30 results about "Self-management" patented technology

The invention relates to a digital chronic disease intelligent management platform based on an AI model and multi-dimensional data fusion, clinical diagnosis and treatment data, wearable equipment monitoring data, medication record data and environment monitoring data are acquired through a data acquisition module, and after standardized preprocessing is performed through a data fusion processing module, deep analysis is performed through an AI analysis module, and the data fusion processing module performs data fusion processing; in combination with medical knowledge of the knowledge base module, the intelligent decision-making module generates a personalized management scheme, and the personalized management scheme is implemented through the intervention execution module and the intelligent interaction module. Multi-dimensional health data are processed through an AI large model, a complex mode and an association relationship are automatically learned, and accurate disease prediction and risk assessment are realized; the pertinence of the scheme and the compliance of a patient are greatly improved; and real-time interaction and personalized guidance are provided, the participation degree and the self-management ability of the patient are effectively enhanced, and a benign health management cycle is formed.

The invention relates to the field of health management, and discloses a health propaganda and education method and system based on an intelligent system, and the method comprises the steps: collecting environment data and multi-dimensional health data of a service object, and creating a scene perception layer; generating a causal relationship chain by using a preset Bayesian network optimization algorithm; determining an adaptive propaganda and education opportunity of the service object based on the health state and the scene type; constructing a self-management scoring device of the service object, and generating personalized propaganda and education contents of the service object; and creating a multi-scene collaborative propaganda and education platform of the service object and a corresponding effect evaluation system thereof. According to the invention, multi-scene collaborative propaganda and education can be realized, and targeted health propaganda and education contents are provided.

The invention relates to the technical field of medical care informatization, in particular to a diabetes and early-stage patient self-management system based on AI and CGM, which comprises the following steps: carrying out delayed alignment on CGM blood glucose flow and multi-modal physiological signals through a signal acquisition and synchronization module; an intelligent event triggering module is used for dynamically adjusting a triggering threshold value for different disease course stages so as to identify suspected events; a mixed consistency verification engine is combined with a medical prior rule and an ensemble classification model to carry out joint discrimination, and a consistency probability is output; and the individual parameters are optimized in cooperation with a hierarchical decision-making mechanism of the interaction module and online incremental learning of the adaptive learning module. Therefore, frequent false alarms caused by physical limitations such as inherent physiological delay and signal drifting of a CGM sensor and alarm fatigue and decline of compliance of a patient caused by the frequent false alarms are solved, and more stable and personalized dynamic blood glucose management support is provided for active people in the early stage of diabetes mellitus and the like.

The invention discloses a 3D holographic image knowledge base interactive playing method for diabetic education, and the method comprises the steps: firstly obtaining patient data of a hospital HIS system and a wearable device, and carrying out the classification, preprocessing and data fusion, thereby obtaining an encrypted standardized data set; and then multi-dimensional feature groups are extracted, knowledge base education contents are matched, and a personalized scheme is generated. And carrying out structural processing on the scheme, constructing a 3D model and a scene resource library, rendering the scheme into a holographic image format, optimizing the scheme, and outputting a material set. And then configuring a calibration material set, scheduling and playing, realizing immersive learning, and generating interaction data and feedback information. And finally, evaluating a learning effect by combining clinical indexes, and dynamically updating and optimizing the knowledge base. By means of 3D holography and multi-mode interaction, intuition, interactivity and individuation of diabetic education are improved, and the self-management ability is enhanced.

The invention relates to the technical field of old-age service management, in particular to an integrated old-age service management system based on end-cloud collaboration, and aims to solve the problems that in the prior art, multi-source data of physical signs, medication and living habits cannot be integrated to predict the health change trend, abnormal fluctuation of four high cannot be perceived in advance, short-term risks cannot be pre-judged, and the service quality is poor. And a personalized intervention scheme cannot be formulated in combination with reinforcement learning. Multi-source data of physical signs, medication and living habits are integrated through the chronic disease management module, LSTM is used for predicting the health change trend, four-high abnormal fluctuation is perceived in advance, short-term risks are pre-judged, a personalized intervention scheme is formulated in combination with reinforcement learning, diet and exercise suggestions and medication reminding are covered, and the system has the advantages of being simple in structure and convenient to use. User compliance is improved by outputting custom prompts through natural languages, the system forms a complete closed loop, the system is suitable for long-term management of diabetes and hypertension diseases, the risk of acute events is reduced, and the self-management ability of patients is enhanced.

The invention provides a dynamic response type cloud platform for chronic disease self-management, and relates to the technical field of artificial intelligence. Comprising the steps that physiological data of a user are collected in real time through a data collection module, health management suggestions are generated through a data analysis module, medical literatures and user feedback are integrated in combination with a knowledge graph module, and a personalized health management suggestion report is provided. The user interaction module realizes real-time data display and feedback, the feedback analysis module generates a user portrait, the data packaging module generates packaged data, and the large model reasoning module quickly updates a health management suggestion report. And the cloud infrastructure module ensures data storage security and access control, so that the health management experience of the user is improved.

The present invention, on the basis of a digital tombstone provided in a cemetery, provides information and content of the deceased through display, input, power source, detection and communication functions and enables visitor recognition, audio and video output, energy self-sustained operation and remote management. In addition, artificial intelligence is used to automatically generate and supplement family trees, genealogies and photo / video-based family relationship data, and memory-associated spaces and an avatar reflecting family characteristics are provided in a metaverse space so that a digital family legacy is continuously managed. Furthermore, emotion inputs and interactions are analyzed using an ethical judgement model so as to refine and block risky expressions, and a safe and reliable digital posthumous digital persona curation service can be provided.

The system according to this embodiment aims to provide individual learners with an optimal learning plan based on their MBTI diagnostic results, thereby supporting their learning motivation and self-management. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a reminder unit, a monitoring unit, and a feedback unit. The reception unit receives MBTI diagnostic results. The analysis unit analyzes the MBTI diagnostic results received by the reception unit and provides a personalized learning plan. The reminder unit delivers periodic reminders based on the learning plan provided by the analysis unit. The monitoring unit monitors learning progress based on the reminders delivered by the reminder unit. The feedback unit provides feedback based on the learning progress monitored by the monitoring unit.

This invention provides a comprehensive platform that integrates 3D virtual tours with a multi-agent Al-driven system for enhanced user engagement, scalability, and autonomous management. The platform includes a 3D Virtual Tour Generator that captures and renders physical spaces into interactive, high-resolution 3D environments. A network of specialized Al agents - including a Platform Guide Agent, Customer-Specific Agents, Visitor Assistant Agents, Compliance Agents, and Engagement and Marketing Agents - drives the platform's interactive capabilities. These agents are powered by distributed knowledge bases, enabling them to provide context-aware assistance, real-time responses, and proactive user guidance. The platform supports self-management, allowing businesses to create and control their digital presence, including configuring Customer-Specific Agents. Additionally, agent-to-agent interactions facilitate continuous learning, platform maintenance, and improvement. The invention is designed for various applications, including real estate, education, tourism, and retail, offering a human-centric, scalable solution that enhances virtual tours and provides autonomous, high-quality information 24 / 7, reducing reliance on human guides.

A multi-tenant system stores tenant data for a tenant in a data space. The system receives a request from a user to generate an isolated workspace according to the tenant data stored in the data space. The user has non-administrative access control with respect to the data space of the multi-tenant system. The system, in accordance with receiving the request, provisions an isolated organization to the user, including establishing administrative access control for the user with respect to the isolated organization while maintaining the non-administrative access control for the user with respect to the data space. The computer system replicates at least a subset of metadata from the data space to the isolated organization. The subset of metadata is accessible to the user in the data space and describes a corresponding subset of data. The system stores, in the isolated organization, all components created in the isolated organization.

The application discloses an unattended system and a running method of a sulfur-burning sulfuric acid plant, which comprises five subsystems, namely, an intelligent acquisition system, an intelligent identification system, an intelligent evaluation system, an intelligent decision system and an intelligent execution system. By further integrating various technologies such as equipment, operation, process, automation and information, the self-automation capability of links such as acquisition, identification, evaluation, decision and execution is established, the system can autonomously respond to the requirements and environmental changes of external quality, quantity and time dimensions, unreasonable operation and market fluctuations, and can also self-manage the integrity and stability of the system, so that the plant can reduce human intervention to the maximum extent and transition to unmanned.

The invention relates to a self-management low-power-consumption sleep and wake-up exception handling method based on an Android system, and the method comprises the following steps: a kernel power management module is created in a kernel layer, the kernel power management module is used for detecting the power state of the Android system and starting a power management service program, and the power state comprises a sleep state, a wake-up signal and a heartbeat signal; the kernel power management module detects dormancy and wake-up signals through a GPIO (General Purpose Input / Output) state; when the ACC signal is turned to off, the power management service program starts the system to sleep; when the ACC signal becomes on, the power management service program wakes up the system and recovers heartbeat detection; and if the heartbeat signal is not received within the specified time, the power management service program actively restarts the Android system. According to the method, the problem of dependence of the MCU in the prior art is solved, and meanwhile, the flexibility and reliability of the system are improved.

The invention provides an internet-based lymphedema patient self-management support system and method, and belongs to the technical field of data processing, and the method specifically comprises the steps: determining a distribution processing scheme of medical staff when a patient sees a doctor based on the coincidence data and doctor seeing data of each patient in an online doctor seeing period, and carrying out the self-management of the patient on the basis of the distribution processing scheme; and when it is determined that the idle condition of the medical staff does not meet the requirement, whether the patient needs to be subjected to allocation optimization processing or not is determined according to the doctor-seeing matching condition of the patient not seeing the doctor and the medical staff in the idle state based on the allocation processing scheme of the patient, so that the self-management manual can more accurately reflect the demand of a user when being updated.

This invention relates to an analysis method based on campus card big data, comprising: acquiring a raw dataset within a preset period; cleaning and statistically analyzing the raw dataset to obtain a campus life behavior dataset usable for analysis; performing cluster analysis on two-dimensional data consisting of consumption frequency and total consumption; obtaining student consumption behavior characteristics based on the cluster analysis results; jointly analyzing the campus life behavior dataset and its effective data subset using the Apriori association rule analysis method; and obtaining student life trajectory behavior characteristics based on the association rule analysis results, providing meal preparation planning suggestions and dining recommendations for both canteen management and students. This invention provides students with a profile reflecting their life behavior characteristics, helping them with self-management and improvement, while also providing school management with suggestions for the overall management of the student living and learning environment and early warnings of potential problems, thus having more multi-dimensional and broader application scenarios.

The invention belongs to the field of chronic pain early warning, and particularly relates to a multi-mode pain early warning method for chronic pain. Multi-dimensional and multi-level patient information is used as a chronic disease diagnosis starting point, hospitals and home personal mobile devices of all layers are used as information collection media, an internet chronic disease cloud database is combined, chronic disease patient archive management is carried out, and AI auxiliary screening and health risk intelligent analysis and prediction are achieved. According to the invention, a novel chronic pain management mode is established by means of new-generation information technologies such as mobile internet, cloud computing and big data; meanwhile, self-management of the patient is assisted through a novel chronic pain management mode, and pre-judgment of chronic pain is improved.

The invention relates to the technical field of health science popularization education, in particular to a knowledge graph-based interactive science popularization platform for old-age common-disease health management scenes, which is characterized in that a main body adopts a four-layer three-terminal architecture system, and the four-layer architecture comprises a data layer, a knowledge layer, a scene interaction layer and an application layer. The system has the advantages of breaking through practical problems such as disease complexity, insufficient health education and information overload so as to improve the health quality and self-management ability of the co-sick elderly patients, and in the actual use process, through the use of a four-layer three-terminal architecture system, the form of traditional health science popularization is fundamentally changed, and the development of health science popularization is facilitated. The knowledge graph is utilized to integrate the segmented single disease knowledge into the associated network, so that the spanning from fragmented informing to systematic understanding is realized, the elderly user is helped to clear complex interaction among common diseases, precise and personalized health guidance is provided, and the learning effect and initiative of the elderly user are effectively improved.

This invention provides a self-management method, apparatus, device, and storage medium for a platform as a service (PAS), comprising: collecting key parameters of the PAS within a target period; performing an upgrade operation on the PAS when the CPU utilization or memory utilization is greater than a predefined alarm threshold; generating a comprehensive score of service profile parameters and an actual score corresponding to each type of service profile parameter within the target period according to preset rules when both CPU utilization and memory utilization are less than the predefined alarm threshold; performing an upgrade operation on the PAS when the comprehensive score is less than a predefined service profile threshold; and performing a downgrade operation on the PAS when the actual score of the throughput indicator is less than a predefined target threshold. This invention improves the performance of the PAS by modifying resource parameter configurations through upgrade or downgrade operations.

A method of programming a metering device (100, 300) with firmware comprising: assembling the metering device (100, 300), wherein the metering device (100, 300) comprises a memory (118, 308, 318, 332) and a processor (110, 310) executable to initiate programming of the metering device (100, 300) with the firmware, writing, to the memory (118, 308, 318, 332), a firmware database comprising a plurality of firmware images, each of the plurality of firmware images comprising a different firmware version and / or different configuration settings, receiving an input indicating which one of the plurality of firmware images from the firmware database should be used to operate the metering device (100, 300), and initiating, by the processor (110, 310), when the metering device 9100, 300) is connected to a power source and in response to the received input, operation of the metering device (100, 300) using the indicated one of the plurality of firmware images from the firmware database.

This invention discloses an intelligent system for the integrated management of coronary heart disease and oral health, belonging to the interdisciplinary fields of digital healthcare and chronic disease management. The key technical points are: the system includes modules for objective data collection, data quality control and cleaning, dynamic linkage analysis, plan generation, multi-terminal intervention guidance, and multi-dimensional effect evaluation. By integrating smart hardware, AI cameras, and medical scales, it jointly extracts the pathological, oral frailty, and psychosocial objective characteristics of coronary heart disease patients; constructs a multi-dimensional coupled correlation model to dynamically predict the association risk between acute cardiovascular events and local oral infections; and achieves closed-loop intervention through real-time guidance and multi-terminal collaborative mechanisms. This solves the problem of separate management of coronary heart disease and oral health in existing health management systems, realizing cross-system collaborative management, and achieving a leap from subjective education to objective digital therapy. It significantly improves the quality of self-management for elderly coronary heart disease patients in a home environment and effectively reduces the risk of adverse health outcomes.

The application relates to the technical field of healthcare informatization, in particular to a diabetes and pre-diabetes patient self-management system based on AI and CGM, which comprises the following steps: delaying and aligning CGM blood glucose flow and multi-modal physiological signals through a signal acquisition and synchronization module; dynamically adjusting a trigger threshold for different disease courses to identify suspected events by using an intelligent event trigger module; combining medical prior rules and an integrated classification model to perform joint discrimination by a hybrid consistency verification engine, and outputting a consistency probability; and cooperating with a hierarchical decision mechanism of an interaction module and online incremental learning of an adaptive learning module to optimize individual parameters. Therefore, the diabetes and pre-diabetes patient self-management system solves frequent false alarms caused by the inherent physiological delay, signal drift and other physical limitations of the CGM sensor, and the resulting patient alarm fatigue and decreased compliance, and provides more stable and personalized dynamic blood glucose management support for active people such as pre-diabetes patients.

To provide an information processing device capable of improving a degree of attainment of self management of a patient or the like without increasing a burden of a medical practitioner.SOLUTION: An information processing device includes: a unit screen display section for displaying a unit screen that receives a reply to a question for a medical question to a subject for each question, and includes one question, multiple options displayed selectably as its reply, a storage reception section for receiving an instruction for storing the selected options, and a transition reception section for receiving an instruction of transition to another unit screen for each unit screen; a storage control section for storing the selected options while following an instruction received by the storage reception section; a unit screen transition section for changing the unit screen while following an instruction received by the transition reception section; and a list screen display section for displaying a list screen including a list of replies to the stored question.SELECTED DRAWING: Figure 5

A method for programming firmware into measuring devices (100, 300), comprising the step of assembling the measuring devices (100, 300), wherein the measuring devices (100, 300) include memories (118, 308, 318, 332) and executable processors (110, 310) for initiating firmware programming of the measuring devices (100, 300), and writing a firmware database containing multiple firmware images into the memories (118, 308, 318, 332). Each of the multiple firmware images contains a different firmware version and / or different configuration settings; it receives input from a firmware database indicating which of the multiple firmware images should be used to operate the measuring instrument (100, 300); and the processor (110, 310), in response to the received input, starts the operation of the measuring instrument (100, 300) using the designated firmware image from the firmware database when the measuring instrument (100, 300) is connected to a power supply.

Systems, devices, and methods are provided for processing data events in a datalake environment using data contracts. Techniques described herein may be utilized to decouple data acquisition from data ingestion to reduce dependencies within a data lake environment, reduce or need for coordination by data producers with downstream data consumers, and allow for self-manage their schemas through versioning.

According to a method, at a given instance of a cluster of instances of at least one service, at least one monitored instance is selected from the cluster of instances according to a selection criterion such that each instance of the cluster of instances is selected as a monitored instance by at least one other instance of the cluster of instances. The given instance is caused to detect an operational status of the at least one monitored instance. If the operational status indicates that one of the at least one monitored instance is failed, the operational status of the failed monitored instance is provided to a centralized controller for the cluster of instances. Through the solution, the detection of instance liveness can be executed by individual instances symmetrically in a distributed and self-management manner.

A stroke recurrence risk assessment and personalized management method and system, the system comprises an application server, the application server comprises a database module, a recurrence risk dynamic prediction model module and a personalized management strategy generation module. The recurrence risk dynamic prediction model module is used to determine a long-term static risk index reflecting the inherent risk of the patient based on the clinical data stored in the database module, determine a short-term dynamic risk adjustment index reflecting the instantaneous risk contribution based on the dynamic feature sequence in the self-management data; by executing a risk coupling algorithm, the long-term static risk index and the short-term dynamic risk adjustment index are fused and calculated by using a weight coefficient to obtain a total risk index; the personalized management strategy generation module is used to convert the total risk index into a query vector, retrieve a matched guideline segment in a stroke management knowledge base; input the guideline segment as a constraint condition into a large language reasoning model to generate a personalized intervention scheme.

The embodiment of the present disclosure provides a kind of machine learning-based heart failure patient remote volume management method and system, comprising: collecting multi-source data, accurately identifying and nutrition analysis to diet image by deep learning model, combine the time trend analysis of physiological monitoring data and the professional evaluation of clinical test data, construct multilevel prediction model, realize the accurate prediction of volume overload risk;Automatic generation of graded warning information according to risk level, and matching individualized intervention scheme is pushed to mobile application terminal.The embodiment of the present disclosure can effectively improve the self-management ability of heart failure patients, realize early intervention of volume problem.

The invention discloses a cervical cancer patient family self-management method based on artificial intelligence and a related device, and the method comprises the steps: constructing a multi-dimensional feature matrix containing spatial-temporal features according to the historical basic physiological data, a gene detection report, an electronic medical record, wearable equipment monitoring data and a psychological assessment scale of a patient; performing feature extraction on the multi-dimensional feature matrix through a hybrid network; training a cervical cancer risk prediction model through federal learning, wherein the cervical cancer risk prediction model adopts a three-layer cascade structure; constructing a dose optimization engine containing a pharmacokinetic model and individualized genomics characteristics, wherein the dose optimization engine searches an optimal solution in the treatment management scheme through a Monte Carlo tree search algorithm; and according to the optimal solution in the treatment management scheme and the current multi-dimensional feature matrix of the patient, establishing a cervical tissue three-dimensional reconstruction model to simulate the rehabilitation effect of the patient. The cervical cancer patient risk prediction and rehabilitation effect simulation are realized, and the effectiveness of family self-management is improved.

A method of programming a metering device (100, 300) with firmware includes assembling the metering device (100, 300), where the metering device (100, 300) includes a memory (118, 308, 318, 332) and a processor (110, 310) executable to initiate programming of the metering device (100, 300) with firmware, write a firmware database including a plurality of firmware images to the memory (118, 308, 318, 332), and write the firmware database to the memory (118, 308, 318, 332). Each of the plurality of firmware images includes a different firmware version and / or a different configuration setting, receiving an input indicating which firmware image of the plurality of firmware images from the firmware database should be used to operate the metering device (100, 300), and when the metering device (100, 300) is connected to a power source and in response to the received input, generating a plurality of firmware images for the metering device (100, 300) to operate the metering device (100, 300). Operation of the metering device (100, 300) is initiated by the processor (110, 310) using the indicated one of the plurality of firmware images from the firmware database.

The invention discloses a wearable self-management system and method with emotion perception and task management functions, and relates to the field of intelligent wearing and personal health management. The system comprises a wearable device, a user terminal and a cloud server. The wearable device collects signals through a physiological sensor and carries out local emotion evaluation, an innovative task interaction unit directly manages preset tasks through physical keys, and state and emotion prompt feedback is provided through an LED lamp ring and the like. The user terminal is used for task configuration, and the cloud server performs intelligent analysis including association analysis, task dynamic adjustment analysis and habit recognition analysis, and generates a decision instruction to be issued to the equipment for execution. According to the method, deep closed-loop fusion of emotion perception and task execution is realized, self-adaptive task management and feedback based on real-time physical and mental states are provided for a user through minimum and interference-free hardware interaction design in combination with intelligence, and the task execution efficiency and the personal state management level are effectively assisted to be improved.