Intelligent analysis and management method and system for three-high health data

Abstract

The invention discloses a three-high health data intelligent analysis and management method and system, and belongs to the technical field of three-high health, and the method comprises the steps: a platform side builds a user side, and the user side comprises a display module, a user module, a voice module, a collection module and an analysis module; a user obtains three-high analysis data through the collection module, the analysis module analyzes the three-high analysis data, and an analysis result is displayed to the user through the display module. The platform side carries out optimization analysis on the correction mode of the three-high data in real time, and when it is judged that the correction mode needs to be optimized, optimization adjustment is carried out on the correction model of the corresponding user; according to the invention, multiple data acquisition modes such as docking monitoring equipment, voice input and handwriting recognition are introduced, so that the operation difficulty is greatly reduced, and a user can easily and accurately complete acquisition of three-high data; according to the method, the user side is allowed to be bound with multiple users, and the three-high data of the multiple users can be comprehensively analyzed.

Description

Technical Field

[0001] The present invention belongs to the field of three-high health technology, and specifically relates to a three-high health data intelligent analysis management method and system. Background Art

[0002] As modern society pays more and more attention to health management, the three highs (high blood pressure, high blood sugar, and high blood lipids) are common chronic diseases, and their effective monitoring and management are particularly important. However, most of the mainstream health monitoring devices on the market currently rely on smart devices for operation, which poses a significant obstacle to people who are illiterate or cannot use smart devices; especially most of the people with the three highs are elderly people, who often lack convenient and effective monitoring methods and find it difficult to understand their health status in real time, which in turn affects the prevention and control of diseases.

[0003] In view of this situation, the present invention provides a method and system for intelligent analysis and management of the three high health data, aiming to provide comprehensive health management services for people who are illiterate and cannot use smart devices in a simple and intuitive way. Summary of the invention

[0004] In order to solve the problems existing in the above scheme, the present invention provides a method and system for intelligent analysis and management of three-high health data.

[0005] The purpose of the present invention can be achieved through the following technical solutions: A method for intelligent analysis and management of three-high health data, the method comprising: Step 1: The platform establishes a user terminal, which includes a display module, a user module, a voice module, a collection module and an analysis module; The display module is used to display the received data; The voice module is used for voice broadcasting and voice control, and includes a voice broadcasting unit and a voice control unit. The voice broadcasting unit is used for voice broadcasting of data within a preset range; the voice control unit is used for the user to perform voice control on the user terminal; The acquisition module is used to collect the user's three high data, and correct the collected three high data to obtain the user's three high analysis data, and send the three high analysis data to the display module; The user module is used for user management; The analysis module is used to analyze the three high analysis data of the user, determine the analysis result of the user, and send the analysis result to the display module.

[0006] Furthermore, the preset range is set as follows: Determine the voice broadcast requirement, identify the data items corresponding to the voice broadcast requirement; filter and integrate the data items into an initial range; When the user has a need to adjust the voice broadcast, the initial range is displayed to the user, and the user selects a data item that needs to be voice broadcast within the initial range, and the data item selected by the user is marked as a data broadcast item; each data broadcast item is combined into a preset range; When the user has no need to adjust the voice broadcast, the initial range is marked as a preset range.

[0007] Furthermore, the method for collecting the three high data is: Obtain the user's monitoring method for the three high data, identify the monitoring device corresponding to the monitoring method, conduct access evaluation on the monitoring device, and determine whether data connection can be performed; When it is determined that data docking can be performed, docking with the monitoring device is performed to obtain the three high data collected by the monitoring device in real time; When it is determined that data docking cannot be performed, the user selects a preset data entry method to enter the three high data, and the data entry method includes voice entry and handwriting entry.

[0008] Furthermore, the method for correcting the three high data includes: Acquire the three high historical data, and determine the personnel characteristics according to the three high historical data; classify the personnel characteristics to form various characteristic classifications; Establishing a corresponding correction model according to the feature classification, wherein the correction model is used to correct the three high data; Identify user information, and match a corresponding correction model for the user according to the user information; correct the user's three-high data through the correction model to obtain three-high analysis data.

[0009] Further, methods for classifying personnel characteristics include: Identify the technical data corresponding to each current monitoring device, screen the technical data, and obtain reserve technical data; classify the reserve technical data to form a technical classification; Conduct simulation analysis on personnel characteristics according to technical classification to determine whether the corresponding personnel characteristics meet the merger requirements; When it is judged that the requirements for merging are not met, the corresponding personnel characteristics are not allocated in the same characteristic classification; When it is determined that the merger requirements are met, the corresponding personnel characteristics are classified into one category to obtain the characteristic classification corresponding to the technical classification.

[0010] Furthermore, the method for performing user management includes: Determine the number of users of the user terminal, set a corresponding number of sub-users according to the number of users, and name the sub-users; Add corresponding sub-user tags to user attribute data in real time; the user attribute data includes three high data, three high analysis data, and analysis results; User attribute data is classified and processed according to sub-user tags.

[0011] Furthermore, the method for analyzing the three high analysis data includes: Acquire user information, determine the user's three-high standard indicators according to the user information, analyze the user's three-high analysis data according to the three-high standard indicators, and determine the user's baseline evaluation result; Generate monitoring curves for the user's high blood pressure, high blood sugar, and high blood lipids according to the distribution of the three high analysis data, where the horizontal axis of the monitoring curve is time and the vertical axis is the monitoring value, and the monitoring value is the monitoring value corresponding to the corresponding high blood pressure, high blood sugar, and high blood lipids; Perform trend warning analysis based on the monitoring curve to obtain corresponding warning results; integrate the warning results and benchmark assessment results into analysis results.

[0012] Furthermore, the method for analyzing the three high analysis data includes: Acquire user information, determine the user's three-high standard indicators according to the user information, analyze the user's three-high analysis data according to the three-high standard indicators, and determine the user's baseline evaluation result; Generate monitoring curves for the user's high blood pressure, high blood sugar, and high blood lipids according to the distribution of the three high analysis data, where the horizontal axis of the monitoring curve is time and the vertical axis is the monitoring value, and the monitoring value is the monitoring value corresponding to the corresponding high blood pressure, high blood sugar, and high blood lipids; Fit the monitoring curve to obtain the corresponding curve function, and mark the curve function as B(t), t time; Set the curve evaluation model. The expression of the curve evaluation model is: ; Where: [B(t)] is the input data, B(t) is the curve function; k B(t) is the slope of the monitoring curve corresponding to the corresponding time; X1 is the threshold; the output data is the curve evaluation value QP[B(t)]; The curve function is evaluated by a curve evaluation model to obtain a curve evaluation value, and a curve evaluation result is determined according to the curve evaluation value; The curve evaluation results and the benchmark evaluation results are integrated into the analysis results.

[0013] Furthermore, the method for analyzing the three high analysis data includes: Acquire user information, determine the user's three-high standard indicators according to the user information, analyze the user's three-high analysis data according to the three-high standard indicators, and determine the user's baseline evaluation result; Generate monitoring curves for the user's high blood pressure, high blood sugar, and high blood lipids according to the distribution of the three high analysis data, where the horizontal axis of the monitoring curve is time and the vertical axis is the monitoring value, and the monitoring value is the monitoring value corresponding to the corresponding high blood pressure, high blood sugar, and high blood lipids; Predicting is performed according to the monitoring curve to obtain a monitoring prediction curve; fitting the monitoring prediction curve to obtain a corresponding monitoring prediction function, and marking the curve function as C(t), where t is time; Set the curve evaluation model. The expression of the curve evaluation model is: ; Where: [C(t)] is the input data, C(t) is the monitoring prediction function; k C(t) is the slope of the monitoring curve corresponding to the corresponding time; X1 is the threshold; the output data is the curve evaluation value QP[C(t)]; The curve function is evaluated by a curve evaluation model to obtain a curve evaluation value, and a curve evaluation result is determined according to the curve evaluation value; The curve evaluation results and the benchmark evaluation results are integrated into the analysis results.

[0014] Step 2: The user obtains the three-high analysis data through the acquisition module, and the analysis module analyzes the three-high analysis data, and displays the analysis results to the user through the display module; Step 3: The platform optimizes and analyzes the correction method of the three high data in real time, and the correction method is to correct the three high data through a preset correction model; When it is determined that the correction method needs to be optimized, the correction model at the corresponding user is optimized and adjusted; When it is determined that there is no need to optimize the correction method, no optimization adjustment is performed.

[0015] An intelligent analysis and management system for health data of three highs, including a user terminal and an optimization module; The user terminal includes a display module, a user module, a voice module, a collection module and an analysis module; The display module is used to display the received data; The voice module is used for voice broadcasting and voice control, and includes a voice broadcasting unit and a voice control unit. The voice broadcasting unit is used for voice broadcasting of data within a preset range; the voice control unit is used for the user to perform voice control on the user terminal; The acquisition module is used to collect the user's three high data, and correct the collected three high data to obtain the user's three high analysis data, and send the three high analysis data to the display module; The user module is used for user management; The analysis module is used to analyze the three high analysis data of the user, determine the analysis result of the user, and send the analysis result to the display module.

[0016] The optimization module optimizes and analyzes the correction method of the three high data. When it is determined that the correction method needs to be optimized, the correction model at the corresponding user is optimized and adjusted; when it is determined that the correction method does not need to be optimized, no optimization adjustment is performed.

[0017] Compared with the prior art, the present invention has the following beneficial effects: For users who are illiterate or cannot use smart devices, the present invention greatly reduces the difficulty of operation by introducing multiple data collection methods such as docking wearable devices, voice input and handwriting recognition, so that users can easily and accurately complete the collection of three high data. These diverse collection methods not only improve the efficiency of data collection, but also ensure the authenticity and reliability of the data, providing a solid foundation for subsequent health management and disease prevention. The present invention has data display, voice broadcast and voice control functions, and can provide real-time feedback on the user's health data, so that the user can intuitively understand their health status. Through voice broadcast and voice control, users can easily obtain health data and perform corresponding operations without reading complex text information, thereby enhancing the user's experience and interactivity. Taking into account the diversity of user backgrounds, the present invention allows the user end to bind multiple users and can perform a comprehensive analysis of the three high data of multiple users. This function not only meets the needs of sharing health data among family members or relatives and friends, but also provides comprehensive data support for doctors or health management agencies, which helps to formulate more accurate and personalized health management plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0020] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] like Figure 1 As shown, a method for intelligent analysis and management of three-high health data includes: Step 1: The platform establishes a user terminal, which is an independent portable device placed in a corresponding position according to user needs, such as a radio, mobile phone, etc. The user terminal includes a display module, a user module, a voice module, a collection module and an analysis module; The display module is used to display the received data and the corresponding operation interface, etc., and is set based on the existing technology.

[0022] The voice module is used for voice broadcast and voice control, including a voice broadcast unit and a voice control unit. The voice broadcast unit is used to voice broadcast data within a preset range, such as three high data, analysis results and other related data; the voice control unit is used to perform voice control on the user end, such as the working mode of various common voice intelligent control devices, and the user controls through voice, such as entering data, turning on and off, system settings, etc., which is specifically set based on existing voice control technology.

[0023] In one embodiment, the preset range is set based on existing technologies, such as being preset and fixed by the platform.

[0024] In one embodiment, the preset range is set as follows: Based on big data and other methods, the current voice broadcast needs of people in the field of three-high data analysis can be determined in combination with the current voice broadcast usage of various elderly people's devices, and the data items corresponding to each voice broadcast need can be identified, such as the data items corresponding to the three-high monitoring data, the data items corresponding to the three-high analysis results, the data items corresponding to the setting parameters, etc.; each data item is screened and integrated into the initial range; screening is to remove unnecessary data items, and the platform staff will perform manual screening, use existing technologies to analyze the needs of each voice broadcast area, and determine whether the voice broadcast demand is still needed, so as to achieve the screening of the corresponding data items; When the user has a need to adjust the voice broadcast, the initial range is displayed to the user, and the user selects data items that need to be voice broadcast within the initial range, marked as data broadcast items, and the data broadcast items are combined into a preset range.

[0025] When the user has no need to adjust the voice broadcast, the initial range is marked as a preset range.

[0026] Subsequently, the corresponding data is identified in real time according to the preset range, and then it is determined whether to perform voice broadcast.

[0027] The acquisition module is used to acquire the user's three high data, and to correct the acquired three high data to obtain the user's three high analysis data; and to send the three high analysis data to the display module.

[0028] In one embodiment, the collection of the three high data can be performed based on existing technologies.

[0029] In one embodiment, the method for collecting the three high data is: Obtain the user's monitoring methods for the three highs data, such as wearing various smart devices for monitoring, manual monitoring through blood glucose meters, hypertension equipment, etc.; identify the monitoring equipment corresponding to the monitoring method, which is the equipment for monitoring the three highs, conduct access evaluation on the monitoring equipment, and determine whether data docking can be performed, that is, whether it can be connected to the corresponding monitoring equipment to obtain its three highs monitoring data; direct judgment can be made based on the monitoring equipment; When it is determined that data connection can be carried out, the corresponding monitoring equipment will be connected to obtain the three high data collected by the monitoring equipment in real time; When it is determined that data docking cannot be performed, the user selects a preset data entry method to enter the three high data, and the data entry methods include voice entry and handwriting entry.

[0030] In one embodiment, the three high data are corrected to eliminate the monitoring errors of the corresponding monitoring equipment. Because the market is flooded with various monitoring equipment, the monitoring accuracy of some monitoring equipment is reduced, so corresponding corrections are needed; specifically, in this embodiment, corrections can be made based on existing technologies.

[0031] In one embodiment, the method for correcting the three high data includes: Obtain historical data of the three highs, and determine group information of patients with the three highs based on the historical data of the three highs. The group information consists of data that may affect the monitoring of the three highs, such as the gender, age, body fat percentage, height, etc. of the corresponding personnel. Mark the information of a single person as a person feature, that is, determine the characteristics of each person based on the historical data of the three highs; classify each person feature to form a classification of each feature; A corresponding correction model is set for each feature classification. The correction model is used to correct the three high data of the feature classification under the corresponding technical classification to eliminate the influence of equipment errors. Specifically, it is established based on existing intelligent technology, such as establishing a correction model based on neural networks such as CNN networks or DNN networks, and manually establishing a corresponding training set for training. The training set includes input data and output data. The input data is the three high data, and the output data is the corrected three high data; analysis is performed through the correction model after successful training.

[0032] Obtain user information, which includes monitoring device information, personal characteristics and other related information, match the feature classification of the corresponding technical classification for the user based on the user information, and then identify the correction model corresponding to the feature classification, correct the three high data through the correction model, and obtain the three high analysis data.

[0033] In one embodiment, the characteristics of each person may be classified based on various existing classification, clustering, and other methods.

[0034] In one embodiment, the method for classifying each person's characteristics includes: Identify the monitoring principles and monitoring technologies used by various monitoring devices on the current market, mark them as technical data, screen the technical data, and obtain the reserve technical data; classify the reserve technical data according to whether the deviations caused are the same, the technology is the same, etc., to form various technical categories. The classification can be result-oriented, and the error results of the parameters in the monitoring process that are the same or the difference is within the allowable range can be classified into one category; the technical personnel of the platform can also directly set the various technical categories; According to each technical classification, simulate and analyze each personnel feature to determine whether the monitoring errors of the corresponding personnel features under the monitoring equipment corresponding to the technical classification are the same or basically the same, that is, use the monitoring equipment corresponding to the technical classification to monitor the personnel corresponding to the corresponding personnel features, obtain the corresponding monitoring data, mark it as simulation data, or directly use the historical monitoring data of the corresponding monitoring equipment instead; analyze the differences between the simulation data, if the differences are within the allowable range, it is considered to meet the merger requirements; classify each personnel feature that meets the merger requirements into one category, and obtain the feature classification corresponding to the technical classification, that is, each personnel feature in the feature classification meets the merger requirements when monitored by the monitoring equipment under the technical classification. It is also possible to establish a simulation analysis model based on neural networks such as CNN networks and DNN networks, and establish a corresponding training set for training by artificial means. The training set includes input data and output data. The input data is the corresponding simulation data, and the output data is the result of the merger requirement judgment; the simulation analysis model after successful training is used for analysis.

[0035] That is, the feature classifications corresponding to different technical classifications may not be the same.

[0036] In one embodiment, the technical data may be screened by removing duplicates.

[0037] In one embodiment, the technical data are screened by first removing duplicates and then screening according to their corresponding shares, eliminating those whose shares are greater than a preset value.

[0038] In one embodiment, the technical data are screened to remove duplicates first, and then the monitoring accuracy corresponding to each technical data is analyzed, and the ones with too low monitoring accuracy and too backward technology are eliminated.

[0039] In one embodiment, the technical data is screened, and the screening may also be performed based on existing technologies.

[0040] The user module is used for user management.

[0041] In one embodiment, user management, such as user information management, is performed in an existing manner.

[0042] In one embodiment, in an actual application context, there is often a situation where multiple people use one user terminal, so the method for user management includes: Set up a corresponding number of sub-users according to the number of users, and name each sub-user for easy distinction, and add corresponding sub-user tags to user attribute data in real time; user attribute data refers to data for corresponding users, such as the three highs data, the three highs analysis data, and the analysis results; User attribute data is classified and processed according to sub-user tags.

[0043] The analysis module is used to analyze the three high analysis data of the user, determine the analysis result of the user, and send the analysis result to the display module.

[0044] In one embodiment, the three high analysis data can be analyzed using existing technologies, such as establishing a corresponding intelligent model based on existing intelligent technology, and analyzing through the intelligent model to obtain early warning results, dietary recommendations and other analysis results.

[0045] In one embodiment, the method for analyzing the three-high analysis data includes: Obtain user information and determine the user's three high standard indicators based on the user information, because the standards of different people are different, such as hypertension assessment indicators, normal adults: blood pressure should be lower than 130 / 85mmHg (some data are around 120 / 80mmHg or 90139 / 6089mmHg). If the blood pressure is continuously higher than 140 / 90mmHg, it can be diagnosed as hypertension. Blood pressure values ​​between the two are called prehypertension. Hypertensive patients: According to the degree of blood pressure increase, hypertension can be divided into grade 1 (mild), grade 2 (moderate) and grade 3 (severe). Grade 1 hypertension: systolic blood pressure 140159mmHg and / or diastolic blood pressure 9099mmHg. Grade 2 hypertension: systolic blood pressure 160179mmHg and / or diastolic blood pressure 100109mmHg. Grade 3 hypertension: systolic blood pressure ≥180mmHg and / or diastolic blood pressure ≥110mmHg.

[0046] Analyze the three-high analysis data according to the three-high standard indicators to determine the analysis results, i.e. whether it is hypertension, the degree of hypertension, etc.; In one embodiment, the method for analyzing the three-high analysis data includes: Obtain user information, determine the user's three high standard indicators according to the user information, analyze the three high analysis data according to the three high standard indicators, and determine the benchmark evaluation result, that is, the analysis result in the above embodiment; Generate monitoring curves of the user's high blood pressure, high blood sugar and high blood lipids based on the three high analysis data. The horizontal axis of the monitoring curve is time, and the vertical axis is the monitoring value, that is, the corresponding blood pressure, blood sugar and other parameter values; Perform trend warning analysis based on the monitoring curve to obtain the corresponding warning result; that is, use the existing prediction technology to predict the monitoring curve, analyze the monitoring curve for subsequent time prediction, form a benchmark evaluation result for subsequent time prediction, and mark it as a warning result; because there is currently mature prediction technology, prediction can be made based on the existing prediction technology; Integrate early warning results and baseline assessment results into analytical results.

[0047] In one embodiment, the method for analyzing the three-high analysis data includes: Obtain user information, determine the user's three high standard indicators according to the user information, analyze the three high analysis data according to the three high standard indicators, and determine the benchmark evaluation result, that is, the analysis result in the above embodiment; Generate monitoring curves of the user's high blood pressure, high blood sugar and high blood lipids based on the three high analysis data. The horizontal axis of the monitoring curve is time, and the vertical axis is the monitoring value, that is, the corresponding blood pressure, blood sugar and other parameter values; Fit the monitoring curve to obtain the corresponding curve function, and mark the curve function as B(t), t time; Set the curve evaluation model. The expression of the curve evaluation model is: ; Where: [B(t)] is the input data, B(t) is the curve function; k B(t) is the slope of the monitoring curve corresponding to the corresponding time; X1 is the threshold, which is set according to the requirement that the corresponding three highs cannot exceed a certain change; the output data is the curve evaluation value QP[C(t)]; The curve function is evaluated through the curve evaluation model to obtain the corresponding curve evaluation value, and the curve evaluation result is determined according to the curve evaluation value; that is, the curve evaluation value of 1 indicates monitoring abnormality; The curve evaluation results and the benchmark evaluation results are integrated into the analysis results.

[0048] In one embodiment, the difference between this embodiment and the previous embodiment is that prediction is performed according to the monitoring curve to obtain the monitoring prediction curve, and prediction is performed using the existing prediction technology; the monitoring prediction curve is fitted to obtain the monitoring prediction function; the monitoring prediction function is evaluated by the curve evaluation model to obtain the corresponding curve evaluation result; The curve evaluation results and the benchmark evaluation results are integrated into the analysis results.

[0049] Step 2: The user obtains the three high analysis data through the acquisition module, and the analysis module analyzes the three high analysis data, and displays the obtained analysis results to the user through the display module; Step 3: The platform optimizes and analyzes the correction method of the three high data in real time, and the correction method is to correct the three high data through the preset corresponding correction model; When it is determined that the correction method needs to be optimized, the corresponding correction model at each user is optimized and adjusted; When it is determined that there is no need to optimize the correction method, no optimization adjustment is performed.

[0050] In one embodiment, the method for optimizing and analyzing the correction method of the three high data is: judging whether there is room for optimization based on the technological development corresponding to the correction model, if there is room for optimization, then optimization is required; at the same time, when it is judged that the correction effect does not meet the preset requirements, more advanced technology can be applied for correction.

[0051] In one embodiment, the correction method of the three high data is optimized and analyzed, and judgment is made according to the existing method.

[0052] An intelligent analysis and management system for health data of three highs, including a user terminal and an optimization module; The user terminal includes a display module, a user module, a voice module, a collection module and an analysis module; The display module is used to display the received data; The voice module is used for voice broadcasting and voice control, and includes a voice broadcasting unit and a voice control unit. The voice broadcasting unit is used for voice broadcasting of data within a preset range; the voice control unit is used for the user to perform voice control on the user terminal; The acquisition module is used to collect the user's three high data, and correct the collected three high data to obtain the user's three high analysis data, and send the three high analysis data to the display module; The user module is used for user management; The analysis module is used to analyze the three high analysis data of the user, determine the analysis result of the user, and send the analysis result to the display module.

[0053] The optimization module optimizes and analyzes the correction method of the three high data. When it is determined that the correction method needs to be optimized, the correction model at the corresponding user is optimized and adjusted; when it is determined that the correction method does not need to be optimized, no optimization adjustment is performed.

[0054] The above formulas are all calculated by removing dimensions and taking numerical values. The formula is a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained by simulating a large amount of data.

[0055] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for intelligent analysis and management of health data of three highs, characterized in that: Methods include: Step 1: The platform establishes a user terminal, which includes a display module, a user module, a voice module, a collection module and an analysis module; The display module is used to display the received data; The voice module is used for voice broadcasting and voice control, and includes a voice broadcasting unit and a voice control unit. The voice broadcasting unit is used for voice broadcasting of data within a preset range; the voice control unit is used for the user to voice control the user terminal; The acquisition module is used to collect the user's three high data, and correct the collected three high data to obtain the user's three high analysis data, and send the three high analysis data to the display module; The user module is used for user management; The analysis module is used to analyze the three high analysis data of the user, determine the analysis result of the user, and send the analysis result to the display module; Step 2: The user obtains the three-high analysis data through the acquisition module, and the analysis module analyzes the three-high analysis data, and displays the analysis results to the user through the display module; Step 3: The platform optimizes and analyzes the correction method of the three high data in real time, and the correction method is to correct the three high data through a preset correction model; When it is determined that the correction method needs to be optimized, the correction model at the corresponding user is optimized and adjusted; When it is determined that there is no need to optimize the correction method, no optimization adjustment is performed.

2. A method for intelligent analysis and management of health data of three highs according to claim 1, characterized in that: The preset range is set as follows: Determine the voice broadcast requirement, identify the data items corresponding to the voice broadcast requirement; filter and integrate the data items into an initial range; When the user has a need to adjust the voice broadcast, the initial range is displayed to the user, and the user selects a data item that needs to be voice broadcast within the initial range, and the data item selected by the user is marked as a data broadcast item; each data broadcast item is combined into a preset range; When the user has no need to adjust the voice broadcast, the initial range is marked as a preset range.

3. The method for intelligent analysis and management of three-high health data according to claim 1, characterized in that: The collection method of the three high data is: Obtain the user's monitoring method for the three high data, identify the monitoring device corresponding to the monitoring method, conduct access evaluation on the monitoring device, and determine whether data connection can be performed; When it is determined that data docking can be performed, docking with the monitoring device is performed to obtain the three high data collected by the monitoring device in real time; When it is determined that data docking cannot be performed, the user selects a preset data entry method to enter the three high data, and the data entry method includes voice entry and handwriting entry.

4. The method for intelligent analysis and management of three-high health data according to claim 1, characterized in that: Methods for correcting the three high data include: Acquire the three high historical data, and determine the personnel characteristics according to the three high historical data; classify the personnel characteristics to form various characteristic classifications; Establishing a corresponding correction model according to the feature classification, wherein the correction model is used to correct the three high data; Identify user information, and match a corresponding correction model for the user according to the user information; correct the user's three-high data through the correction model to obtain three-high analysis data.

5. The method for intelligent analysis and management of three-high health data according to claim 4 is characterized in that: Methods for classifying personnel characteristics include: Identify the technical data corresponding to each current monitoring device, screen the technical data, and obtain reserve technical data; classify the reserve technical data to form a technical classification; Conduct simulation analysis on personnel characteristics according to technical classification to determine whether the corresponding personnel characteristics meet the merger requirements; When it is judged that the requirements for merging are not met, the corresponding personnel characteristics are not allocated in the same characteristic classification; When it is determined that the merger requirements are met, the corresponding personnel characteristics are classified into one category to obtain the characteristic classification corresponding to the technical classification.

6. The method for intelligent analysis and management of three-high health data according to claim 1, characterized in that: Methods for user management include: Determine the number of users of the user terminal, set a corresponding number of sub-users according to the number of users, and name the sub-users; Add corresponding sub-user tags to user attribute data in real time; the user attribute data includes three high data, three high analysis data, and analysis results; User attribute data is classified and processed according to sub-user tags.

7. The method for intelligent analysis and management of three-high health data according to claim 1, characterized in that: Methods for analyzing the three high analysis data include: Acquire user information, determine the user's three-high standard indicators according to the user information, analyze the user's three-high analysis data according to the three-high standard indicators, and determine the user's baseline evaluation result; Generate monitoring curves for the user's high blood pressure, high blood sugar, and high blood lipids according to the distribution of the three high analysis data, where the horizontal axis of the monitoring curve is time and the vertical axis is the monitoring value, and the monitoring value is the monitoring value corresponding to the corresponding high blood pressure, high blood sugar, and high blood lipids; Perform trend warning analysis based on the monitoring curve to obtain corresponding warning results; integrate the warning results and benchmark assessment results into analysis results.

8. The method for intelligent analysis and management of three-high health data according to claim 1, characterized in that: Methods for analyzing the three high analysis data include: Acquire user information, determine the user's three-high standard indicators according to the user information, analyze the user's three-high analysis data according to the three-high standard indicators, and determine the user's baseline evaluation result; Generate monitoring curves for the user's high blood pressure, high blood sugar, and high blood lipids according to the distribution of the three high analysis data, where the horizontal axis of the monitoring curve is time and the vertical axis is the monitoring value, and the monitoring value is the monitoring value corresponding to the corresponding high blood pressure, high blood sugar, and high blood lipids; Fit the monitoring curve to obtain the corresponding curve function, and mark the curve function as B(t), t time; Set the curve evaluation model. The expression of the curve evaluation model is: ； Where: [B(t)] is the input data, B(t) is the curve function; k B(t) is the slope of the monitoring curve corresponding to the corresponding time; X1 is the threshold; the output data is the curve evaluation value QP[B(t)]; The curve function is evaluated by a curve evaluation model to obtain a curve evaluation value, and a curve evaluation result is determined according to the curve evaluation value; The curve evaluation results and the benchmark evaluation results are integrated into the analysis results.

9. An intelligent analysis and management system for health data of three highs, characterized in that: A method for intelligent analysis and management of health data of three highs according to any one of claims 1 to 8 is implemented, comprising a user end and an optimization module; The user terminal includes a display module, a user module, a voice module, a collection module and an analysis module; The display module is used to display the received data; The voice module is used for voice broadcasting and voice control, and includes a voice broadcasting unit and a voice control unit. The voice broadcasting unit is used for voice broadcasting of data within a preset range; the voice control unit is used for the user to voice control the user terminal; The acquisition module is used to collect the user's three high data, and correct the collected three high data to obtain the user's three high analysis data, and send the three high analysis data to the display module; The user module is used for user management; The analysis module is used to analyze the three high analysis data of the user, determine the analysis result of the user, and send the analysis result to the display module; The optimization module optimizes and analyzes the correction method of the three high data, and when it is determined that the correction method needs to be optimized, optimizes and adjusts the correction model at the corresponding user; When it is determined that there is no need to optimize the correction method, no optimization adjustment is performed.

Images