Health monitoring and management system

Abstract

The invention discloses a health monitoring and management system, and relates to the technical field of health monitoring. The health monitoring and management system comprises a user mobile terminal, a health manager terminal and a background management terminal. The user mobile terminal is electrically connected with the intelligent health monitoring equipment, and obtains and uploads health data; the background management terminal performs early warning judgment according to the health data and sends early warning information to the health manager terminal; and the health manager terminal receives the early warning information for intervention guidance. And the user mobile terminal is used for acquiring the biological information data and performing matching analysis. And the background management end realizes the functions of data monitoring, early warning triggering, report generation and image recognition. And the health supervisor terminal provides professional consultation and guidance through the health supervisor intervention module. The system is further provided with a family health module, and health data sharing and checking among family members are achieved. According to the system, real-time monitoring, intelligent analysis and timely intervention of health data can be realized, and the health management efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of health monitoring technology, and more specifically to a health monitoring and management system. Background Technology

[0002] With the aging population and rising incidence of chronic diseases, health monitoring and management systems are playing an increasingly important role in disease prevention and improving quality of life. Currently, these systems are widely used in personal health management, family health monitoring, and remote patient monitoring in medical institutions.

[0003] Existing technologies typically include health data collection modules, big data intelligent analysis modules, and real-time monitoring and early warning modules. They achieve organic integration among users, hospitals, and communities through three main modules: a family health management main module, a community health management main module, and a health management cloud platform. They also include an early warning information recording, organization, and uploading module to record the time, location, and environmental conditions when an early warning occurs and propose emergency response plans. While such systems can achieve health data collection and early warning functions to a certain extent, they lack continuity and comprehensiveness in data collection.

[0004] In existing technologies, multiple monitoring devices and wearable devices are integrated to collect patients' physiological and motor parameters, providing medical staff with a comprehensive view of the patient's health status. Through real-time monitoring, medical staff can promptly detect abnormal physiological and motor characteristics in patients. While such systems are relatively mature in specific fields, they lack health management functions for ordinary users. Although these devices are comprehensive in data collection, they lack connection with professional health managers and cannot provide timely and effective professional intervention.

[0005] Existing dynamic analysis systems for health monitoring data can improve the accuracy and universality of monitoring and analysis, enabling dynamic analysis of human health monitoring data and improving the accuracy of monitoring data, thus facilitating early prediction and prevention of diseases. However, these systems lack integration in terms of intelligent early warning mechanisms and professional intervention, making it difficult to form a complete closed loop for health management.

[0006] In summary, existing health monitoring and management systems suffer from the following shortcomings: First, data collection is discontinuous and incomplete, making it difficult to form complete health records; second, the early warning mechanism is lagging, failing to detect health risks in a timely manner; third, the professional intervention mechanism is inadequate, lacking real-time guidance from health managers; fourth, user participation is low, making it difficult to conduct continuous and effective health management; and fifth, system integration is low, with insufficient coordination between functional modules, failing to form a complete health management loop. Therefore, there is an urgent need for a health monitoring and management system capable of real-time health data monitoring, intelligent early warning, professional intervention, and personalized management to address the aforementioned technical problems. Summary of the Invention

[0007] To address the technical problems existing in current health management systems, such as discontinuous and incomplete data collection, lack of and delayed early warning mechanisms, inadequate intervention mechanisms, low user participation, and low system integration, and to achieve a closed-loop health management process, real-time monitoring and intelligent early warning, a combination of personalization and professionalization, family health management, high system integration, and improved user health awareness and participation, this invention provides an intelligent health monitoring and management system.

[0008] The technical solution adopted by this invention to solve its technical problem is as follows: a health monitoring and management system is provided, electrically connected to an intelligent health monitoring device, including a user mobile terminal, a health manager terminal, and a back-end management terminal; the user mobile terminal uploads the health data collected by the intelligent health monitoring device; the back-end management terminal makes an early warning judgment based on the collected health data and sends the early warning information to the health manager terminal; the health manager terminal receives the early warning information and provides intervention guidance.

[0009] Preferably, the user mobile terminal includes: a data acquisition module and an information management module; the data acquisition module is electrically connected to the intelligent health monitoring device to acquire biological information data; the information management module matches the acquired biological information data with personal information and analyzes it through AI big data algorithms to perform functions such as early warning display, historical query, health report generation, AI recognition, online consultation, and health record management.

[0010] Preferably, the health manager terminal uses an H5 interface to implement functions such as member management, alarm reception, trend analysis, online communication, and follow-up management.

[0011] Preferably, the backend management terminal includes: an early warning analysis module, a report generation module, and an AI recognition module; wherein, the early warning analysis module runs continuously, monitors the incoming user data stream, and has a built-in dynamic threshold model based on personalized user information and an anomaly detection algorithm; when a data point exceeds the threshold or continuous data shows an abnormal trend, the early warning analysis module will be immediately triggered; after determining an anomaly, the early warning analysis module will generate an early warning event record and send the early warning information to the user's mobile terminal and the corresponding health administrator terminal simultaneously through a message push service; the report generation module is started periodically or upon user request. The system extracts user data within a specified time period from the database, automatically generates health trend maps using data visualization technology, and fills in specific data using text templates to form daily / monthly health reports and achievement reports. When a user uploads a physical examination report image or a tongue photograph, the AI ​​recognition module calls a pre-trained deep learning model to perform image recognition and information extraction, transforming unstructured image information into structured health data or assessment conclusions, which are then stored in the user's profile. Based on the early warning analysis module, the report generation module, and the AI ​​recognition module, the system achieves big data analysis, user profile generation, early warning push notifications, report export, and system management functions.

[0012] Preferably, the health manager terminal includes: a health manager intervention module; after receiving member warning information pushed by the backend management terminal, the health manager intervention module immediately takes action; the health manager views the member's complete health record to fully understand their historical data and health status; the health manager proactively contacts the user to provide professional consultation, reminders and intervention guidance; all intervention records are saved to form a service closed loop.

[0013] Preferably, the user's mobile terminal also includes: a family health module, in which the user adds relatives and friends as family members by sharing an invitation code or QR code; at the database level, a family group relationship table is established; the aggregated view of the health data of the bound family members is opened to family members; members can switch between different members' data overview and warning status within the App with one click.

[0014] The beneficial effects of this invention are as follows: Compared with existing health management systems, this invention achieves a closed-loop health management process, forming a complete management chain from data collection, early warning, intervention to feedback, thus improving the efficiency of health management; through the combination of intelligent hardware and algorithm models, real-time monitoring and intelligent early warning are achieved, enabling early identification and warning of health risks; combining AI analysis with intervention by health managers ensures both professionalism and user experience, achieving a combination of personalization and professionalism; it supports multi-user data sharing, facilitating collaborative family health management; deep integration of software and hardware ensures smooth data flow, avoids information silos, and has a high degree of system integration; through daily reports, monthly reports, trend charts, and other forms, it enhances users' initiative in health management and improves users' health awareness and participation. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0016] Figure 1 The overall architecture diagram provided for this invention; Figure 2 This is a functional block diagram of a user mobile terminal provided by the present invention; Figure 3 A functional module diagram of the backend management terminal provided by the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Example 1

[0018] A health monitoring and management system, such as Figure 1 As shown, it is electrically connected to the intelligent health monitoring device, including a user mobile terminal, a health manager terminal, and a back-end management terminal.

[0019] In this health monitoring and management system, users upload health data collected by smart health monitoring devices via their mobile terminals; the back-end management terminal makes early warning judgments based on the collected health data and sends the early warning information to the health manager's terminal; the health manager's terminal receives the early warning information and provides intervention guidance.

[0020] User mobile terminal, such as Figure 2 As shown, the system includes a data acquisition module and an information management module. The data acquisition module is electrically connected to the intelligent health monitoring device to acquire biological information data. The information management module matches the acquired biological information data with personal information and analyzes it using AI big data algorithms to provide functions such as early warning display, historical query, health report generation, AI recognition, online consultation, and health record management. Through the information management module, users can view their health status in real time, obtain health warning information, query historical health data, generate personalized health reports, use AI recognition to analyze health status, conduct online consultations, and manage their personal health records.

[0021] Furthermore, the smart health monitoring device transmits the collected physiological data to the user's mobile terminal's data acquisition module via an encrypted protocol at a preset frequency (e.g., real-time for wristbands, after each measurement for body fat scales) or by user-initiated triggering (for rapid testing devices). The data acquisition module within the app performs preliminary verification and formatting of the received raw data, and then, via HTTPS and SDK protocols, calls the API interface provided by the backend server to upload the data packet (containing user ID, device type, measurement value, and timestamp) to the backend server's database for persistent storage in real time.

[0022] The user's mobile device also includes a family health module. Within this module, users can add relatives and friends as family members by sharing invitation codes or QR codes. At the database level, a family group relationship table is established. An aggregated view of the health data of bound family members is made available to all family members. Members can switch between different members' data overviews and alerts within the app with a single click. This allows family members to monitor each other's health status, stay informed about changes in family members' health, and form a family health management network.

[0023] The health administrator terminal uses an H5 interface to manage members, receive alerts, analyze trends, communicate online, and conduct follow-up visits. Health administrators can view information on all users under their responsibility, receive health alerts from the backend, analyze user health data trends, communicate with users online, and conduct regular follow-up visits. The H5 interface design allows health administrators to conveniently access the system on different devices, improving work efficiency.

[0024] The health manager terminal includes a health manager intervention module. Upon receiving a member alert from the backend management system, the health manager immediately takes action. The health manager reviews the member's complete health record to gain a comprehensive understanding of their historical data and health status. The health manager proactively contacts the user, providing professional consultation, reminders, and intervention guidance. All intervention records are saved, forming a closed-loop service. Through the professional intervention of the health manager, users can receive timely health guidance, preventing further escalation of health risks.

[0025] The health management terminal is essentially a responsive web application that maintains real-time communication with the backend server via Ajax or WebSocket technologies, ensuring the immediacy of alerts and smooth interaction. It seamlessly combines AI-powered intelligent alerts with professional human intervention, achieving "early intervention" and providing users with warm and trustworthy health management services.

[0026] Backend management terminal such as Figure 3 As shown, the system includes an early warning analysis module, a report generation module, and an AI recognition module. The early warning analysis module runs continuously, monitoring the incoming user data stream. It incorporates a dynamic threshold model based on personalized user information and an anomaly detection algorithm. When a data point exceeds a threshold or continuous data shows an abnormal trend, the early warning analysis module is immediately triggered. After determining an anomaly, the module generates an early warning event record and simultaneously sends the warning information to the user's mobile terminal and the corresponding health manager's terminal via push notification service. This allows both the user and the health manager to be informed of any health abnormalities immediately and take appropriate measures.

[0027] The report generation module is activated periodically or upon user request, extracting user data for a specified time period from the database. It automatically generates health trend graphs using data visualization technology and, combined with text templates, populates the data to create daily / monthly health reports and outcome reports. These reports visually demonstrate changes in users' health status, helping users and health managers better understand health data.

[0028] When a user uploads a picture of their medical examination report or tongue, the AI ​​recognition module uses a pre-trained deep learning model to perform image recognition and information extraction. This transforms unstructured image information into structured health data or assessment conclusions, which are then stored in the user's profile. This feature eliminates the need for users to manually input medical examination report data, improving the convenience and accuracy of data collection.

[0029] Based on the early warning analysis module, report generation module, and AI recognition module, the backend management terminal realizes big data analysis, user profile generation, early warning push, report export, and system management functions. The big data analysis function can uncover patterns and trends in health data; the user profile generation function can build personalized health characteristic descriptions for each user; the early warning push function ensures timely notification of health risks; the report export function makes it convenient for users and health managers to obtain health reports; and the system management function ensures the stable operation of the entire system.

[0030] Through the collaborative work of user mobile terminals, health manager terminals, and back-end management terminals, this health monitoring and management system realizes the intelligent collection, analysis, early warning, and intervention of health data throughout the entire process, providing users with comprehensive, timely, and personalized health monitoring and management services. Example 2

[0031] This embodiment provides a backend management terminal, such as... Figure 3 As shown, it includes an early warning analysis module, a report generation module, and an AI recognition module.

[0032] The early warning analysis module continuously monitors the incoming user data stream. This module incorporates a dynamic threshold model based on personalized user information and an anomaly detection algorithm. The early warning analysis module is immediately triggered when a data point exceeds a threshold or when continuous data exhibits an abnormal trend. After determining an anomaly, the module generates an early warning event record and simultaneously sends the warning information to both the user's mobile terminal and the corresponding health administrator's terminal via push notification service.

[0033] The early warning analysis module's dynamic threshold model establishes personalized normal ranges for health indicators for each user based on their historical health data, age, gender, and underlying medical conditions. For example, for users with hypertension, the system sets stricter blood pressure monitoring thresholds; while for healthy individuals, standard medical reference ranges are used. The trend anomaly detection algorithm uses time series analysis to identify abrupt changes and gradual anomalies in user health data, such as a continuous upward trend in blood sugar levels over a week. Even if a single measurement has not exceeded the threshold, the system can issue an early warning.

[0034] The report generation module starts periodically or upon user request, extracting user data for a specified time period from the database and automatically generating health trend maps using data visualization technology. Combined with text templates, specific data is populated to generate daily / monthly health reports and outcome reports.

[0035] The report generation module supports various report types, including daily, weekly, monthly, and annual health summaries. For daily reports, the system automatically extracts various health indicators for the day, such as blood pressure, blood sugar, heart rate, and steps, and compares them with the previous day's data. Monthly reports analyze the user's health trends over the month and identify potential health risks. Annual health summaries comprehensively assess changes in the user's health status throughout the year and provide targeted improvement suggestions. Reports utilize various visual charts to display health data, including line charts, bar charts, and radar charts, allowing users to intuitively understand the trends in their health status.

[0036] When a user uploads a picture of their medical examination report or tongue, the AI ​​recognition module uses a pre-trained deep learning model to perform image recognition and information extraction. This transforms unstructured image information into structured health data or assessment conclusions, which are then stored in the user's profile.

[0037] The AI ​​recognition module uses convolutional neural networks (CNN) and optical character recognition (OCR) technology to process images from medical examination reports, accurately identifying and extracting various indicator values ​​from the reports. For tongue image analysis, the system employs a specially trained deep learning model capable of recognizing features such as the color, thickness, and distribution of the tongue's texture and coating, and providing corresponding health assessments based on Traditional Chinese Medicine theory. The AI ​​recognition module supports various common medical examination report formats and tongue images under different lighting conditions, achieving an accuracy rate of over 95%.

[0038] Based on the early warning analysis module, report generation module, and AI recognition module, the backend management terminal has implemented multiple functions: The big data analytics function generates population health trend reports by statistically analyzing the health data of all users, providing a basis for health management decisions. The system can analyze data according to multiple dimensions such as age group, region, and occupation to identify the health characteristics and risk factors of different groups.

[0039] The user profile generation feature constructs a comprehensive health profile based on a user's health data, lifestyle habits, exercise records, and other information, providing a foundation for personalized health management. The user profile includes multiple dimensions such as basic health status, chronic disease risk assessment, and lifestyle score, and is continuously updated and improved as data accumulates.

[0040] When the system detects abnormalities in a user's health data, it automatically generates an alert and pushes it to users based on the severity of the abnormality. For severe abnormalities, the system will notify both the user and their health manager, and provide coping suggestions; for minor abnormalities, the system will send a health reminder to the user and suggest lifestyle adjustments.

[0041] The report export function supports exporting generated health reports in multiple formats such as PDF and Excel, making it convenient for users to save and print. Exported reports retain complete charts and analysis content, and support customization of report covers and content modules.

[0042] The system management functions provide basic management features such as user permission management, data backup and recovery, and system log querying to ensure the secure and stable operation of the system. Administrators can set different levels of access permissions to control the scope of access to user data by health administrators and protect user privacy.

[0043] In a preferred embodiment, the early warning analysis module also integrates machine learning algorithms, which can continuously optimize early warning rules based on user feedback to reduce the false alarm rate. The system records the user's confirmation status after each early warning. If a user repeatedly marks a certain type of early warning as a "false alarm," the system will automatically adjust the relevant threshold parameters to improve the accuracy of the early warning.

[0044] In another preferred embodiment, the report generation module supports intelligent text generation technology, which can automatically generate personalized health suggestions and improvement measures based on user data, making the report content more instructive. The system analyzes the user's health data trends, identifies potential health problems, extracts relevant health suggestions from the knowledge base, and generates targeted improvement plans. Example 3

[0045] In this embodiment, the health manager intervention module is an important component of the health manager terminal, used to enable health managers to provide timely intervention and health guidance to members.

[0046] The main function of the health manager intervention module is to enable health managers to immediately intervene after receiving member alert information pushed from the backend management terminal. When the backend management terminal detects abnormal health data or potential health risks in a member, the system automatically generates an alert and pushes it to the health manager intervention module on the health manager's terminal. The alert information includes the member's basic information, the type of abnormal indicator, and the severity of the abnormality, allowing health managers to quickly understand the situation.

[0047] The health manager intervention module provides a function to view member health records. Health managers can use this function to view a member's complete health record, gaining a comprehensive understanding of their historical data and health status. The health record includes multi-dimensional information such as the member's basic health information (e.g., age, gender, height, weight), chronic disease history, medication records, allergy history, family medical history, recent trends in health indicators, and lifestyle data (e.g., diet, exercise, sleep). Health managers can view member health data in various formats, such as charts and data lists, and conduct longitudinal and cross-sectional comparative analyses to gain a comprehensive understanding of the member's health status.

[0048] The health manager intervention module also integrates multiple contact methods, allowing health managers to proactively contact users and provide professional consultation, reminders, and intervention guidance. These methods include, but are not limited to, telephone, video calls, instant messaging, and email. Health managers can choose the appropriate contact method based on the member's urgency and preferences. During the contact process, the module provides support from a health knowledge base, allowing health managers to quickly access relevant health knowledge, guidance plans, and health advice to provide members with personalized health intervention plans. For different types of health problems, health managers can use this module to send members customized health education materials, dietary advice, exercise plans, medication reminders, and other content.

[0049] The health manager intervention module has comprehensive recording capabilities, automatically saving all intervention records to form a service loop. The content, time, method, and member feedback of each intervention activity are recorded in detail. Intervention records include a complete information chain encompassing the intervention cause (early warning information), intervention process (communication content, health advice), and intervention results (member acceptance level, follow-up plans). These records can be used for subsequent follow-up references, intervention effectiveness evaluation, and service quality analysis. Through complete intervention records, health managers can track changes in members' health status, assess the effectiveness of intervention measures, and adjust intervention strategies as needed, thus forming a complete service loop from problem identification, plan development, intervention implementation to effectiveness evaluation.

[0050] In a preferred embodiment, the health manager intervention module also integrates intelligent analysis capabilities, enabling it to automatically generate intervention suggestions based on members' health data and historical intervention records, thus assisting health managers in developing more precise intervention plans. This function analyzes trends in members' health data and historical intervention effects to predict potential health risks and recommend suitable intervention measures, thereby improving the efficiency and accuracy of health managers' work.

[0051] In another preferred embodiment, the health administrator intervention module also supports team collaboration, allowing multiple health administrators to work together on complex health issues. When a member's health condition requires assessment and intervention by a multidisciplinary team, the lead health administrator can use this module to invite other specialist health administrators to participate in discussions and jointly develop an intervention plan. The system records the entire team collaboration process to ensure the continuity and consistency of the intervention plan.

[0052] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0053] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A health monitoring and management system, electrically connected to an intelligent health monitoring device, characterized in that, include: User mobile terminal, health manager terminal and back-end management terminal; The user's mobile terminal uploads the health data collected by the intelligent health monitoring device; The back-end management terminal makes an early warning judgment based on the collected health data and sends the early warning information to the health manager terminal; the health manager terminal receives the early warning information and provides intervention guidance.

2. The health monitoring and management system according to claim 1, characterized in that, The user mobile terminal includes a data acquisition module and an information management module; the data acquisition module is electrically connected to the intelligent health monitoring device to acquire biological information data; the information management module matches the acquired biological information data with personal information and analyzes it through AI big data algorithms to perform functions such as early warning display, historical query, health report generation, AI recognition, online consultation, and health record management.

3. The health monitoring and management system according to claim 1, characterized in that, The health manager terminal uses an H5 interface to implement functions such as member management, alarm reception, trend analysis, online communication, and follow-up management.

4. The health monitoring and management system according to claim 1, characterized in that, The backend management system includes: an early warning analysis module, a report generation module, and an AI recognition module; The system includes an early warning analysis module that runs continuously and monitors the incoming user data stream. It incorporates a dynamic threshold model based on personalized user information and an abnormal trend detection algorithm. The early warning analysis module is immediately triggered when a data point exceeds the threshold or when continuous data shows an abnormal trend. After determining that an anomaly is detected, the early warning analysis module generates an early warning event record and sends the early warning information to both the user's mobile terminal and the corresponding health administrator terminal via a message push service. The report generation module is activated periodically or upon user request. It extracts user data for a specified time period from the database, automatically generates a health trend map using data visualization technology, and fills in specific data using a text template to form a daily / monthly health report and a results report. When a user uploads a physical examination report image or a photo of their tongue, the AI ​​recognition module calls a pre-trained deep learning model to perform image recognition and information extraction, transforming unstructured image information into structured health data or assessment conclusions, and storing them in the user's profile. Based on the aforementioned early warning analysis module, report generation module, and AI recognition module, the system can perform big data analysis, user profile generation, early warning push, report export, and system management functions.

5. A health monitoring and management system according to claim 1 or 3, characterized in that, The health manager terminal includes a health manager intervention module; upon receiving a member warning message pushed by the backend management terminal, the health manager immediately takes action; the health manager views the member's complete health record to fully understand their historical data and health status; the health manager proactively contacts the user to provide professional consultation, reminders, and intervention guidance; all intervention records are saved, forming a service loop.

6. A health monitoring and management system according to claim 1 or 2, characterized in that, The user's mobile terminal also includes a family health module, in which users can add relatives and friends as family members by sharing invitation codes or QR codes; at the database level, a family group relationship table is established; the aggregated view of the health data of the bound family members will be open to family members; members can switch between different members' data overview and warning status within the App with one click.

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