AI health management and first-aid system based on wearable intelligent equipment

Abstract

The invention mainly aims to provide an intelligent health management system based on wearable intelligent equipment and an AI technology, and the intelligent health management system can be deeply operated according to health data collected by the wearable intelligent equipment of a user, so that 1, accurate monitoring, prediction, early warning and first-aid alarming are performed on the health of the user, and an AI comprehensive health management service is provided for the user; 2, popular and easy-to-understand visual real-time health monitoring, prediction and early warning information is provided for the user, so that the user can grasp personal health conditions in time to perform'preventive medical treatment 'or diagnosis and treatment; 3, through a simple and smooth interaction mode with the user, a convenient AI health consultation service is provided for the user; and fourthly, the system is provided with a first-aid automatic response mechanism to provide timely first-aid service for people who need first aid and are helpless.

Description

Technical Field

[0001] The present invention relates to an AI health management system, particularly a method for health monitoring, prediction, and emergency response based on wearable intelligent devices and AI algorithms. Specifically, the present invention relates to a system that can real-time evaluate and predict the comprehensive health status of users and automatically trigger an SOS response when an emergency health problem occurs. Background Art

[0002] With the popularization of wearable intelligent devices, more and more health monitoring systems have been applied to daily life. Most of the existing intelligent health management systems focus on monitoring a single health indicator (such as heart rate, blood pressure, exercise amount, etc.) or only provide basic health data recording functions. Although these systems can provide certain health assessment functions, they lack an intelligent comprehensive health index assessment and real-time response mechanism in case of emergency, especially when sudden health problems occur, they cannot intervene in a timely and accurate manner. Therefore, how to accurately evaluate the health status of users through the integration of multi-dimensional health data and quickly trigger an emergency response in case of emergency has become an urgent problem to be solved. Summary of the Invention

[0003] The main object of the present invention is to provide an intelligent health management system based on wearable intelligent devices and AI technology, which can, according to the health data collected by users wearing intelligent devices, through the in-depth operation of this system, one, accurately monitor, predict, and give early warning of users' health and give first aid alarm, providing users with AI comprehensive health management services; two, provide users with easy-to-understand visual real-time health monitoring, prediction, and early warning information so that users can timely master their personal health conditions and carry out "preventive medical treatment" or diagnosis and treatment; three, provide users with convenient AI health consultation services through a simple and smooth interaction method with users; four, this system sets up an emergency automatic response mechanism to provide timely first aid services for those in need of first aid and helpless people.

[0004] To achieve the above object, the present invention provides an AI health management and first aid system, including: "mobile phone App",

Health data collection and primary health data generation module

Comprehensive health index and future comprehensive health index calculation module

Digital dynamic health operation trajectory and first aid signal generation module

Sampling frequency and weight dynamic adjustment module

Visual dynamic health operation trajectory generation module

Displacement module

Language interaction module

[0005] 1. Figure 1 It is a schematic diagram of the processing flow of

AI health management and first aid system based on wearable intelligent devices

[0006] 2. As Figure 1, the process includes a wearable smart device, a smartphone, and an AI system computer center. The wearable smart device collects health data and transmits it to the user's smartphone; the smartphone uploads the data to the computer AI system. The data is processed by the [Health Data Collection and Primary Health Data Generation Module] of the system to generate "primary health data" and output it to the [Comprehensive Health Index and Future Comprehensive Health Index Calculation Module] for in-depth processing to generate "comprehensive health index and future comprehensive health index"; the "comprehensive health index and future comprehensive health index" are further processed by the [Digital Dynamic Health Trajectory and Emergency Signal Generation Module] to generate "digital dynamic health trajectory and emergency signal" for health monitoring, prediction, and determination of whether the user is in an emergency state; at the same time, the data triggers the [Sampling Frequency and Weight Dynamic Adjustment Module] to dynamically adjust the sampling frequency and weight; the "digital dynamic health trajectory" is generated by the [Visual Dynamic Health Trajectory Generation Module] to generate a "visual dynamic health trajectory", which is linked to the user's health through the [Language Interaction Module]; when the system determines that the user is in an emergency state, it triggers the [Displacement Module] and the [Language Interaction Module] to automatically link to the user's emergency contact 120 for first aid or the system automatically links to 120 for first aid, while providing the user's location information. The entire process realizes the full-process closed-loop management of AI health management and first aid. Specific implementation plan

[0007] The following are the steps of the specific implementation of the present invention in combination with the accompanying drawings. As Figure 1 shown, the AI health management and first aid system of the present invention provides the following technical implementation plans

[0008] 1. User "Mobile App"

[0009] A. The system objective of the "Mobile App": Provide user-side support for realizing "AI health management and first aid" for users.

[0010] B. Function module settings of this system

[0011] · Function module settings of the system

[0012] (1) "User Health Information Module"

[0013] (2) "Wearable Smart Device Management Module"

[0014] (3) "SOS Emergency Rescue Module"

[0015] (4) "AI Customer Service Module"

[0016] (5) "Personal Center Module"

[0017] (6) "Background Automatic Update Module"

[0018] · Content and Implementation of System Function Modules

[0019] (1) "User Health Information Module": Displays users' health information, such as health running trajectories, health levels, and risk levels, etc., so that users can timely understand their personal health conditions for "preventive medicine" or diagnosis and treatment.

[0020] (2) "Wearable Smart Device Management Module": Users can add or delete wearable smart devices, manage connected devices, and achieve the collection and transmission of health information data of wearable smart devices.

[0021] (3) "SOS Emergency Rescue Module": When users encounter emergencies, click the SOS button to automatically initiate a rescue request.

[0022] (4) "AI Customer Service Module": Users interact with the system AI customer service through the App to obtain health advice, answer common questions, etc.

[0023] (5) "Personal Center Module": Users can manage personal information (such as name, age, gender, etc.).

[0024] (6) "Background Automatic Update Module": ① Keep the system upgraded in real time; ② Clear redundant data to keep the system running smoothly.

[0025] (7) Integrate the above individual sub-modules to generate the "Mobile App" software system.

[0026] C. Development and Implementation Steps of Mobile App Software

[0027] · This module demonstrates the implementation steps of generating the "Mobile App" software system by using the currently optimal model (or software) selection.

[0028] · Front-end Development

[0029] (1) Use Flutter or React Native for UI design and development.

[0030] (2) Implement UI components such as health information, wearable smart device management, SOS rescue, AI customer service, and personal center.

[0031] · Back-end Development

[0032] (1) Use FastAPI to develop API interfaces to handle user requests.

[0033] (2) Use PostgreSQL for data storage to ensure data security.

[0034] · Testing and Optimization

[0035] (1) Conduct unit tests and integration tests to ensure the normal functionality of the system.

[0036] (2) Optimize the system performance to ensure real-time performance and response speed.

[0037] · Set up automatic background updates

[0038] (1) Keep the system updated in real-time.

[0039] (2) Clear redundant data to keep the system running smoothly.

[0040] · Integrate the above various components to generate the "Mobile App" software system.

[0041] 2.

Health Data Collection and Primary Health Data Generation Module

[0042] A. Objectives of this module

[0043] · Generate scientific and accurate "primary health data" to provide the source basic data for the user's "AI Health Management and First Aid System".

[0044] B. System function module settings of this module

[0045] · System function module settings:

[0046] (1) "Primary Health Data Model Generation Module"

[0047] (2) "Data Storage and Transmission Module"

[0048] (3) "Automatic Incremental Learning and Optimization Module"

[0049] · Content and implementation of system function modules

[0050] (1) "Primary Health Data Model Generation Module": ① Set up a "data preprocessing program" for handling missing values, denoising, and standardizing various health data (from wearable intelligent devices); ② Generate a "core model" through modeling, model training, and model optimization; ③ Integrate the "data preprocessing program" and the "core model" to generate a "primary health data model"; ④ Deep-process the user's various health data (from wearable intelligent devices) through the "primary health data model" to generate "primary health data".

[0051] (2) Implementation of the "Data Storage and Transmission Module": ① Establish a database and store relevant data in the database; ② Develop a FastAPI interface to ensure stable and real-time data transmission between this module and the mobile app and the backend.

[0052] (3) Implementation of the "Automatic Incremental Learning and Optimization Module": Continuously optimize the model through automatic incremental learning to improve the adaptability and accuracy of the system.

[0053] (4) Integrate the above various sub - modules to generate the software system of [Health Data Collection and Primary Health Data Generation Module].

[0054] C. Development and implementation steps of this module system

[0055] · Select the current optimal model (or software) for this module to demonstrate the implementation steps of the software system generated by [Health Data Collection and Primary Health Data Generation Module].

[0056] · Generation of "Primary Health Data Model"

[0057] (1) Set the "data pre - processing program"

[0058] a) Data source: various health data of users (from wearable intelligent devices).

[0059] b) Pre - process each data

[0060] Missing value handling: Use interpolation methods (such as linear interpolation or KNN interpolation) to supplement missing data and ensure data integrity.

[0061] Anomaly detection and denoising: Use algorithms (such as Isolation Forest) to detect abnormal data and use signal processing techniques (such as Kalman filtering, band - pass filtering) to remove noise.

[0062] Data standardization and normalization: Perform Z - score standardization or Min - Max normalization on health data to ensure that different health indicators are within a unified dimension range (such as normalized to [0, 1]).

[0063] ① Z - score standardization:

[0064]

[0065] Among them, μ is the mean of the data, σ is the standard deviation of the data, X is the original data, and X norm is the standardized data

[0066] ② Min - Max normalization:

[0067]

[0068] Among them, X min and x max are the minimum and maximum values of the data respectively, and X norm is the normalized data.

[0069] (2) Generation of "Core Model"

[0070] a) Basic Model Combination: This module uses TensorFlow + Keras + LSTM + XGBoost as the basic models, where:

[0071] Machine Learning and Modeling: Use TensorFlow + Keras + LSTM to develop a deep learning model for generating health data.

[0072] Ensemble Learning Framework: Use XGBoost to process health data.

[0073] b) Generation of "Core Model"

[0074] LSTM: LSTM processes time series health data and predicts the dynamic change trend of health data.

[0075] XGBoost: A deep processing model (for various health data) used to generate relevant health data.

[0076] Algorithm for Generating Primary Health Data (H): Generate primary health data such as heart rate, blood pressure, blood oxygen, etc. through the system of this module.

[0077]

[0078] Where: is age, is gender, is height, is weight, is past medical history, is exercise status, is sleep quality, is respiratory rate, is skin temperature, is heart rate, is blood pressure, is blood oxygen saturation, is electrocardiogram.

[0079] Model Training: Use historical health data to train the LSTM and XGBoost models to ensure that the models can generate accurate "primary health data".

[0080] (3) Integrate the "Data Preprocessing Program" and the "Core Model" to generate the "Primary Health Data Model".

[0081] (4) The user's various health data (from wearable intelligent devices) are deeply processed through the "primary health data model" to generate "primary health data".

[0082] · "Data storage and transmission module"

[0083] (1) Use PostgreSQL to establish a database and store relevant data in the database.

[0084] (2) Develop a FastAPI interface to ensure stable and real-time data transmission between this module and the front and back ends.

[0085] · "Automatic incremental learning and optimization module"

[0086] (1) Adopt XGBoost incremental training + mini-batch incremental learning.

[0087] (2) Set up automatic incremental learning.

[0088] (3) Through automatic incremental learning, continuously optimize the model to improve the adaptability and accuracy of the system.

[0089] · Integrate the "primary health data model", "data storage and transmission module" and "automatic incremental learning and optimization module" to generate the software system of

Health data collection and primary health data generation module

[0090] 3.

Comprehensive health index and future comprehensive health index calculation module

[0091] A. Objectives of this module

[0092] · Generate a scientific and accurate "comprehensive health index" to provide a data basis for the user's "AI health management and first aid".

[0093] · Generate a scientific and accurate "future comprehensive health index" (next 72 hours) to provide a data basis for AI to predict the user's future health status.

[0094] B. System function module settings of this module

[0095] · System function module settings

[0096] (1) "Comprehensive health index and future comprehensive health index calculation model generation module"

[0097] (2) "Data storage and transmission module"

[0098] (3) "Automatic incremental learning and optimization module"

[0099] · Content and implementation of system function modules

[0100] (1) "Comprehensive Health Index and Future Comprehensive Health Index Calculation Model Generation Module": ① Set the "data preprocessing program" for "primary health data"; ② Generate the "core model" through modeling, model training, and model optimization; ③ Integrate the "data preprocessing program" and the "core model" to generate the "Comprehensive Health Index and Future Comprehensive Health Index Calculation Model"; ④ The "primary health data" is deeply processed by the "Comprehensive Health Index and Future Comprehensive Health Index Calculation Model" to generate the "Comprehensive Health Index" and the "Future Comprehensive Health Index".

[0101] (2) "Data Storage and Transmission Module": ① Establish a database and store relevant data in the database; ② Develop a FastAPI interface to ensure stable and real-time data transmission between this module and the front and back ends.

[0102] (3) "Automatic Incremental Learning and Optimization Module": Continuously optimize the model through automatic incremental learning to improve the adaptability and accuracy of the system.

[0103] (4) Integrate the above individual sub-modules to generate the [Comprehensive Health Index and Future Comprehensive Health Index Calculation Module] software system.

[0104] C. Development and Implementation Steps of this Module System

[0105] · Demonstrate the implementation steps generated by the [Comprehensive Health Index and Future Comprehensive Health Index Calculation Module] software system by selecting the current optimal model (or software) for this module.

[0106] · Generation of the "Comprehensive Health Index and Future Comprehensive Health Index Calculation Model"

[0107] (1) Set the "data preprocessing program"

[0108] a) Data source: "Primary health data" (from the [Health Data Collection and Primary Health Data Generation Module]).

[0109] b) Split the "primary health data" according to the time series to generate a training set and a validation set.

[0110] (2) Generation of the "core model"

[0111] a) Model design and modeling: This system uses the LSTM + Attention + XGBoost model combination as the basic model of this module system, where:

[0112] LSTM: Used to process time series data and capture long-term dependencies in the time series.

[0113] Attention mechanism: Used to enhance the LSTM's ability to focus on data at critical time points and improve the model's prediction ability.

[0114] XGBoost: Used to improve the accuracy and stability of the model.

[0115] b) Model training and optimization, including the following steps:

[0116] Model initialization: Initialize the model structure using LSTM and the Attention mechanism.

[0117] XGBoost: Give full play to its advantages in processing high-dimensional health data and improve the accuracy and stability of the model through ensemble learning.

[0118] Loss function selection: Select an appropriate loss function, such as mean squared error (MSE), to measure the error of model prediction:

[0119]

[0120] where: y i is the actual value, is the predicted value, and N is the number of samples.

[0121] Training and optimization: Use the Adam optimizer for training and adopt L2 regularization and dropout techniques to prevent overfitting.

[0122] Cross-validation and hyperparameter tuning

[0123] ① Evaluate the generalization ability of the model through cross-validation and select the optimal hyperparameters.

[0124] ② Use Optuna for hyperparameter tuning to automatically optimize important hyperparameters in the model (such as learning rate, number of LSTM units, number of trees in XGBoost, etc.) to improve the accuracy and efficiency of the model

[0125] c) Generate algorithms for "Comprehensive Health Index" and "Future Comprehensive Health Index (72 hours)"

[0126] Calculation formula for "Comprehensive Health Index (CHI)":

[0127] ① Assume that the user's health data is X = {x 1 , x 2 , …, x n}, where x iIf each health indicator (such as heart rate, blood oxygen, etc.) is represented, then the "Comprehensive Health Index (CHI)" can be calculated through the following steps:

[0128] CHI = ML Model(X)

[0129] Among them, ML Model is a trained LSTM + Attention + XGBoost model.

[0130] ② The model inputs the user's health data X and outputs a value representing the "Comprehensive Health Index (CHI)". The numerical dimension range of the "Comprehensive Health Index" is between [0, 1].

[0131] Calculation formula for the "Future Comprehensive Health Index (FCHI)":

[0132] ① For the calculation of the "Future Comprehensive Health Index (FCHI)", an incremental learning method is used in combination with a time series prediction model generated by LSTM.

[0133] ② Assume X t is the health data at the current moment. The FCHI prediction formula is:

[0134] FCHI(t + 1) = LSTM(X t ) · Attention + XGBoost(X t )

[0135] Among them, LSTM(X t ) predicts the future health trend through a time series model.

[0136] ③ The Attention module strengthens the attention to key data points, and finally optimizes the prediction result through XGBoost to generate the "Future Comprehensive Health Index". The numerical dimension range of the "Future Comprehensive Health Index" is between [0, 1].

[0137] (3) Integrate the "Data Preprocessing Program" and the "Core Model" to generate the "Comprehensive Health Index and Future Comprehensive Health Index Calculation Model".

[0138] (4) The "Primary Health Data" undergoes in-depth processing by the "Comprehensive Health Index and Future Comprehensive Health Index Calculation Model" to generate the "Comprehensive Health Index" and the "Future Comprehensive Health Index".

[0139] · "Data Storage and Transmission Module"

[0140] (1) Use PostgreSQL to establish a database and store relevant data in the database.

[0141] (2) Develop the FastAPI interface to ensure stable and real-time data transmission between this module and the front and back ends.

[0142] · "Automatic Incremental Learning and Optimization Module"

[0143] (1) Adopt XGBoost incremental training + mini-batch incremental learning.

[0144] (2) Set up automatic incremental learning.

[0145] (3) Continuously optimize the model through automatic incremental learning to improve the adaptability and accuracy of the system.

[0146] · Integrate the "Comprehensive Health Index and Future Comprehensive Health Index Calculation Model", the "Data Storage and Transmission Module", and the "Automatic Incremental Learning and Optimization Module" to generate the [Comprehensive Health Index and Future Comprehensive Health Index Calculation Module] software system.

[0147] 4. [Digital Dynamic Health Operation Trajectory and Emergency Signal Generation Module]

[0148] A. Objectives of this module

[0149] · Generate the "Digital Dynamic Health Operation Trajectory and Emergency Signal" to achieve "AI Health Management and First Aid" for users.

[0150] B. System function module settings of this module

[0151] · System function module settings

[0152] (1) "Digital Dynamic Health Operation Trajectory and Emergency Signal Model Generation Module"

[0153] (2) "Data Storage and Transmission Module"

[0154] (3) "Automatic Incremental Learning and Optimization Module"

[0155] · Implementation of the content of the system function module

[0156] (1) "Digital Dynamic Health Trajectory and First Aid Signal Model Generation Module": ① Set the "data preprocessing program" for the "Comprehensive Health Index" and the "Future Comprehensive Health Index"; ② Generate the "core model" through modeling, model training, and model optimization; ③ Integrate the "data preprocessing program" and the "core model" to generate the "Digital Dynamic Health Trajectory and First Aid Signal Model"; ④ The "Comprehensive Health Index" and the "Future Comprehensive Health Index" are deeply processed by the "Digital Dynamic Health Trajectory and First Aid Signal Model" to generate the "Digital Dynamic Health Trajectory and First Aid Signal"; among which the "Digital Dynamic Health Trajectory" includes: "Digital Dynamic Threshold Matrix (including health level, 6 hours)", "Digital Dynamic Health Curve (including risk level, 72 hours)", "Digital Derived Dynamic Threshold Matrix (including health level, next 6 hours)", and "Digital Derived Dynamic Health Curve (including risk level, next 72 hours)".

[0157] (2) "Data Storage and Transmission Module": ① Establish a database and store relevant data in the database; ② Develop a FastAPI interface to ensure stable and real-time data transmission between this module and the front and back ends.

[0158] (3) "Automatic Incremental Learning and Optimization Module": Continuously optimize the model through automatic incremental learning to improve the adaptability and accuracy of the system.

[0159] (4) Integrate the above individual sub-modules to generate the [Digital Dynamic Health Trajectory and First Aid Signal Generation Module] software system.

[0160] C. Development and Implementation Steps of this Module System

[0161] · Use the currently optimal model (or software) selection in this module to demonstrate the implementation steps of the [Digital Dynamic Health Trajectory and First Aid Signal Generation Module] software system.

[0162] · Generation of the "Digital Dynamic Health Trajectory and First Aid Signal Model"

[0163] (1) Set the "data preprocessing program"

[0164] a) Data source: "Comprehensive Health Index and Future Comprehensive Health Index" (from the [Comprehensive Health Index and Future Comprehensive Health Index Calculation Module]).

[0165] b) Standardize, denoise, and perform time series processing on the "Comprehensive Health Index" and "Future Comprehensive Health Index" data to ensure the consistency of model input.

[0166] (2) Generation of the "core model"

[0167] a) Basic Model Combination: This module uses LSTM + Attention + XGBoost as the basic models, where:

[0168] The LSTM + Attention model is used for generating dynamic health trajectories.

[0169] The XGBoost model is used for health risk assessment.

[0170] b) Model Design and Modeling

[0171] LSTM layer: Captures the time-dependence of user health data and is suitable for processing time-series data.

[0172] Assume the input data is: X t =[X 1 , X 2 , …, X t , and the state update formula of LSTM is:

[0173] i t =σ(W i X t + U i h t-1 + b i )

[0174] f t =σ(W f X t + U f h t-1 + b f )

[0175] o t =σ(W o X t + U o h t-1 + b o )

[0176] c t =f t ·c t-1 + i t ·tanh(W c X t + U c h t-1 + b c )

[0177] h t =o t ·tanh(c t )

[0178] Among them: i t , f t , o t are the input gate, forget gate, and output gate respectively, c t is the cell state, and h t is the hidden state.

[0179] Attention mechanism: Enhance the attention of the LSTM model to important moments and improve the sensitivity to changes in health status.

[0180] Attention mechanism: Used to weight and focus on critical moments in the sequence, calculating the importance weights:

[0181]

[0182] Among them, e t = W e h t represents the attention score at each moment.

[0183] c) XGBoost model: Based on the "Comprehensive Health Index" and "Future Comprehensive Health Index" (the numerical dimension range is between [0, 1.0]) and the outputs of LSTM and Attention for in-depth processing.

[0184] d) Set the operation area division of the "Comprehensive Health Index and Future Comprehensive Health Index" of the digital dynamic threshold matrix and the digital derivative dynamic threshold matrix, divided into 5 areas: [0, 0.2], [0.2, 0.4], [0.4, 0.6], [0.6, 0.8], [0.8, 1.0].

[0185] e) Set the operation area division of the "Comprehensive Health Index" and "Future Comprehensive Health Index" of the digital dynamic health curve and the digital derivative dynamic health curve to be [0, 1.0].

[0186] f) According to the model output and combined with the medical expert threshold setting, the health levels are divided into: healthy, sub-healthy I, sub-healthy II, sub-healthy III, dangerous, and first aid, a total of 6 levels.

[0187] g) According to the model output and combined with the medical expert threshold setting, the risk levels are divided into: risk 0, risk I, risk II, risk III, risk IV, and risk V, a total of 6 levels.

[0188] h) Method for generating the "Digital Dynamic Health Operation Trajectory"

[0189] Use the sliding window method to calculate the health status within 72 hours before and after in real time.

[0190] Use exponential smoothing or Kalman filtering to process short-term fluctuations and ensure the smoothness of the long-term health trend.

[0191] Calculate the change trend of the risk index through the second derivative (rate of change acceleration) to help identify emergency signals:

[0192] Second derivative calculation: Δ 2 H t =(H t -H t-1 )-(H t-1 -H t-2 )

[0193] Map the values of the "Comprehensive Health Index" and the "Future Comprehensive Health Index" to the set health index area (HMA).

[0194] Correspond to the health level (HL), and generate health warnings and emergency signals based on the threshold changes.

[0195] Through the above, generate a digital dynamic threshold matrix (including health level, 6 hours), a digital dynamic health curve (including risk level, 72 hours), a digital derivative dynamic threshold matrix (including health level, future 6-hour prediction), and a digital derivative dynamic health curve (including risk level, future 72-hour prediction) respectively.

[0196] i) Model training and optimization

[0197] LSTM model training: Train the LSTM model based on historical health data and use a standard optimization algorithm (such as the Adam optimizer) for training.

[0198] Attention mechanism training: Enhance the ability of the LSTM model to focus on specific moments, and optimize the response to health fluctuations by adjusting weights during training.

[0199] XGBoost training: Train the

[0200] XGBoost model based on the output features of the LSTM and Attention models to evaluate the user's health level and risk level.

[0201] j) Regularization and optimization

[0202] L2 Regularization and Dropout: Incorporate L2 regularization and Dropout techniques into the LSTM and XGBoost models to prevent overfitting and improve the generalization ability of the models.

[0203] Early Stopping: Use the Early Stopping method to prevent overfitting during the training process.

[0204] Cross-Validation: Use the K-fold cross-validation method to evaluate the performance of the model on different datasets and ensure the stability and accuracy of the model.

[0205] (3) Integrate the "Data Preprocessing Program" and the "Core Model" to generate the "Digital Dynamic Health Operation Trajectory and First Aid Signal Model".

[0206] (4) The "Comprehensive Health Index" and the "Future Comprehensive Health Index" are deeply processed by the "Digital Dynamic Health Operation Trajectory and First Aid Signal Model" to generate the "Digital Dynamic Health Operation Trajectory and First Aid Signal", including: "Digital Dynamic Threshold Matrix (including health level, 6 hours)", "Digital Dynamic Health Curve (including risk level, 72 hours)", "Digital Derived Dynamic Threshold Matrix (including health level, future 6 hours)" and "Digital Derived Dynamic Health Curve (including risk level, future 72 hours)".

[0207] · "Data Storage and Transmission Module"

[0208] (1) Use PostgreSQL to establish a database and store relevant data in the database.

[0209] (2) Develop a FastAPI interface to ensure stable and real-time data transmission between this module and the front and back ends.

[0210] · "Automatic Incremental Learning and Adaptive Optimization Module"

[0211] (1) Adopt XGBoost incremental training + mini-batch incremental learning.

[0212] (2) Set up automatic incremental learning.

[0213] (3) Through automatic incremental learning, continuously optimize the model to improve the adaptability and accuracy of the system.

[0214] · Integrate the "Digital Dynamic Health Operation Trajectory and First Aid Signal Model", the "Data Storage and Transmission Module" and the "Automatic Incremental Learning and Optimization Module" to generate the software system of

Digital Dynamic Health Operation Trajectory and First Aid Signal Generation Module

[0215]

Sampling Frequency and Weight Dynamic Adjustment Module

[0216] A. Objectives of this module

[0217] · Dynamically adjust the sampling frequency of health data and the weight coefficients of each health indicator according to the user's health status, so as to generate more accurate health data for the user.

[0218] B. System function module settings of this module

[0219] · System function module settings

[0220] (1) "Sampling Frequency and Weight Dynamic Adjustment Model Generation Module"

[0221] (2) "Data Storage and Transmission Module"

[0222] (3) "Automatic Incremental Learning and Optimization Module"

[0223] · Content and implementation of system function modules

[0224] (1) "Sampling Frequency and Weight Dynamic Adjustment Model Generation Module": ① Set the "data preprocessing program" for identifying the "trigger signal", and identify the "health level", "risk level" and "first aid signal"; ② Generate the "core model" through modeling, model training and model optimization; ③ Integrate the "data preprocessing program" and the "core model" to generate the "sampling frequency and weight dynamic adjustment model"; ④ Through the "sampling frequency and weight dynamic adjustment model", generate the "sampling frequency and weight dynamic adjustment trigger signal and data", and trigger the "Mobile App" and

Comprehensive Health Index and Future Comprehensive Health Index Calculation Module

[0225] (2) "Data Storage and Transmission Module": ① Establish a database and store relevant data in the database; ② Develop a FastAPI interface to ensure stable and real-time data transmission between this module and the front and back ends.

[0226] (3) "Automatic Incremental Learning and Optimization Module": Continuously optimize the model through automatic incremental learning to improve the adaptability and accuracy of the system.

[0227] (4) Integrate the above individual sub-modules to generate the software system of

Sampling Frequency and Weight Dynamic Adjustment Module

[0228] C. System development and implementation steps of this module

[0229] · Use the currently optimal model (or software) selection in this module to demonstrate the implementation steps generated by the software system of

Sampling Frequency and Weight Dynamic Adjustment Module

[0230] ·Generated by "Sampling Frequency and Weight Dynamic Adjustment Model"

[0231] (1) Set the "Data Preprocessing Program" for identifying the "Trigger Signal"

[0232] a) Data source: "Health Level", "Risk Level" and "Emergency Signal" (from

Digital Dynamic Health Operation Trajectory and Emergency Signal Generation Module

[0233] b) Identify "Health Level", "Risk Level" and "Emergency Signal", etc.

[0234] (2) Generation of the "Core Model"

[0235] a) Model design and modeling

[0236] Model objective: Based on the user's health status (HL), automatically adjust the data sampling frequency, and use the Sigmoid function model to adjust the sampling frequency according to the change of health status and adjust the weight coefficients of various health indicators (such as heart rate HR, blood pressure BP, etc.) according to health risk factors and the rate of health change (Vt).

[0237] Input features: Health Level (HL), Rate of health change (V_t), Risk factors.

[0238] Output: Dynamic sampling frequency (F_t), Dynamic weight coefficient (W_i').

[0239] b) Model algorithm

[0240] Sigmoid function model: Used to dynamically adjust the sampling frequency according to the health level (HL).

[0242] Weight adjustment formula of the weighted dynamic adjustment model: Dynamically adjust the weight coefficients of each health indicator according to the rate of health change.

[0243] c) Sampling frequency adjustment:

[0244] Adjust the data sampling frequency in real time according to the user's health status (HL) and the rate of health change.

[0245] When the health status is at different levels (such as "healthy" or "dangerous"), adjust the sampling frequency to ensure the accuracy of data collection and processing.

[0246] d) Weight coefficient adjustment:

[0247] When the health status changes rapidly (e.g., the change rate of Vt is large), increase the weights of key health indicators (e.g., heart rate, blood pressure) so that the system can more sensitively reflect health risks.

[0248] Dynamically adjust the weight coefficients of each health indicator according to the changes in health risk factors to improve the sensitivity of the system to abnormal changes.

[0249] e) Model training (training model: supervised learning model)

[0250] Train the sampling frequency adjustment model: The goal is to train a function model that can dynamically adjust the sampling frequency according to the health status. The training method uses supervised learning, and historical health data is used to train the model to optimize parameters and thresholds.

[0251] Train the weight adjustment model: The goal is to dynamically adjust the weights according to the health change rate and the change in health level. The training method uses supervised learning, and training is carried out according to health data and emergency signals to optimize the weight adjustment factor.

[0252] f) Model optimization

[0253] Regularization: Perform L2 regularization on the model to prevent overfitting and ensure good generalization ability among different user groups.

[0254] Real-time data feedback and tuning: Adjust the model parameters according to the real-time health data feedback to ensure that the dynamic adjustment mechanism of the model can adapt to the user's health status at any time.

[0255] (3) Integrate the "data preprocessing program" and the "core model" to generate the "sampling frequency and weight dynamic adjustment model".

[0256] (4) "Health level (HL)", "risk signal" and "emergency signal" activate the [sampling frequency and weight dynamic adjustment model] to generate the "sampling frequency and weight dynamic adjustment trigger signal and data", and trigger the "mobile phone App" and the [comprehensive health index and future comprehensive health index calculation module] in real time for dynamic adjustment of sampling frequency and weight.

[0257] · "Data storage and transmission module"

[0258] (1) Use PostgreSQL to establish a database and store relevant data in the database.

[0259] (2) Develop a FastAPI interface to ensure stable and real-time data transmission between this module and the front and back ends.

[0260] · "Incremental Learning and Optimization Module"

[0261] (1) Adopt XGBoost incremental training + mini-batch incremental learning.

[0262] (2) Set up automatic incremental learning.

[0263] (3) Through automatic incremental learning, continuously optimize the model to improve the adaptability and accuracy of the system.

[0264] · Integrate the "Sampling Frequency and Weight Dynamic Adjustment Model", "Data Storage and Transmission Module" and "Automatic Incremental Learning and Optimization Module" to generate the [Sampling Frequency and Weight Dynamic Adjustment Module] software system.

[0265] 6. [Visual Dynamic Health Operation Trajectory Generation Module]:

[0266] A. Objectives of this module

[0267] · Generate an interface of "Visual Dynamic Health Operation Trajectory" that is easy for users to understand;

[0268] · Through this interface, users can intuitively understand all health monitoring and prediction warning information such as their health status, "health level" and "risk level", and at the same time provide necessary technical support for medical staff and health management personnel to master the user's health status, preventive medicine and diagnosis and treatment.

[0269] B. System function module settings of this module

[0270] · System function module settings

[0271] (1) "Visualization Module"

[0272] (2) "Data Storage and Transmission Module"

[0273] · Content and implementation of system function modules

[0274] (1) "Visualization Module": ① Set a "data preprocessing program" to process the information data received from the [Digital Dynamic Health Operation Trajectory and First Aid Signal Generation Module] into the format required for visualization; ② Integrate the "data preprocessing program" with relevant visualization processing software to generate the "Visualization Module"; ③ The "Digital Dynamic Health Operation Trajectory" is deeply processed through the "Visualization Module" to generate "Visual Dynamic Health Operation Trajectory" and "Visual Health Monitoring, Prediction and Warning Information"; the "Visual Dynamic Health Operation Trajectory" includes visualization charts such as "Dynamic Threshold Matrix Diagram", "Dynamic Health Curve Diagram", "Derivative Dynamic Threshold Matrix Diagram" and "Derivative Dynamic Health Curve Diagram".

[0275] (2) "Data Storage and Transmission Module": ① Establish a database and store relevant data in the database; ② Develop a FastAPI interface to ensure stable and real-time data transmission between this module and the front and back ends.

[0276] (3) Integrate the above individual sub-modules to generate the software system of [Visualization Dynamic Health Operation Trajectory Generation Module].

[0277] C. Development and Implementation Steps of this Module System

[0278] · This module uses the currently optimal software selection to demonstrate the implementation steps of the software system generated by [Visualization Dynamic Health Operation Trajectory Generation Module].

[0279] · Generated by the "Visualization Module"

[0280] (1) Set the "Data Preprocessing Program"

[0281] a) Data source: "Digital Dynamic Health Operation Trajectory (including health monitoring and prediction and early warning information such as health level and risk level)" (from [Digital Dynamic Health Operation Trajectory and First Aid Signal Generation Module])

[0282] b) Process the data of "Digital Dynamic Health Operation Trajectory (including health monitoring and prediction and early warning information such as health level and risk level)" into the format required for visualization.

[0283] (2) Integrate the "Data Preprocessing Program" with relevant visualization processing software (here using Plotly / D3.js) to generate the "Visualization Module".

[0284] (3) Generation of "Visualization Dynamic Health Operation Trajectory" and "Visualization Health Monitoring, Prediction and Early Warning Information"

[0285] a) Through the "Visualization Module", generate charts of "Visualization Dynamic Health Operation Trajectory" such as "Dynamic Threshold Matrix Chart", "Dynamic Health Curve Chart", "Derivative Dynamic Threshold Matrix Chart" and "Derivative Dynamic Health Curve Chart".

[0286] b) Through the "Visualization Module", generate "Visualization Health Monitoring, Prediction and Early Warning Information".

[0287] c) Provide a user interaction interface to allow users to view the health operation trajectory and browse detailed data.

[0288] · "Data Storage and Transmission Module"

[0289] (1) Use PostgreSQL to establish a database and store relevant data in the database.

[0290] (2) Develop a FastAPI interface to ensure stable and real-time data transmission between this module and the front-end and back-end.

[0291] · Integrate the "Visualization Module" and the "Data Storage and Transmission Module" to generate the software system of [Visualization Dynamic Health Operation Trajectory Generation Module].

[0292] 7. [Displacement Module]:

[0293] A. Objectives of this module

[0294] · Quickly obtain the user's current precise location when the emergency signal is triggered;

[0295] · Transmit the location of the emergency user in real-time to the [Language Interaction Module] to provide the precise location of the emergency user when linking the user's emergency contacts or the 120 emergency center.

[0296] B. System function module settings of this module

[0297] · System function module settings

[0298] (1) "Displacement Model Generation Module"

[0299] (2) "Data Storage and Transmission Module"

[0300] (3) "Automatic Incremental Learning and Optimization Module"

[0301] · Content and implementation of system function modules

[0302] (1) "Displacement Model Generation Module": ① Set the "data preprocessing program" for identifying the "emergency signal". ② Generate the "core model" through modeling, training, and optimization; ③ Integrate the "data preprocessing program" and the "core model" to generate the "displacement model". ④ Obtain the real-time precise location of the emergency user through the "displacement model".

[0303] (2) "Data Storage and Transmission Module": ① Establish a database and store relevant data in the database; ② Develop a FastAPI interface to ensure stable and real-time data transmission between this module and the front-end and back-end.

[0304] (3) "Automatic Incremental Learning and Optimization Module": Continuously optimize the model through automatic incremental learning to improve the adaptability and accuracy of the system.

[0305] (4) Integrate the above individual sub-modules to generate the software system of [Displacement Module].

[0306] C. System development and implementation steps of this module

[0307] · Use the currently optimal model (or software) selection in this module to demonstrate the implementation steps of the software system generated by [Displacement Module].

[0308] · Generation of "displacement model"

[0309] (1) Set "data preprocessing program"

[0310] a) Data source: "emergency signal" (from

Digital dynamic health operation trajectory and emergency signal generation module

[0311] b) Identify "emergency signal".

[0312] (2) Generation of "core model"

[0313] a) Basic model combination: This module uses LSTM+CNN as the basic model.

[0314] b) Build and train a deep learning model

[0315] LSTM model design

[0316] ① Input layer: Input time series data (such as GPS position, accelerometer, gyroscope data).

[0317] ② LSTM layer: Several LSTM layers are used to process time series data and learn the temporal pattern of user movement.

[0318] ③ Output layer: Output the user's current position and predict the future position.

[0319] ④ Input layer: Input time series data (such as GPS position, accelerometer, gyroscope data).

[0320] ⑤ LSTM layer: Several LSTM layers are used to process time series data and learn the temporal pattern of user movement.

[0321] ⑥ Output layer: Output the user's current position and predict the future position.

[0322] CNN model design

[0323] ① Input layer: Input multi-dimensional data (such as GPS data, accelerometer, and gyroscope data), and convert it into a format suitable for CNN processing.

[0324] ② Convolutional layer: Extract spatial features through the convolutional layer.

[0325] ③ Pooling layer: Perform downsampling to reduce the computational amount and extract the main features.

[0326] ④ Fully connected layer: Further process the extracted features.

[0327] ⑤ Output layer: Predict the current position and future position.

[0328] c) Training process

[0329] Data preparation: Divide the processed historical trajectory data into a training set and a test set.

[0330] Train the model: Use the training set data to train the LSTM and CNN models. Use the Adam optimizer, and the loss function is the mean squared error (MSE).

[0331] Model evaluation: Use the test set to evaluate the training results and ensure the accuracy of the model.

[0332] d) Deep Reinforcement Learning (DRL)

[0333] Goal: Optimize the displacement prediction strategy, automatically adjust the sampling frequency and position prediction strategy to cope with the dynamically changing environment.

[0334] Training process

[0335] ① State space: The user's current position, speed, acceleration, etc.

[0336] ② Action space: Adjust the prediction strategy, optimize the sampling frequency, etc.

[0337] ③ Reward function: Calculate the reward based on the positioning accuracy and real-time response time.

[0338] ④ Training: Use Q-learning or Deep Q-Network (DQN) to optimize the behavior of the system.

[0339] (3) Integrate the "data preprocessing program" and the "core model" to generate the "displacement model".

[0340] (4) When activated by the "emergency signal", generate the user's real-time accurate position through the "displacement model".

[0341] · "Data storage and transmission module"

[0342] (1) Establish a database using PostgreSQL and store the relevant data in the database.

[0343] (2) Develop a FastAPI interface to ensure stable and real-time data transmission between this module and the front and back ends.

[0344] · "Automatic incremental learning and optimization module"

[0345] (1) Adopt XGBoost incremental training + mini-batch incremental learning.

[0346] (2) Set up incremental learning automatically.

[0347] (3) Continuously optimize the model through incremental learning automatically to improve the adaptability and accuracy of the system.

[0348] · Integrate the "displacement model", "data storage and transmission module" and "incremental learning and optimization module" to generate the [Displacement Module] software system.

[0349] 8. [Language Interaction Module]

[0350] A. Objectives of this module

[0351] · Through this module, realize the interaction between the system and the user, and provide technical support for the user's "AI health management";

[0352] · Through this module, realize the interaction between the system and the user's emergency contacts or the 120 emergency center, and provide technical support for the user to obtain timely first aid services.

[0353] B. Setting of system function modules of this module

[0354] · Setting of system function modules

[0355] (1) "Language Interaction Model Module"

[0356] (2) "Data Storage and Transmission Module"

[0357] (3) "Incremental Learning and Optimization Module"

[0358] · Content and implementation of system function modules

[0359] (1) "Language Interaction Model Generation Module": ① Set the "data preprocessing program": process and classify data such as "first aid signals", first aid user locations, "visualized dynamic health operation trajectories" and "visualized health monitoring, prediction and early warning information" respectively. ② Generate the "core model" through modeling, model training and optimization; ③ Integrate the "data preprocessing program" and the "core model" to generate the "language interaction model". ④ The "language interaction model" has functions of natural language interaction and linkage, speech recognition, dialogue management, emergency signal determination and first aid linkage; they are used to realize the linkage between the system and user health management and provide real-time health status feedback, dynamically adjust relevant parameters of the language format required by the user voice conversion system, generate health consultations and personalized suggestions, judge whether the user is in a state of helplessness in an emergency based on the user's health data and voice feedback, and automatically link the user's emergency contacts and the 120 emergency center.

[0360] (2) "Data Storage and Transmission Module": ① Establish a database and store relevant data in the database; ② Develop a FastAPI interface to ensure stable and real-time data transmission between this module and the front and back ends.

[0361] (3) "Automatic Incremental Learning and Optimization Module": Continuously optimize the model through automatic incremental learning to improve the adaptability and accuracy of the system.

[0362] (4) Integrate the above individual sub-modules to generate the [Language Interaction Module] software system.

[0363] C. Development and Implementation Steps of this Module System

[0364] · Select the current optimal model (or software) for this module to demonstrate the implementation steps of the [Language Interaction Module] software system.

[0365] · Generation of "Language Interaction Model"

[0366] (1) Set the "Data Preprocessing Program"

[0367] a) Data Sources:

[0368] "Emergency Signals" (from the [Digital Dynamic Health Operation Trajectory and Emergency Signal Generation Module]).

[0369] Emergency User Location (from the [Displacement Module]).

[0370] Data such as "Visualized Dynamic Health Operation Trajectory" and "Visualized Health Monitoring, Prediction and Early Warning Information" (from the [Visualized Dynamic Health Operation Trajectory Generation Module]).

[0371] b) Process and classify data such as "Emergency Signals", Emergency User Location, "Visualized Dynamic Health Operation Trajectory", and "Visualized Health Monitoring, Prediction and Early Warning Information" respectively.

[0372] (2) Generation of "Core Model"

[0373] a) Model Design and Modeling

[0374] Model Objectives

[0375] ① Speech Recognition and Processing: Convert the user's speech into the required language format of the system and perform efficient speech recognition through DeepSpeech.

[0376] ② Dialogue Generation and Management: Generate smooth and natural health consultation dialogues based on GPT / BERT and perform state management through Rasa / Dialogflow.

[0377] ③ Emergency signal determination and automatic response: Combine user health data and voice feedback to determine whether the user is in a state of helplessness during first aid. Once it is determined that the user is in a state of helplessness during first aid, the system immediately activates the first aid linkage.

[0378] Input features

[0379] ① Health data: Include "visualized dynamic health operation trajectory", "health monitoring and prediction warning information", "first aid signal", and the location of the first aid user, etc.

[0380] ② Voice data: The text input by the user's voice.

[0381] ③ First aid signal feature: The determination of the first aid state based on health data and voice feedback.

[0382] Output

[0383] ① Real-time health feedback: Provide real-time health advice according to the user's health condition.

[0384] ② Emergency response signal: Trigger the first aid alarm and link the user's emergency contacts or the 120 emergency center.

[0385] ③ Health management advice: Generate a personalized health management plan based on the conversation.

[0386] b) Model algorithm

[0387] Speech recognition: DeepSpeech is used for speech to digital data or text.

[0388] Natural language processing: GPT / BERT is used to generate health advice, and Rasa / Dialogflow is used to manage the conversation flow.

[0389] Emergency signal determination: Based on health data and voice input, the first aid signal is determined through a rule engine and XGBoost.

[0390] Incremental learning: Use online SGD to optimize the health data processing model and dynamically update the conversation management model.

[0391] c) Model training and optimization

[0392] Data collection and preprocessing

[0393] ① Voice data: Collect the user's voice input and process it through DeepSpeech to extract digital data or text data.

[0394] ② Health data: The user's health data is transmitted in real time and undergoes standardized processing.

[0395] ③ Emergency signal data: Extract emergency signals from historical health data for model training.

[0396] Model training

[0397] ① Speech recognition model training: Use a large amount of health-related speech data for

[0398] DeepSpeech model training.

[0399] ② Dialogue generation and management model: Fine-tune through GPT / BERT to provide personalized health advice.

[0400] d) Model functions

[0401] Speech recognition and health consultation

[0402] ① The user inputs through speech, and the system uses DeepSpeech to convert the speech into the language format required by the system, and dynamically adjusts relevant parameters based on the user's speech feedback.

[0403] ② GPT / BERT generates personalized health advice and synthesizes speech through Tacotron 2 to return to the user.

[0404] Emergency signal determination and response

[0405] ① The system uses a rule engine and XGBoost to determine whether the user is in a state of helplessness in an emergency through health data and the user's speech feedback.

[0406] ② If it is confirmed that the user is in an emergency state, the system automatically links the user's emergency contacts and the emergency center through the Twilio / Agora API.

[0407] ③ Real-time communication and emergency linkage: Achieve real-time communication with emergency contacts and the 120 emergency center through the Twilio / Agora API to ensure timely response.

[0408] (3) Integrate the "data preprocessing program" and the "core model" to generate the "language interaction model".

[0409] (4) The "AI Health Management and Emergency System" provides users with comprehensive "personalized health management and automatic emergency services" through the "language interaction model".

[0410] · "Data storage and transmission module"

[0411] (1) Establish a database using PostgreSQL and store relevant data in the database.

[0412] (2) Develop a FastAPI interface to ensure stable and real-time data transmission between this module and the front and back ends.

[0413] · "Automatic Incremental Learning and Optimization Module"

[0414] (1) Adopt XGBoost incremental training + mini-batch incremental learning.

[0415] (2) Set up automatic incremental learning.

[0416] (3) Continuously optimize the model through automatic incremental learning to improve the adaptability and accuracy of the system.

[0417] · Integrate the "Language Interaction Model", "Data Storage and Transmission Module" and "Automatic Incremental Learning and Optimization Module" to generate the [Language Interaction Module] software system.

[0418] This solution realizes "health monitoring, prediction and early warning, personalized health management and first aid" services for users based on the health data collected by wearable intelligent devices through AI prediction + medical rules.

Claims

1. An AI health management and emergency system based on wearable smart devices, comprising: (1) "Mobile App" (2) [Health data collection and primary health data generation module] (3) [Comprehensive health index and future comprehensive health index calculation module] (4) [Digital dynamic health running trajectory and emergency signal generation module] (5) [Sampling frequency and weight dynamic adjustment module] (6) [Visual dynamic health operation trajectory generation module] (7)【Displacement module】 (8)【Language interaction module】 2. The system according to claim 1, wherein the "mobile phone app": (1) System objectives Provide user-side support for user AI health management and first aid. (2) System function module settings "User health information module" "Wearable smart device management module" "SOS rapid rescue module" "AI customer service module" "Personal Center Module" "Background automatic update module" (3) System functional module content and implementation: "User health information module": displays the user's health information, such as health running track, health level and risk level, so that users can timely grasp their personal health status, "preventive medical care" or diagnosis and treatment; "Wearable smart device management module": users can add or delete wearable smart devices, manage connected devices, and realize the collection and transmission of health information data of wearable smart devices; "SOS rapid rescue module": when the user encounters an emergency, click the SOS button to automatically initiate a request for help; "AI Customer Service Module": Users interact with the system's AI customer service through the App to get health advice, answers to common questions, etc.; "Personal Center Module": Users can manage personal information (such as name, age, gender, etc.); "Background automatic update module": ① Keep the system updated in real time; ② Clear redundant data to keep the system running smoothly; Integrate the above modules to generate a "mobile phone app" software system. These features ensure that the system obtains user health information data in a timely manner, and that users obtain their own health information data in a timely manner, providing client technical support for user AI health management and first aid.

3. The system according to claim 1, wherein the [health data collection and primary health data generation module]: (1) Objectives of this module Generate scientific and accurate "primary health data" to provide source basic data for users' "AI health management and emergency system". (2) System function module settings of this module: "Primary health data model generation module" "Data storage and transmission module" "Automatic incremental learning and optimization module" (3) Content and implementation of this module system function module "Primary health data model generation module": ① Set up a "data preprocessing program" for missing value processing, denoising, and standardization of various health data (derived from wearable smart devices); ② Generate a "core model" through modeling, model training, and model optimization; ③ Integrate the "data preprocessing program" and the "core model" to generate a "primary health data model"; ④ The user's various health data (derived from wearable smart devices) are deeply processed through the "primary health data model" to generate "primary health data"; "Data storage and transmission module": ① Establish a database and store relevant data in the database; ② Develop FastAPI interface to ensure stable and real-time data transmission between this module and the mobile app and backend; "Automatic incremental learning and optimization module": Through automatic incremental learning, the model is continuously optimized to improve the adaptability and accuracy of the system; Integrate the above modules to generate the [health data collection and primary health data generation module] software system. These features ensure the accurate collection, processing and unified standardization of health data, providing a reliable data foundation for subsequent health analysis and prediction.

4. The system according to claim 1, wherein [comprehensive health index and future comprehensive health index calculation module]: (1) Objectives of this module Generate a scientific and accurate "comprehensive health index" to provide a data basis for users' AI health management and first aid; Generate a scientific and accurate "Future Comprehensive Health Index" (next 72 hours) to provide a data basis for AI to predict the user's future health status. (2) System function module settings of this module "Comprehensive health index and future comprehensive health index calculation model generation module" "Data storage and transmission module" "Automatic incremental learning and optimization module" (3) Content and implementation of this module system function module "Comprehensive health index and future comprehensive health index calculation model generation module": ① set the "data preprocessing program" for "primary health data"; ② generate the "core model" through modeling, model training, and model optimization; ③ integrate the "data preprocessing program" and the "core model" to generate the "comprehensive health index and future comprehensive health index calculation model"; ④ "primary health data" is deeply processed through the "comprehensive health index and future comprehensive health index calculation model" to generate the "comprehensive health index" and the "future comprehensive health index"; "Data storage and transmission module": ① Establish a database and store relevant data in the database; ② Develop FastAPI interface to ensure stable and real-time data transmission between this module and the front and back ends; "Automatic incremental learning and optimization module": Through automatic incremental learning, the model is continuously optimized to improve the adaptability and accuracy of the system; Integrate the above sub-modules to generate the [comprehensive health index and future comprehensive health index calculation module] software system. These features help generate a scientific and accurate "comprehensive health index" and "future comprehensive health index" from "multi-dimensional single health data", providing a data foundation for AI health management and first aid, while improving the accuracy and stability of health monitoring and prediction.

5. The system according to claim 1, wherein [digital dynamic health running trajectory and emergency signal generation module]: (1) Objectives of this module Generate "digital dynamic health operation trajectory and emergency signals" to realize users' "AI health management and emergency treatment". (2) System function module settings of this module "Digital dynamic health running trajectory and emergency signal model generation module" "Data storage and transmission module" "Automatic incremental learning and optimization module" (3) Content and implementation of this module system function module "Digital dynamic health operation trajectory and emergency signal model generation module": ① set the "data preprocessing program" for the "comprehensive health index" and the "future comprehensive health index"; ② generate the "core model" through modeling, model training, and model optimization; ③ integrate the "data preprocessing program" and the "core model" to generate the "digital dynamic health operation trajectory and emergency signal model"; ④ the "comprehensive health index" and the "future comprehensive health index" are deeply processed by the "digital dynamic health operation trajectory and emergency signal model" to generate the "digital dynamic health operation trajectory and emergency signal"; the "digital dynamic health operation trajectory" includes: "Digital dynamic threshold matrix (including health level, 6 hours)", "Digital dynamic health curve (including risk level, 72 hours)", "Digital derived dynamic threshold matrix (including health level, next 6 hours)" and "Digital derived dynamic health curve (including risk level, next 72 hours)"; "Data storage and transmission module implementation": ① Establish a database and store relevant data in the database; ② Develop FastAPI interface to ensure stable and real-time data transmission between this module and the front and back ends; "Automatic incremental learning and optimization module implementation": Through automatic incremental learning, the model is continuously optimized to improve the adaptability and accuracy of the system; Integrate the above sub-modules to generate the [digital dynamic health running trajectory and emergency signal generation module] software system. These features ensure that the system generates scientific and accurate user "digital dynamic health operation trajectory and emergency information "No. 1" provides a direct path for the system to conduct "AI health management and first aid" for users, realizing "AI health management and first aid" for users from a technical level.

6. The system according to claim 1, wherein [sampling frequency and weight dynamic adjustment module]: (1) Objectives of this module Dynamically adjust the sampling frequency of health data and the weight coefficient of each health indicator according to the user's health status to generate more accurate health data for the user. (2) System function module settings of this module "Model generation module for dynamic adjustment of sampling frequency and weight" "Data storage and transmission module" "Automatic incremental learning and optimization module" (3) Content and implementation of this module system function module · "Sampling frequency and weight dynamic adjustment model generation module": ① Set the "data preprocessing program" to identify the "trigger signal" to identify the "health level", "risk level" and "first aid signal"; ② Generate the "core model" through modeling, model training and model optimization; ③ Integrate the "data preprocessing program" and the "core model" to generate the "sampling frequency and weight dynamic adjustment model"; ④ Generate the "sampling frequency and weight dynamic adjustment trigger signal and data" through the "sampling frequency and weight dynamic adjustment model", and trigger the "mobile phone app" and [comprehensive health index and future comprehensive health index calculation module] in real time to dynamically adjust the sampling frequency and weight; "Data storage and transmission module": ① Establish a database and store relevant data in the database; ② Develop FastAPI interface to ensure stable and real-time data transmission between this module and the front and back ends; "Automatic incremental learning and optimization module": Through automatic incremental learning, the model is continuously optimized to improve the adaptability and accuracy of the system; Integrate the above sub-modules to generate the [sampling frequency and weight dynamic adjustment module] software system. These features ensure that the system generates a scientific sampling frequency and dynamic adjustment mechanism for weight coefficients, providing technical support for generating more accurate health data for users.

7. The system according to claim 1, wherein [Visualized health running trajectory generation module]: (1) Objectives of this module Generate a user-friendly "visualized dynamic health running trajectory" interface; Through this interface, users can intuitively understand their health status, "health level" and "risk level" and other health monitoring and prediction and early warning information, while providing necessary technical support for medical personnel and health management personnel to understand the user's health status, preventive medical care and diagnosis and treatment. (2) System function module settings of this module "Visualization module" "Data storage and transmission module" (3) Content and implementation of this module system function module "Visualization module": ① Set the "data preprocessing program" to process the received information data of the [digital dynamic health operation trajectory and emergency signal generation module] into the format required for visualization; ② Integrate the "data preprocessing program" with related visualization processing software to generate the "visualization module"; ③ The "digital dynamic health operation trajectory" is deeply processed by the "visualization module" to generate the "visualized dynamic health operation trajectory" and "visualized health monitoring, prediction and early warning information"; the "visualized dynamic health operation trajectory" includes the "dynamic threshold matrix diagram", "dynamic health curve diagram", "derived dynamic threshold matrix diagram" and "derived dynamic health curve diagram" and other visualization charts; "Data storage and transmission module": ① Establish a database and store relevant data in the database; ② Develop FastAPI interface to ensure stable and real-time data transmission between this module and the front and back ends; Integrate the above sub-modules to generate the [Visual Dynamic Health Operation Trajectory Generation Module] software system. These features ensure that the system provides users with a scientific, intuitive and visual health information interface, providing the most direct and effective means for users to understand their health status, conduct "preventive medical care" and cooperate with doctors in diagnosis and treatment.

8. The system of claim 1, wherein the displacement module: (1) Objectives of this module When an emergency signal is triggered, quickly obtain the user's current precise location; Transmit the emergency user's location to the [language interaction module] in real time, so as to provide the emergency user's precise location when linking the user's emergency contact or 120 emergency center. (2) System function module settings of this module "Displacement model generation module" "Data storage and transmission module" "Automatic incremental learning and optimization module" (3) Content and implementation of this module system function module "Displacement model generation module": ① Set up a "data preprocessing program" to identify "first aid signals". ② Generate a "core model" through modeling, training, and optimization; ③ Integrate the "data preprocessing program" with the "core model" to generate a "displacement model". ④ Obtain the real-time and accurate location of the first aid user through the "displacement model"; "Data storage and transmission module": ① Establish a database and store relevant data in the database; ② Develop FastAPI interface to ensure stable and real-time data transmission between this module and the front and back ends; "Automatic incremental learning and optimization module": Through automatic incremental learning, the model is continuously optimized to improve the adaptability and accuracy of the system; Integrate the above sub-modules to generate the [displacement module] software system. These features ensure that the system can obtain the user's precise location when the customer is in need of emergency treatment, and provide technical support for the user's emergency services.

9. The system according to claim 1, wherein the [language interaction module]: (1) Objectives of this module Through this module, the system and users can interact with each other and provide technical support for users' "AI health management"; Through this module, the system can interact with the user's emergency contact or 120 emergency center to provide technical support for users to obtain timely emergency services. (2) System function module settings of this module "Language interaction model generation module" "Data storage and transmission module" "Automatic incremental learning and optimization module" (3) Content and implementation of this module system function module ·"Language interaction model generation module": ①Set up "data preprocessing program": process and classify data such as "first aid signal", first aid user location, "visualized dynamic health operation trajectory" and "visualized health monitoring, prediction and early warning information" respectively. ②Generate a "core model" through modeling, model training and optimization; ③Integrate the "data preprocessing program" and the "core model" to generate a "language interaction model". ④The "language interaction model" has the functions of natural language interaction and linkage, voice recognition, dialogue management, emergency signal judgment and first aid linkage; they are used to realize the linkage between the system and user health management and provide real-time health status feedback, dynamically adjust relevant parameters of the language format required by the user voice conversion system, generate health consultation and personalized suggestions, judge whether the user is in a helpless state of first aid based on the user's health data and voice feedback, and automatically link the user's emergency contacts and the 120 emergency center; "Data storage and transmission module": ① Establish a database and store relevant data in the database; ② Develop FastAPI interface to ensure stable and real-time data transmission between this module and the front and back ends; "Automatic incremental learning and optimization module": Through automatic incremental learning, the model is continuously optimized to improve the adaptability and accuracy of the system; Integrate the above modules to generate the [language interaction module] software system. These features ensure that the system provides smooth assistance to users' "AI health management and first aid" services.