to the user's unique identity. The AI ​​health information source is further analyzed and calculated using artificial intelligence health algorithms to generate dynamic and progressive personalized health assessments and recommendations. A preliminary assessment result is generated only when the AI ​​health information source contains only the basic health monitoring data. Subsequently, when the user supplements health information through any means, the system instantly generates a more accurate assessment result based on the more complete AI health information source, and the assessment result is optimized synchronously with the continuous update of the AI ​​health information source. The AI ​​health digital human deployed in this system has an intelligent consultation function: when users raise health consultation questions, if it finds information that is not reflected in the AI ​​health information source but should be supplemented, it will dynamically generate non-fixed format consultation questions to achieve personalized consultation centered on the user's health consultation question; after the user's answer is cleaned, filtered, and standardized, key information is extracted and supplemented to the AI ​​health information source, and the information source is dynamically updated with the question and answer interaction; The system has an intelligent reminder function for supplementary health information: by analyzing the user's health consultation conversation records, it identifies the health issues or potential health issues that the user is concerned about. If it is found to be related to the missing content of the AI ​​health information source, the reminder mechanism will be activated. The reminder content clearly points to the type of information that needs to be supplemented, the supplementary entry point, and the relevance to the health issue; The system has an intelligent reminder function for user health matters: when the system identifies the user's scheduled or established health events from the AI ​​health information source, it will promptly remind the user; the system will continuously monitor and analyze the AI ​​health information source, and if it finds potential signs of acute health risks, it will promptly remind the user to adjust their work and rest schedule or recommend medical treatment; the reminder function will update the reminder content in real time as the AI ​​health information source and the user's health status change, ensuring that the user can respond to risk prevention and health needs in a timely manner.

2. The interactive personal health management system based on artificial intelligence according to claim 1, characterized in that: The system uses a variety of collection devices, channels and methods to obtain more supplementary health information from the same user, including: Basic health file information, medical and physical examination report information, emotion and ability assessment results, and other physical sign data related to medical health completed by users on the system's operating terminal devices; Supplementary health information of users obtained by the AI ​​health digital human deployed in the operating terminal of this system during health consultation or diagnosis with users; Obtain more health monitoring data from users by using multiple collection devices alternately or simultaneously; Users' daily dining records, work and living environment monitoring data, social activity records, and information or data directly or indirectly related to the user's health obtained through special collection or monitoring equipment, algorithms, or other means; Record the user's and their surrounding voice environment with the help of specific collection and monitoring equipment; User medical records and health-related information obtained from the relevant management systems of one or more third-party diagnosis, treatment or physical examination institutions through data interfaces and communication protocols; Furthermore, The basic health file refers to the health information file filled out by the user or his / her health agent on the operating terminal according to the system questionnaire, including the user's basic information, work-related information, eating habits, lifestyle information, bad habits, physical condition, basic medical history, female-specific information, and other information directly or indirectly related to the user's health; The medical and physical examination reports are report documents generated by a regular hospital after a medical examination and assessment of the user's physical condition, and then uploaded to the system by the user or their health agent through an operation terminal. They include blood tests, urine tests, various imaging examination reports, pathological examinations, microbiological cultures, and drug concentration test reports, as well as electrocardiogram reports, echocardiogram reports, endoscopy reports, surgical reports, diagnostic reports, treatment reports, follow-up reports and health assessment reports, medication guidelines, relevant pre- and post-operative notices, review notice documents, and other relevant documents; The emotion and ability assessment is obtained by responding to the user's questionnaire on the operating terminal, including the user's relevant assessment results or conclusions obtained through intelligence tests, emotion control tests, cognitive ability tests, personality trait tests, memory tests, attention tests, and language ability tests; The daily meal records are obtained by the system through special collection equipment, algorithms or other means, including meal time, food type, intake, dining environment information, and other relevant information; The work and living environment monitoring data is the environmental monitoring data of the above-mentioned users obtained by the system through special collection equipment, algorithms or other means, including environmental air pressure, temperature, humidity, light intensity, ultraviolet intensity, noise intensity, air quality information, as well as environmental safety, comfort information, and other relevant information; The recording of the user and the surrounding voice environment is that the system uses acoustic collection equipment to record the user's speech or activity sounds, as well as the user's surrounding environment sound information; Going further, The scope of the supplementary health information is further expanded to include: From the perspective of collection channels, the system expands from using only specific collection channels to collecting all collection channels and methods, without specifying dedicated or specific collection equipment, to collecting supplementary health information directly or indirectly related to the user's health; At the level of collection equipment categories, these include ordinary wearable smart devices that have not been certified as medical devices, wearable health data collection devices that have been certified as medical devices, personal or home medical devices and health testing equipment that have been certified as medical devices, work and living environment monitoring data monitoring equipment that have or have not been professionally certified, supplementary health information that is directly or indirectly related to the health of the above-mentioned users that can be collected by professionally certified or non-professional certified acoustic collection equipment, and other collection equipment related to user health; From the perspective of how the collection equipment is used, this includes the supplementary health information directly or indirectly related to the user's health collected when a single designated type of collection device is used, multiple types of collection devices are used alternately, or multiple types of collection devices are used simultaneously; From the perspective of data information collection methods, it has expanded from collecting health data through collection equipment to directly or indirectly obtaining medical records, conditions and health-related information about the above-mentioned users from the relevant management systems of one or more third-party diagnosis or physical examination institutions through data interfaces and communication protocols.

3. The interactive personal health management system based on artificial intelligence according to claim 2, characterized in that: Through one or more collection devices and multiple channels and methods, the system obtains more comprehensive health monitoring data and supplementary health information of the same user. The system software architecture consists of a collection terminal, an operation terminal, and an AI health cloud service platform. The system develops AI health intelligent bodies and AI health digital humans based on comprehensive training of artificial intelligence technology and health expertise, deploys AI health intelligent body clients and AI health digital humans on the operation terminals, and deploys AI health intelligent body cloud servers on the AI ​​health cloud service platform. The system's operation terminals are divided into primary operation terminals and secondary operation terminals. The health management system also covers a hardware system consisting of data collection equipment, operation terminal equipment, and AI health cloud service platform. The operation terminal equipment is divided into main operation terminal equipment and auxiliary operation terminal equipment. The data collection terminal is installed in the data collection equipment, the main operation terminal is installed in the main operation terminal equipment of user health management, and the auxiliary operation terminal is installed in the auxiliary operation terminal equipment of user health management as an auxiliary tool for user health management. In the health management system, the collection device is connected to the operation terminal device to transmit the user's health monitoring data and information; as well as The operating terminal device can conduct two-way communication with the AI ​​health cloud service platform to achieve the uploading of user health information sources, comprehensive analysis and calculation of AI health intelligent bodies, and the reception and output of AI health assessment results; as well as As part of the AI ​​health agent, the AI ​​health digital human is an intelligent health advisor that interacts with the user through natural language, while the AI ​​health agent provides data and decision support for the AI ​​health digital human; and All components of the system work together to promote the operation of health management; A typical data collection device of the health management system is similar to an ordinary wearable smart device, integrating multiple sensors and technologies, including an optical heart rate sensor, an electrode electrocardiogram sensor, a blood pressure detection sensor, a thermometer, an accelerometer, a gyroscope, and other health-related sensors; and By interacting with these sensors and applying professional algorithms, the collection device can drive the sensors in real time to collect user health monitoring data, thereby obtaining the user's health sign data, health behavior data, and health stress data, and obtain preliminary health monitoring information through professional algorithm analysis; The collection device of the health management system transmits the health monitoring data and information to the paired operation terminal device through a wired or wireless connection, and the operation terminal device then transmits it to the AI ​​health cloud service platform as the basic data of the user's AI health information source; and The health management system supports automatic measurement mode of collection equipment at regular intervals, or users can actively initiate measurement of relevant data at any time; As the user cooperates with the collection equipment to continue working normally, the user's health monitoring data will continue to accumulate and update, and will be recorded in the user's AI health information source in chronological order; In the health management system, health monitoring data and supplementary health information complement each other and together constitute a complete AI health information source for users.

4. The interactive personal health management system based on artificial intelligence according to claim 3, characterized in that: The system sets up a user management system and device binding module, as well as related system operation mechanisms; The system's user management system consists of an operation terminal user management module, a group cluster management module, a family cluster management module, and a system user management module for the AI ​​health cloud service platform; These modules work together to provide users with corresponding operations and functional permissions in different health management scenarios; The system automatically generates a "User Account Unique Identification Code" and a "User Identity ID Number" for each registered user. The two correspond one to one and represent a user. The system can automatically generate a "User Identity QR Code" for each user's "User Account Unique Identity Code" to facilitate interaction between users; A user can simultaneously apply the individual user health management mode, group cluster health management mode, and family cluster health management mode. The use of each mode will be clearly marked in the user application file, and its role in the relevant cluster health management mode will also be clearly marked in the user application file; When a user establishes a health care relationship with another user, their role as the health care provider or the health care recipient in this relationship will also be clearly marked in the user's application profile. Other operations and settings performed by the user in the health management system also follow the same principle; With the help of the system client and cloud server data synchronization storage module, the user's latest application file information is saved; at the same time Through system communication protocols and instructions, the user management modules of the main and secondary operation terminals and the AI ​​health cloud service platform are prompted to synchronously update user application profile information; The device binding module of the health management system is divided into a personal device binding module and a cluster device binding module. The personal device binding module is used to bind the user's own devices, and the cluster device binding module is used for users to bind the devices of their cluster members. When a user registers a user account using a primary operating terminal device, the system will automatically read the "Device Serial Number" and "MAC Address" of the primary operating terminal device, and then generate a "Primary Operating Terminal Device Unique Machine Identification Code". This code will be used as the unique identifier of the user's primary operating terminal device in the system and recorded in the user's device file; and When a user binds a collection device or a secondary operation terminal device, the system will automatically read the "Device Serial Number" and "MAC Address" of the corresponding device, generate a "Unique Machine Identification Code for the Collection Device" or "Unique Machine Identification Code for the Secondary Operation Terminal Device", and use this identification code to represent the user's collection device or secondary operation terminal device in the system and record it in the user's device file; The corresponding device's unique machine identification code is bound one-to-one with the user's "user account unique identification code" to ensure that the primary operation terminal device is used by the associated user, that the health monitoring data and information obtained by the collection device are attributed to the associated user's health information source, and that the user of the secondary operation terminal device is the associated user; The system can automatically generate a "Device QR Code" for each device's unique machine identification code, making it easier for users to perform on-site or remote binding operations. A user can use the system's built-in scanning tool or a third-party scanning tool to scan the "Device QR Code" of a collection device to achieve binding, or can search and select to perform binding through Bluetooth device pairing. A user's account is unique and can only be logged in and used on one main operating terminal device; and When a user changes the primary operation terminal device and logs in on the new device, the user account on the original primary operation terminal device will automatically log out; at the same time A user can enable multiple collection devices, which can be used interchangeably or simultaneously to collect more health monitoring data for the user; also A user can also activate two or more secondary operation terminal devices; they can also use different secondary operation terminal devices in different time periods to conduct health consultations or other applications with the AI ​​health digital human, or use two or more secondary operation terminal devices in the same time period to interact with the AI ​​health digital human at the same time; With the help of the system client and cloud server data synchronization storage module, the latest user device profile information is saved; at the same time Through system communication protocols and instructions, the device management modules of the main operation terminal and the secondary operation terminal and the AI ​​health cloud service platform can synchronously update the user device file information to ensure the configuration and data consistency and integrity of the entire system, and the system operates or manages according to the updated configuration conditions.

5. The interactive personal health management system based on artificial intelligence according to claim 4, characterized in that: In this system, the operation terminal serves as an application, and the collection device and the operation terminal device not only serve as collection or operation tools, but also serve as network data transmission tools. Once the user's health monitoring data is successfully transmitted to the AI ​​health cloud service platform, the user's profile and health information are locally disconnected from the operation terminal and collection device. Data collection devices typically temporarily store health monitoring information for a certain period of time, and the temporary storage duration varies from device to device. When the system detects that a user's health monitoring data and information are missing, the AI ​​health digital human will promptly guide the user to investigate the cause and remind them to upload the data as soon as possible to reduce the risk of data loss. When the operating terminal is connected to the network and in working state, health monitoring information and health supplement information will be quickly and synchronously transmitted to the AI ​​health cloud service platform, which provides stable and reliable protection for data storage, backup, system security and maintenance; Taking into account the special needs of some users or policy restrictions in certain regions, health data may be restricted from leaving the country or being transmitted to the AI ​​health cloud service platform. In this case, the health management system can deploy the AI ​​health intelligent body cloud service system to the operation terminal after optimized configuration.

6. The interactive personal health management system based on artificial intelligence according to claim 5, characterized in that: The AI ​​health digital person's intelligent consultation has a scientific strategy mechanism. Based on the user's comprehensive AI health information source, it uses a fusion analysis algorithm based on an artificial intelligence deep learning framework to extract features and perform correlation analysis through a neural network model to determine the consultation strategy. The consultation is centered on the user's health consultation questions and is conducted in the order from important factors to secondary factors, and from direct factors to indirect factors related to the user's health problems; at the same time Update the user's health profile based on their answers, adjust the consultation process and dialogue in real time, achieve personalized consultation, and ensure smooth acquisition of key health supplementary information; The intelligent medical consultation has a self-learning optimization mechanism: based on multi-source data such as medical consultation records, health assessment feedback, and changes in health status, it uses recurrent neural networks and long short-term memory network deep learning algorithms to learn user response patterns, optimizes medical consultation strategies through reinforcement learning, discovers new risk patterns and group characteristics with the help of unsupervised learning, and updates the knowledge base and strategies in real time.

7. The interactive personal health management system based on artificial intelligence according to claim 6, characterized in that: The reminder content of the health supplementary information intelligent reminder function mainly focuses on the missing health information in the health supplementary information management system of the operating terminal, including the user's incomplete filling of relevant important information in the system's basic health file management module, the user's incomplete relevant important assessment in the system's emotion and ability assessment module, the user's incomplete relevant important medical or physical examination reports in the system's medical and physical examination report management module, and other supplementary information related to the user's health; The intelligent reminder function is implemented by: notifying the user through the AI ​​health digital human in its dialogue window, popping up a reminder window or reminder label on the relevant display interface of the operation terminal, sending SMS or push notification to the user's registered contact information, or other notification methods or forms; The intelligent reminder function can be optimized based on user usage habits and behavior patterns. If the system finds that users are usually more likely to respond to reminders and supplement health information in specific time periods or specific scenarios, the system will prioritize sending reminders at the corresponding time or scenario to improve the efficiency and enthusiasm of users to supplement health information.

8. The interactive personal health management system based on artificial intelligence according to claim 7, characterized in that: The reminder methods of the intelligent reminder function for health matters include: using the AI ​​health digital person to remind users of relevant health matters in its dialogue window. This notification is proactive and not only performed when the user seeks health consultation; the system reminds users of health matters by displaying a reminder label in a pop-up reminder window on the relevant interface of the operating terminal; other notification methods or forms.

9. The interactive personal health management system based on artificial intelligence according to claim 8, characterized in that: The AI ​​health digital human deployed by the system can automatically adjust the communication method and use professional or popular methods to communicate and dialogue; For users with a professional background, the AI ​​health digital human will adopt a relatively professional communication method, using professional terminology and in-depth medical knowledge to discuss health issues more efficiently and provide professional advice; The user's background information can be obtained from the user's basic health records. If the file information shows that the user has a medical-related education background or works in the medical industry, the system will mark the user as having a professional background; If a user frequently uses professional terms or demonstrates a high level of familiarity with professional medical knowledge, the AI ​​health digital human will also determine that the user has a certain professional background; For ordinary users without professional background, AI health digital people switch to popular ways of communication, using easy-to-understand language and avoiding complex professional terms.

10. The interactive personal health management system based on artificial intelligence according to claim 9, characterized in that: The system or AI health digital human can remind users of abnormal usage of their data collection equipment or operating terminal devices and provide guidance and suggestions; When a user's health monitoring data or information is missing or abnormal for a long period of time, or when interruption, delay or loss of data transmission between the collection device and the operation terminal is detected, the system or AI health digital human will remind the user and provide reference troubleshooting steps based on possible causes; When the health monitoring data collected by the sensors of the collection equipment shows abnormal fluctuations or significantly deviates from the user's normal range, the system or AI health digital human will issue a timely reminder and provide targeted troubleshooting suggestions based on the characteristics of different types of sensors and common problems; If the user is confirmed that the data abnormality is not caused by a device problem, it will become a user health issue. The system or AI health digital person will remind the user to pay attention and recommend that they consult a professional doctor or seek medical treatment in time.

11. The interactive personal health management system based on artificial intelligence according to claim 10, characterized in that: The AI ​​health digital human deployed in its system can serve as the customer service of this system; When users have questions about the use of this system and related equipment, the AI ​​health digital human can provide users with accurate instructions and professional guidance in a timely manner; Whether it is the functional operation process of the operating terminal, or the correct wearing method and daily maintenance points of the collection equipment, the AI ​​health digital human can answer them in a clear and easy-to-understand manner.

12. The interactive personal health management system based on artificial intelligence according to claim 11, characterized in that: The system and its deployed AI health agent have optimization mechanisms and adaptive learning capabilities; The AI ​​health agent is trained on a large AI model and has the ability to acquire and learn health knowledge. By connecting to professional medical databases, it continuously scans and collects medical and health knowledge, health research results, and disease treatment methods nationwide and globally. Furthermore, developers can use the system's AI health agent development module to debug and optimize the AI ​​health agent based on the system's continuously accumulated user health data and user feedback information; Furthermore, the learning content of the AI ​​health agent covers multiple aspects; From the perspective of health data, learn the relationship between different health indicators; From the perspective of user behavior patterns, learn about users' acceptance and implementation of health management recommendations; From the perspective of user feedback, adjust the assessment methods and management strategies based on user satisfaction with health assessment results and improvement suggestions; In addition, the AI ​​health agent has the ability to self-adjust and can automatically adjust its algorithm and model parameters to better adapt to the health status and needs of different users, thereby improving the accuracy of health assessment and management.

13. The interactive personal health management system based on artificial intelligence according to claim 12, characterized in that: The system supports the application mode of health care between users; In this application model, when a user pays attention to the health of another user, the user who initiates the attention behavior is defined as the "follower", and the user being followed is the "followed object"; The object of interest can choose to use the primary operation terminal device or the secondary operation terminal device; The follower needs to use the main operation terminal device to follow others, or their health custodian can perform the following operation on their behalf; When a user obtains the permission to follow the health of another user, the user can consult the AI ​​health digital human on his / her operation terminal about the health status of the user being followed and the working status of the data collection equipment; and A user can follow multiple followers at the same time; When a user submits a health care application to another user and obtains the consent of the other party or the other party's health custodian, the health management system will determine that the two users have reached a health care agreement; at this time The system will establish a health care relationship profile for the two users in the system user management module of the AI ​​health cloud service platform, and will synchronously update the relevant settings and permissions of the two users' operating terminals through communication protocols and instructions to ensure the normal operation of various functions in the health care relationship between the two parties; and The AI ​​Health Digital Human dialogue window in the terminal device operated by the follower will automatically add new followers, allowing the follower to quickly switch followers. When the follower switches followers, the AI ​​Health Digital Human will automatically cooperate and target the newly switched followers in the subsequent health consultation, including their health information and problems, as well as the working status of the collection equipment. Due to system authority restrictions, the supplementary health information about the person being followed provided by the follower to the AI ​​health digital human is considered invalid information. The AI ​​health intelligent body will ignore this information and will not collect it into the health information source of the person being followed, thereby ensuring the uniformity and traceability of the user's health information source.

14. The interactive personal health management system based on artificial intelligence according to claim 13, characterized in that: The system supports a single user to use the primary operation terminal device and the secondary operation terminal device in a joint application mode at the same time; When a user has both a primary operation terminal device and a secondary operation terminal device, both can be bound to the collection device to obtain the user's health monitoring data and information, and the user can switch between the two modes at will; and The user health monitoring data and information obtained by the primary operation terminal device and the secondary operation terminal device are essentially the same and both belong to the user's health data. The user can flexibly switch between different operation terminal devices to use the health management function; Through the main operating terminal device, users can view the health assessment results presented by the system in detail, perform systematic and patterned health supplementary information operations, and conduct health consultations with the AI ​​health digital person in a relatively intuitive way; Due to its flexibility and uniqueness, the secondary operation terminal device can serve as an auxiliary device for the main operation terminal device. Without opening the main operation terminal device, users can directly wake up the AI ​​health digital human in the secondary operation terminal through voice wake-up, so as to quickly conduct health consultations or handle other issues. The joint application mode of primary and secondary operation terminal devices is suitable for users to conveniently perform health management interactions in different scenarios.

15. The interactive personal health management system based on artificial intelligence according to claim 14, characterized in that: The system supports two users to use the main operation terminal device and the secondary operation terminal device respectively, and realizes the joint application mode of health hosting between the two users; In this mode, one user uses a primary operation terminal device to enable cluster user mode and becomes a cluster administrator or family member to exercise health hosting rights and become the health custodian. Another user, as a cluster member or family member, uses a secondary operation terminal device to become the health hosting object, and the two parties establish a "health hosting" relationship. A user with escrow authority exercises health escrow authority in the relevant management module of their operating terminal and registers an escrow user account for another user. At this time, the system will establish a health escrow relationship file for the two users in the user management module of the AI ​​Health Cloud Service Platform system, and synchronously update the relevant settings and permissions of the two users' operating terminals through communication protocols and instructions to ensure the normal operation of various functions in the health escrow relationship between the two parties; In this mode, cluster administrators manage the health of managed objects. Specifically, they can perform relevant management operations on managed objects on their operation terminals, including: Registering user accounts for managed objects; and Provide supplementary health information management for the managed individuals: establish their basic health profiles, upload their medical and physical examination reports, and conduct emotional and ability assessments; and Bind the collection device and the secondary operation terminal device to the managed object; In this mode, the cluster administrator, acting as the custodian, can use the device binding module of the primary operation terminal to bind the collection device of the managed object and the secondary operation terminal device. By recording the device serial number, MAC address, and other information, the correct association between the device and the managed object is established, ensuring that the device and the managed object are accurately matched, thereby correctly obtaining the health data of the managed object. This application mode also supports remote operations, including: remote registration of user accounts of managed objects; remote binding of their collection devices and secondary operation terminal devices; remote execution of health management operations; remote monitoring of their health status; Health management users can turn on or voice-activate the AI ​​health digital person on their secondary operation terminal, consult with the AI ​​health digital person about their health issues, receive health reminders from the AI ​​health digital person, or answer questions from the AI ​​health digital person to supplement their health information. If the health trustee user wishes to follow other users, the health trustee can apply to the other party on his / her own primary operation terminal device. Once the other party agrees, the health trustee user will obtain the right to follow other users' health; and The health custodian user can consult the AI ​​health digital person on his / her main operation terminal about the health status of the custodian and the use of related equipment; and When a health custodian user exercises the right to pay attention to the health of the custodian object, he or she can actively provide supplementary health information of the custodian object when consulting the AI ​​health digital person on the custodian object's health status; The health custodian user can manage multiple custodians at the same time. The list of custodians will be automatically listed in the dialogue window with the AI ​​health digital person, so that the custodian user can choose to switch between different custodians at any time. If the health custodian user wishes to transfer the health custodian authority of the custodian object to another user, he or she can apply to transfer this authority to the other user through the relevant permission operation. If the other user agrees to accept, a new health custodian relationship will be established between the other user and the custodian object, and the custodian relationship between the original health custodian and the original custodian object will be automatically terminated; When a health hosting relationship between users is established, transferred or terminated, the system will conduct corresponding health hosting relationship file management in the system user management module of the AI ​​Health Cloud Service Platform according to the authority agreement reached between the two relevant users, and will synchronously update the settings and permissions of the relevant user operation terminals through communication protocols and instructions to ensure the normal and orderly operation of various system health hosting functions.

16. The interactive personal health management system based on artificial intelligence according to claim 15, characterized in that: The system supports multi-user group cluster health management mode and mechanism; After an individual user registers an account, when the user initiates an application to join a group cluster in the relevant management module of the main operation terminal, or upgrades to a group cluster administrator through relevant functional operations, the user operation terminal system will automatically load the functional modules or permissions related to group cluster health management; A cluster is the basic unit of a group-based cluster structure created by individual users. The healthy managed objects of the cluster administrator automatically become members of the cluster, and the cluster can also accept more individual users to join as members. Cluster members are directly under the administrator and are managed by him; Cluster administrators can merge other clusters and their subclusters. The direct members of the merged cluster will be directly under the administrator, and its subclusters will also be transferred to become subclusters of the merged cluster. Cluster members have the right to create one-level sub-clusters and manage their members; In this mode, cluster members can create first-level subclusters and manage their members. Subclusters and clusters have a hierarchical relationship. The upper-level cluster can contain first-level subclusters, and the first-level subclusters can contain second-level subclusters, and so on. Subcluster members are only direct members of their subcluster, and health management permissions can only be exercised by the administrator of the subcluster; When an individual user creates a new group cluster, they automatically become the cluster administrator of the cluster; The cluster administrator has the right to manage the health of other secondary operation terminal users, and the user automatically becomes a member of this cluster; According to the system mechanism, cluster administrators automatically have the authority to monitor the health of direct members of the cluster and all subordinate sub-clusters without the consent of the monitored members; A cluster is a basic unit system. It has direct cluster members, including cluster administrators, first-level sub-group administrators, and cluster member roles. The cluster is the basic unit, and its direct members include cluster administrators, first-level sub-group administrators, and cluster member roles; and A first-level subcluster is a branch of a cluster. A first-level cluster also has its own direct cluster members, including first-level subcluster administrators, second-level subcluster administrators, and first-level subcluster member roles. and The first-level subcluster administrator is a direct member of the cluster and also a direct member of the second-level subcluster it creates. The mechanism is similar for lower-level branches. A cluster administrator can apply to "join" with another cluster to become a first-level joint cluster. The cluster administrator who agrees to the joint becomes the first-level joint cluster administrator. He can then apply to join with other first-level joint clusters to become a second-level joint cluster, and so on. A federated cluster is an associative framework, and a federated cluster has no directly affiliated cluster members; Within the top-level federated cluster, all federated clusters, clusters, and subclusters are not allowed to be federated with each other again; and The federated clusters and clusters within the top-level federated cluster can be federated with other top-level federated clusters or independent clusters. After the federation, the other clusters become part of the top-level federated cluster. In this mode, all peer clusters within a top-level federated cluster can be merged, and the merged cluster administrators and cluster members become members of the newly merged cluster. and All different-level clusters within a top-level federated cluster can be merged, with lower-level sub-cluster administrators and cluster members becoming members of the upper-level cluster; and All clusters at the same level in a tree-type system can merge external independent clusters, that is, there is no joint state; and After the merger, the external cluster administrator and cluster members become members of the newly merged cluster, and the subclusters of the external independent cluster are also transferred; In this mode, all lower-level cluster members within the top-level joint cluster can view and apply for health attention to any cluster member within the top-level joint cluster system; and The member can apply by entering the other party's user ID number to search or scanning the other party's user ID QR code; In this mode, the cluster administrator is a management responsibility identity. The administrator is actually an individual user, and the administrator is only the management authority identity. An individual user can have multiple cluster management responsibilities, and these responsibilities do not conflict with each other; As an individual user, the cluster administrator can conduct health care activities with other users as an individual, join other clusters or subclusters, and create more clusters or subclusters; and As a cluster administrator, exercise relevant cluster management rights, including consenting to others joining the cluster, inviting others to join the cluster, health management, cluster federation, cluster merger, transfer of cluster administrator rights, and disbanding cluster management rights and responsibilities; In this mode, the system agreements reached between cluster administrators and cluster members through relevant management authority operations will be filed and executed by the AI ​​Health Cloud Service Platform system to ensure the accuracy and effectiveness of the authority settings.

17. The interactive personal health management system based on artificial intelligence according to claim 15, characterized in that: The system supports multi-user family cluster health management mode and mechanism; After an individual user registers an account, when the user initiates an application to join a family cluster in the relevant management module of the main operation terminal, or upgrades to a family tree creator through relevant functions, the user operation terminal system will automatically load the functional modules or permissions related to family cluster health management; The family-based cluster health management model refers to the construction and application of the group-based cluster health management model, combining the group nature, structure, and family health management scenario requirements of the family, as well as the characteristics of family marriage similar to cluster union, to comprehensively design a multi-user health management model; In the family cluster model, the system's operation and interaction methods take into account the family's behavioral habits and communication methods; Family members can easily create family clusters, invite others to join the family, and remove others from the family; The family-type cluster health management model is a health management model based on family members. The family is similar to a cluster, the family is similar to a sub-cluster, and the family marriage is similar to a joint cluster. The family cluster includes two roles: the creator of the family tree and the family members. The creator of the family tree is only responsible for the management of the family tree; Any family member or other individual user can pay attention to each other's health; and Families are defined within the family according to specific rules, and each family member automatically has the right to be the custodian of the health of other family members; In this model, the family cluster health management model has made specific definitions of relevant members, roles, and units. The creator of the family tree refers to the family member who first creates the family tree. He is responsible for the maintenance and modification of the family tree and can also assign management authority to other family members for joint management. A family lineage person is a person whose position in the family is determined by blood relationship. Each family lineage person carries the family gene and is a link in the family reproduction chain; and Family members are all members of a family, including all family members within its lineage; and In the AI ​​health management system, "family" refers to a relative unit in a family or family tree, consisting of parents, children, and their spouses; and The highest level of the family tree in the AI ​​health management system is called the "branch ancestor", and the family they form is the "family branch ancestor family", which is also the first-level family in the family; The family cluster presents a tree-like branching structure. The "family ancestral family" is called the first-level sub-family, the second-level sub-family, and so on according to the lineage. A family's lineage members and their spouses, children and their spouses are members of the family. A family member is also a member of the paternal family and a member of the maternal family. If married, he or she also becomes a member of the spousal family. In the family cluster health management model, the concept of family as a unit is not completely fixed, and in actual situations it presents the characteristics of relativity and overlap. In the family cluster model of the AI ​​health management system, it strictly adheres to the family structure principle of the father-son or mother-son relationship based on blood relationship. Regardless of male or female members, even after the woman gets married, under the family cluster model of this system, her family relationship still continues in the original family tree. If the above-mentioned user also has adoptive parents with whom he or she has a legal relationship, this situation is also suitable for the family cluster model of the AI ​​health management system. In this case, the user can add adoptive father-family and adoptive mother-family accordingly, and their management mechanism is consistent with that of the biological parents' family. Any family member who creates a family tree for the first time becomes the creator and administrator of the family tree, responsible for its maintenance and modification. They can also assign permissions to family members to share management responsibilities, and the management scope can be divided into the entire family or some branches; and When creating a family tree, you need to indicate the name, birthday and gender of the family members, and the system will automatically generate family relationships and titles; Every time a new family member is added to the family tree, the AI ​​health management system automatically generates a "user account unique identification code" and "user identity ID number" for it. At the same time, it automatically marks the position of each family member in the family and family tree, and clarifies the relative relationship between the family member and other family members or family members. In this mode, the AI ​​health management system automatically grants family members the right to follow each other's health. The follower or the person being followed can also choose to cancel the follow-up based on the actual situation. Following other family members requires the consent of the family member or the custodian. In this mode, the AI ​​health management system assumes that family members have the right to manage the health of other family members; and For example, children's health custody relationship with their parents can be terminated and transferred to siblings; and To act as a custodian for other family members, one must obtain their consent or be authorized by a family member who has the authority to act as a custodian; In this mode, the creator and administrator of the family tree only bear the responsibility of family tree management. The administrator is actually an individual user, and the administrator is only the identity of the authority to manage the family tree. As an individual user, any family member can not only participate in the family cluster health management, but also conduct health care activities with other individual users who are not family members; In this mode, the agreements reached between the genealogy creator, administrator, family members, and family members through management authority operations are filed and executed by the AI ​​Health Cloud Service Platform system to ensure the accuracy and effectiveness of the authority settings.

18. An interactive personal health management system based on artificial intelligence according to claim 16 or 17, characterized in that: The system supports users to use individual health management mode, group cluster health management mode, and family cluster health management mode at the same time; The three modes operate independently and are not mutually exclusive. Users can freely combine them according to actual needs.

19. The interactive personal health management system based on artificial intelligence according to claim 18, characterized in that: The system is compatible with various types of devices as secondary operation terminal devices, including smart robots, smart speakers, digital photo frames, and other smart hardware devices; These devices can be specially developed based on the system's communication protocol, or they can be common smart devices on the market that the system is actively compatible with; Users can purchase a new auxiliary operation terminal device that matches the system, or use the existing compatible device resources. After installing the operation terminal program of this system, they can use the relevant functions and services; In special cases, the secondary operation terminal device can also integrate some functional modules of the collection terminal. In this case, the device has both the operation terminal and the collection terminal functions. The user health monitoring data or environmental monitoring information collected by it can be directly transmitted to the AI ​​health cloud service platform; and Users can collect data directly through the device and conduct health consultations with AI health digital people.

20. The interactive personal health management system based on artificial intelligence according to claim 19, characterized in that: The system includes a host computer and a narrow "AI health management system without a host computer" model at the development compatibility level, where the host computer is used to handle communication protocol adaptation and control instruction interaction with the acquisition equipment and auxiliary operation terminal equipment; These two modes constitute the unique form of the system in terms of development compatibility; The "AI health management system without a host computer" is in the form of HTML5 as a pure software client. The HTML5 application can be nested and integrated with the APP of ordinary wearable smart devices. Through the software development kit SDK or application programming interface API, communication protocol adaptation and control are achieved, so that the APP of ordinary wearable smart devices has some core functions of the AI ​​health management system, including preliminary analysis and display of health data, some health consulting services based on natural language processing, and health reminder functions.

21. The interactive personal health management system based on artificial intelligence according to claim 20, characterized in that: The health management system supports the export of AI health information sources and AI health assessment results in a standard format, and supports users to provide them to third-party medical or health institutions that provide diagnosis and treatment services for data sharing and reference.

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