Health station system based on artificial intelligence assistance and health analysis method

Abstract

The embodiment of the invention provides a health station system based on artificial intelligence assistance and a health analysis method, and relates to the technical field of intelligent health services, and the system comprises an identity recognition module which is used for carrying out resident identity recognition and creating a personal health file; the data acquisition module is used for acquiring vital signs and evaluating a health scale; the intelligent analysis module is used for integrating historical medical records of residents, outpatient service records of doctors and a chronic disease management dynamic knowledge base on the basis of a big language reasoning model and an enhanced retrieval technology, analyzing collected data and generating a health risk assessment and management scheme; and the content generation module is used for automatically generating a health report and guidance content according to the health risk assessment and management scheme by adopting a multi-modal generation model. According to the technical scheme of the invention, the accuracy and efficiency of primary health services can be improved.

Description

Technical Field

[0001] This application relates to the field of intelligent health service technology, and more specifically, to an artificial intelligence-assisted health station system and health analysis method. Background Technology

[0002] With the deepening of the "Healthy China 2030" initiative, primary healthcare services have become a crucial link in improving residents' health. Traditional health stations or health clinics mainly provide services such as self-health testing, self-assessment, and health guidance and intervention, but they have significant shortcomings: 1. Disconnect between data collection and assessment: Traditional systems often separate vital sign collection, health scale assessment, and health guidance, resulting in a lack of integration of residents' health data. For example, basic vital sign self-testing, health scale self-assessment, and national public health physical examinations are completed by independent devices, generating multiple reports, resulting in a fragmented user experience and making it difficult to form a unified health view.

[0003] 2. Insufficient accuracy in assessment: Health risk assessment relies on simple rules or threshold judgments and lacks comprehensive analysis of multi-source data such as residents' historical medical records and chronic disease knowledge bases. As a result, the assessment results are not accurate enough and cannot dynamically adapt to individual differences.

[0004] 3. Poor Personalization of Guidance: Health guidance content is standardized and generalized, failing to incorporate data such as residents' personal health records and electronic medical records. This makes it difficult to implement recommendations and results in poor continuous health management. For example, existing systems struggle to handle discrepancies between health and sports data (such as conflicts between exercise recommendations and disease contraindications), and station staff lack cross-disciplinary assessment capabilities.

[0005] 4. Weak systematization and continuity: Health management processes are interrupted, data is not included in the resident health record system, long-term tracking and intervention are impossible, and digital and intelligent full-cycle management is not achieved.

[0006] While some existing systems attempt to integrate data, they focus primarily on report generation and indicator set processing, failing to fully utilize AI models for dynamic reasoning and content generation, and lacking multimodal push mechanisms. Furthermore, according to government requirements, health stations must possess multiple functions such as health promotion and disease prevention, but existing systems have gaps in AI-assisted decision-making and personalized plan generation. Summary of the Invention

[0007] The embodiments of this application provide an artificial intelligence-assisted health station system and health analysis method to solve the technical problems existing in the prior art.

[0008] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0009] According to a first aspect of the embodiments of this application, an artificial intelligence-assisted health station system is provided, comprising: The identity verification module is used to identify residents and create personal health records; The data acquisition module is used for collecting vital signs and conducting health scale assessments. The intelligent analysis module is used to integrate residents' historical medical records, doctors' outpatient records, and a dynamic knowledge base for chronic disease management based on a large language reasoning model and enhanced retrieval technology to analyze the collected data and generate health risk assessments and management plans. The content generation module is used to automatically generate health reports and guidance content based on health risk assessments and management plans using a multimodal generation model.

[0010] In some embodiments of this application, based on the foregoing scheme, the following further methods are also included: Push and Archive Module: Used to automatically push health reports and guidance content links, and automatically incorporate the collected data into residents' electronic health records.

[0011] In some embodiments of this application, based on the aforementioned scheme, the identity recognition module performs identity recognition by swiping an ID card / scanning an electronic health card / facial recognition during the process of resident identity recognition.

[0012] In some embodiments of this application, based on the foregoing scheme, the data acquisition module includes: Vital signs sensors are used to collect residents' vital signs; A scale assessment terminal used for conducting health scale assessments.

[0013] In some embodiments of this application, based on the foregoing scheme, the intelligent analysis module includes: The model training submodule is used to train large language reasoning models; The multi-source data fusion submodule is used to integrate residents' historical medical records, doctors' outpatient records, and a dynamic knowledge base for chronic disease management; The dynamic weight adjustment submodule is used to dynamically adjust the weights during the analysis process.

[0014] According to a second aspect of the embodiments of this application, an artificial intelligence-assisted health analysis method is provided, comprising: Conduct resident identification and create personal health records; Collect vital signs of residents who have passed the identification process; Based on the collected vital sign data, a large language reasoning model is used to integrate residents' historical medical records, doctors' outpatient records, and a dynamic knowledge base for chronic disease management to conduct risk assessment and generate health risk assessment and management plans. Based on the health risk assessment and management plan, a multimodal large model is used to generate health reports and guidance content; The system automatically pushes links to health reports and guidance content, and automatically incorporates the collected data into residents' electronic health records.

[0015] The technical solution of this application has the following beneficial effects: 1. Enhance data integration: By linking multi-source data through a unified platform, the problem of disconnect between collection, evaluation, and guidance can be solved, forming an integrated health view and realizing information aggregation.

[0016] 2. Enhance assessment accuracy: Utilize large language models and dynamic knowledge bases to achieve context-based, accurate risk assessments and reduce misjudgment rates. For example, combining historical medical records can improve the accuracy of chronic disease risk prediction.

[0017] 3. Achieve personalized guidance: Through multimodal generation models, produce tailor-made health content (such as dialect audio, personalized exercise videos, etc.) to improve user compliance and satisfaction, and effectively improve the rate of health behavior improvement through personalized suggestions.

[0018] 4. Ensure continuous management: Automatic archiving and push mechanisms support long-term health trajectory tracking, and AI enables more efficient full-cycle management.

[0019] 4. Improve system efficiency: AI assistance reduces human intervention, significantly reduces service response time, supports large-scale applications at the grassroots level, and achieves the goal of high efficiency and convenience.

[0020] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0021] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings: Figure 1 A structural block diagram of an AI-assisted health station system according to an embodiment of this application is shown; Figure 2 A flowchart illustrating an artificial intelligence-assisted health analysis method according to an embodiment of this application is shown. Detailed Implementation

[0022] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art.

[0023] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0024] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0025] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0026] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0027] The following detailed description of some embodiments of this application will be provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0028] The following technical issues exist in the existing health station system: 1. Insufficient data integration: Health data collection (such as vital signs, scales, and physical fitness data) is scattered, assessment and guidance are disconnected, and there is a lack of a unified platform for correlation analysis.

[0029] 2. Low assessment accuracy: Risk assessment is based on static rules and does not incorporate dynamic data such as residents' historical medical records and chronic disease knowledge bases, resulting in inaccurate judgment of health status.

[0030] 3. Non-personalized guidance: Health advice is templated and does not take into account individual health records, behavioral habits, and other factors, resulting in poor feasibility and low user compliance.

[0031] 4. Weak management continuity: Health management is interrupted, data is not systematically archived, and it is impossible to support long-term tracking and early intervention.

[0032] 5. Inefficient system: It relies on manual guidance, has a slow service response, and is difficult to meet the needs of large-scale grassroots applications, failing to achieve the goal of "wide coverage, high efficiency and convenience".

[0033] To address this technical problem, this application provides an AI-assisted health station system. This system integrates multi-source data through modular design and utilizes AI models to achieve intelligent reasoning and content generation.

[0034] For example, see Figure 1 The diagram shows a structural block diagram of a health station system based on artificial intelligence assistance.

[0035] like Figure 1 As shown, specifically, the system 100 includes: The identity recognition module 101 is used to identify residents and create personal health records; Data acquisition module 102 is used for collecting vital signs and conducting health scale assessments; The intelligent analysis module 103 is used to integrate residents' historical medical records, doctors' outpatient records, and a dynamic knowledge base for chronic disease management based on a large language reasoning model and enhanced retrieval technology to analyze the collected data and generate health risk assessment and management plans. The content generation module 104 is used to automatically generate health reports and guidance content based on health risk assessment and management plans using a multimodal generation model.

[0036] It should be noted that, in this embodiment, after the identity recognition module successfully identifies the resident, it automatically queries the resident's information to create a personal health record to ensure data consistency.

[0037] It should be noted that, in this embodiment, the multimodal generation model used by the content generation module can be an audio, video, and image generation model based on Transformer; the generated health reports and guidance content can be voice prompts, visual charts, video tutorials, etc.

[0038] In some feasible embodiments, based on the foregoing scheme, the following further applies: Push and Archive Module: Used to automatically push health reports and guidance content links, and automatically incorporate the collected data into residents' electronic health records.

[0039] It should be noted that in this embodiment, the push and archiving module pushes health reports and guidance content links through channels such as SMS and WeChat. The push and archiving module achieves automated system management.

[0040] In some feasible embodiments, based on the aforementioned scheme, the identity recognition module performs identity recognition by swiping an ID card / scanning an electronic health card / facial recognition during the resident identity recognition process.

[0041] In some feasible embodiments, based on the foregoing scheme, the data acquisition module includes: Vital signs sensors are used to collect residents' vital signs; A scale assessment terminal used for conducting health scale assessments.

[0042] It should be noted that, in this embodiment, vital signs refer to data such as blood pressure, blood sugar, and electrocardiogram. Health scales refer to scales such as chronic disease risk and lifestyle assessments.

[0043] It should be noted that, in this embodiment, vital signs are collected in real time through a standardized interface.

[0044] In some feasible embodiments, based on the foregoing scheme, the intelligent analysis module includes: The model training submodule is used to train large language reasoning models; The multi-source data fusion submodule is used to integrate residents' historical medical records, doctors' outpatient records, and a dynamic knowledge base for chronic disease management; The dynamic weight adjustment submodule is used to dynamically adjust the weights during the analysis process.

[0045] Based on the same inventive concept, this application also provides an artificial intelligence-assisted health analysis method, see [link to relevant documentation]. Figure 2 This illustrates a flowchart of an AI-assisted health analysis method, such as... Figure 2 As shown, the method includes: Conduct resident identification and create personal health records; Collect vital signs of residents who have passed the identification process; Based on the collected vital sign data, a large language reasoning model is used to integrate residents' historical medical records, doctors' outpatient records, and a dynamic knowledge base for chronic disease management to conduct risk assessment and generate health risk assessment and management plans. Based on the health risk assessment and management plan, a multimodal large model is used to generate health reports and guidance content; The system automatically pushes links to health reports and guidance content, and automatically incorporates the collected data into residents' electronic health records.

[0046] It should be noted that this method can be implemented based on an AI-assisted health station system provided in this application. When implemented through this system, the specific process of this method is as follows: 1. Identity verification: Residents can verify their identity by swiping their ID card / scanning their electronic health card / using facial recognition. After successful verification, they can query or create their associated personal health records.

[0047] 2. Data collection: Residents take various health measurements and fill out health questionnaires. The system automatically calibrates sensor data and filters outliers.

[0048] 3. AI Health Analysis: Based on the collected data, the big data reasoning model calls on residents' health records, chronic disease management knowledge base, electronic medical records, etc. to conduct risk assessment and generate health risk assessment and management plans.

[0049] 4. AI Content Generation: Based on the AI ​​analysis results, multimodal health reports and guidance content are generated using a multimodal large model.

[0050] 5. Push and Archive: Push to users, and archive data to personal health records.

[0051] Other embodiments of this application will readily conceive of by those skilled in the art upon consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. It should be understood that this application is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A health station system based on artificial intelligence assistance, characterized in that, include: The identity verification module is used to identify residents and create personal health records; The data acquisition module is used for collecting vital signs and conducting health scale assessments. The intelligent analysis module is used to integrate residents' historical medical records, doctors' outpatient records, and a dynamic knowledge base for chronic disease management based on a large language reasoning model and enhanced retrieval technology to analyze the collected data and generate health risk assessments and management plans. The content generation module is used to automatically generate health reports and guidance content based on health risk assessments and management plans using a multimodal generation model.

2. The system according to claim 1, characterized in that, Also includes: Push and Archive Module: Used to automatically push health reports and guidance content links, and automatically incorporate the collected data into residents' electronic health records.

3. The system according to claim 1, characterized in that, The identity recognition module identifies residents by swiping their ID cards, scanning their electronic health cards, or using facial recognition.

4. The system according to claim 1, characterized in that, The data acquisition module includes: Vital signs sensors are used to collect residents' vital signs; A scale assessment terminal used for conducting health scale assessments.

5. The system according to claim 1, characterized in that, The intelligent analysis module includes: The model training submodule is used to train large language reasoning models; The multi-source data fusion submodule is used to integrate residents' historical medical records, doctors' outpatient records, and a dynamic knowledge base for chronic disease management; The dynamic weight adjustment submodule is used to dynamically adjust the weights during the analysis process.

6. A health analysis method based on artificial intelligence, characterized in that, include: Conduct resident identification and create personal health records; Collect vital signs of residents who have passed the identification process; Based on the collected vital sign data, a large language reasoning model is used to integrate residents' historical medical records, doctors' outpatient records, and a dynamic knowledge base for chronic disease management to conduct risk assessment and generate health risk assessment and management plans. Based on the health risk assessment and management plan, a multimodal large model is used to generate health reports and guidance content; The system automatically pushes links to health reports and guidance content, and automatically incorporates the collected data into residents' electronic health records.

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