Health archive management system and method based on data visualization

Abstract

The invention discloses a health archive management system and method based on data visualization, and relates to the technical field of archive management.The health archive management method comprises the steps that historical health data of a user are obtained, and a health dynamic record library is constructed to collect the health data of the user in real time; analyzing periodic user health data steady-state distribution of the user periodic health data based on the dynamic health record library of the user; according to the steady-state distribution data of the user health data in each period, constructing a user state matrix in a corresponding period; analyzing state transition data of the users in the adjacent periods by combining the user state matrixes of the adjacent periods to obtain user state transition matrixes of the adjacent periods; analyzing the health data offset degree of the user in the adjacent periods based on the user state transition matrix in the adjacent periods, and performing abnormity prompting based on the analysis data; according to the invention, a unified digital health management platform is provided for the user.

Description

Technical Field

[0001] The present invention relates to the field of file management, and specifically to a health file management system and method based on data visualization. Background Art

[0002] With the rapid development of information technology and the digital transformation of the medical and health field, personal health management has gradually shifted from traditional paper records to an electronic and intelligent management mode. The accumulation of data such as electronic medical records, physical examination reports, and health indicators provides rich health information for individuals and families. However, these data are often scattered in different medical institutions and platforms, which is not conducive to users' real-time access; moreover, the complexity and diversity of health data make it difficult for users to intuitively understand and utilize this information, resulting in low efficiency of health management and being not conducive to users' intuitive access to their own health status. Summary of the Invention

[0003] The purpose of the present invention is to provide a health file management system and method based on data visualization to solve the problems raised in the prior art.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A health file management method based on data visualization, the method comprising the following steps:

[0006] Obtain the user's historical health data, and construct a health dynamic record library to collect the user's health data in real time;

[0007] Analyze the steady-state distribution of the user's periodic health data based on the user's health dynamic record library; construct the user state matrix for the corresponding period according to the steady-state distribution data of the user's periodic health data in each period;

[0008] Analyze the state transition data of the user within adjacent periods by combining the user state matrices of adjacent periods to obtain the user state transition matrix of adjacent periods; analyze the deviation degree of the user's health data within adjacent periods based on the user state transition matrix of adjacent periods, and give an abnormal prompt based on the analysis data.

[0009] Further, the user's historical health data includes the user's electronic health file data and paper health file data; the user's historical health record data is obtained by the user uploading the electronic health file data and by scanning the user's paper health file data.

[0010] Store the user's historical health record data by constructing a health dynamic record library, and collect the real-time user health data by associating with the real-time health data collection device of the user; the health data includes the user's heart rate data, blood pressure data, body temperature data, etc.; the real-time health data collection device associated with the user realizes data collection through signal transmission via the Internet of Things;

[0011] Authenticate the user's identity by generating a user dynamic identity code for the health dynamic record library, and open the data access and operation permissions based on the verification result; among them, the user dynamic identity code is sent to the user's mobile device, and after the user successfully authenticates the identity through the user dynamic identity code, the user can access and operate the data in the health dynamic record library.

[0012] Further, divide the user health data recorded in the health dynamic record library according to the data collection time data according to the user-set data collection period; based on the period division of the user health data, number and mark each period, and perform data steady-state distribution analysis on the user health data of each period;

[0013] Retrieve the user health data of each period, and respectively judge the abnormality of the corresponding health data at each time point within the period; by introducing the reference interval of the corresponding type of user health data for comparison, if there is data that does not belong to the interval, mark it as abnormal; otherwise, calculate the mean value of the corresponding type of user health data within the period, and perform steady-state distribution analysis on the corresponding type of user health data within the period based on the mean value of the corresponding type of user health data; compare the corresponding type of user health data at each time point within the period with the mean value to obtain the steady-state distribution index of the corresponding type of user health data at the corresponding time point; if it is greater than the mean value, record the steady-state distribution index of the corresponding type of user health data at the corresponding time point as 1; if it is equal to the mean value, record the steady-state distribution index of the corresponding type of user health data at the corresponding time point as 0; if it is less than the mean value, the steady-state distribution index of the corresponding type of user health data at the corresponding time point is -1; according to the comparison result, calculate the absolute value of the difference between the corresponding type of user health data at each time point within the period and the mean value, and calculate the ratio of the calculated data to the mean value to obtain the fluctuation index of the corresponding type of user health data at each time point within the period;

[0014] Combine the steady-state distribution index and the fluctuation index of the corresponding type of user health data at each time point within the period to construct the distribution feature vector of the corresponding type of user health data at the corresponding time point, denoted as a(x,y); where x is the steady-state distribution index; y is the fluctuation index; based on the distribution feature vector of the corresponding type of user health data at each time point within the period, construct the periodic user state matrix of the user's health data within the period, denoted as A n(m×t); where n is the cycle number; m is the numbering of the types of user health data included in the vertical axis of the cycle user status matrix; t is the numbering of the time points within the cycle on the horizontal axis of the cycle user status matrix; wherein, each element within the cycle user status matrix is a distribution feature vector of the corresponding type of user health data at the corresponding time point.

[0015] Further, arbitrarily select adjacent cycle user status matrices, calculate the absolute value of the difference between the adjacent cycle user status matrices, calculate the absolute value of the difference for the elements at the same coordinate positions in the adjacent cycle user status matrices respectively, and obtain the absolute value calculation data of the distribution feature vectors of the corresponding type of user health data at the corresponding time points; based on the calculation results, obtain the transfer data of the distribution feature vectors of the corresponding type of user health data at the corresponding time points in the adjacent cycle user status matrices, and construct an adjacent cycle user status transfer matrix.

[0016] Based on the adjacent cycle user status matrices, perform offset divergence analysis on the distribution feature vectors of the corresponding type of user health data at the corresponding time points of the elements at the same positions respectively; according to the offset divergence analysis results of the corresponding elements at the same positions in the adjacent cycle user status matrices, analyze the comprehensive offset divergence from the previous cycle user status matrix to the next cycle user status matrix in the adjacent cycle user status matrices; through vector transformation of the adjacent cycle user status transfer matrix, obtain the corresponding vector modulus data of the adjacent cycle user status transfer matrix; by multiplying the comprehensive offset divergence analysis data of the adjacent cycle user status matrices by the corresponding vector modulus data of the adjacent cycle user status transfer matrix, obtain the offset index of the adjacent cycle user health data, and judge the abnormal offset of the user health data in the adjacent cycle according to the comparison with the index threshold; wherein, if the offset index of the adjacent cycle user health data is greater than the threshold, it is prompted that the data offset is abnormal; otherwise, it is normal, and continue to perform periodic offset analysis on the user health data; wherein, performing offset divergence analysis on the distribution feature vectors of the corresponding type of user health data at the corresponding time points of the elements at the same positions respectively is specifically

[0017]

[0018] wherein, D(A n (a ij ): A n+1 (a ij )) corresponds to the offset divergence of the elements at the same positions in the adjacent cycle user status matrices; |A n (a ij )| and |A n+1 (a ij )| correspond to the modulus of the distribution feature vectors of the corresponding type of user health data at the corresponding time points of the elements at the same positions in the adjacent cycle user status matrices; i, j are the element position coordinates in the matrix.

[0019] Analyze the comprehensive offset divergence of the previous cycle user status matrix pointing to the next cycle user status matrix in the adjacent cycle user status matrix, specifically

[0020]

[0021] where D(A n : A n+1 ) is the comprehensive offset divergence of the adjacent cycle user status matrix.

[0022] Furthermore, output the periodic health data of the user through a visualization interface; the visualization interface includes displaying the user's health data in the forms of tables, pie charts, curves, and human models; build a data management platform through the visualization interface of the service application to record and display the periodic health data and series analysis data of the user, and generate a unique exclusive data identification code for the user's full-life cycle health management, such as a QR code, etc., to facilitate the user to transfer or transfer data through the identification code, and improve the efficiency of scenarios such as personal health management prevention and improving the efficiency of medical consultations; use the health record management system as the technical basis to facilitate individuals and family members, and enable authorized doctors and third-party institutions to quickly understand the patient's condition, such as judging the severity of related symptoms, emergency medication, etc., so as to give suggestions on medical consultations and drug distribution to the patient.

[0023] Output the offset evaluation data of the user's periodic health data, and mark the abnormal offset periodic health data to prompt data anomalies; among them, the offset evaluation data of the user's health data can output the evaluation results of a single type of periodic data or the combined evaluation results of multiple types of periodic data according to the user's needs.

[0024] A health record management system based on data visualization, the system includes a user health data acquisition module, a user status analysis module, a status offset evaluation module, and a visualization output module;

[0025] The user health data acquisition module acquires the user's historical health data and constructs a health dynamic record library to collect the user's health data in real time; the user status analysis module analyzes the steady-state distribution of the user's periodic health data based on the user's health dynamic record library; constructs the corresponding cycle user status matrix according to the steady-state distribution data of each cycle user health data; the status offset evaluation module analyzes the status transfer data of the user within the adjacent cycle by combining the adjacent cycle user status matrix to obtain the adjacent cycle user status transfer matrix; analyzes the offset degree of the user's health data within the adjacent cycle based on the adjacent cycle user status transfer matrix, and gives an anomaly prompt based on the analysis data; the visualization output module visually outputs the user's periodic health data and gives a prompt for abnormal data.

[0026] Furthermore, the user health data acquisition module includes a user health data acquisition unit and a health dynamic record library construction unit;

[0027] The user health data acquisition unit obtains the user's historical health record data by the user uploading electronic health record data and by document scanning of the user's paper health record data;

[0028] The health dynamic record library construction unit stores the user's historical health record data by constructing a health dynamic record library, and collects real-time user health data by associating with the user's real-time health data acquisition device; authenticates the user's identity by generating a user dynamic identity code for the health dynamic record library, and opens data access and operation permissions based on the verification result.

[0029] Furthermore, the user status analysis module includes a periodic health data division unit and a periodic user status matrix construction unit;

[0030] The periodic health data division unit divides the user health data recorded in the health dynamic record library according to the data acquisition period set by the user based on the data acquisition time data; numbers and marks each period based on the periodic division of the user health data, and performs data steady-state distribution analysis on the user health data of each period;

[0031] The periodic user status matrix construction unit retrieves the user health data of each period, and respectively judges the abnormality of the corresponding health data at each time point within the period; compares by introducing the reference interval of the corresponding type of user health data, and if there is data that does not belong to the interval, marks it as abnormal; otherwise, calculates the mean value of the corresponding type of user health data within the period, and performs steady-state distribution analysis on the corresponding type of user health data within the period based on the mean value of the corresponding type of user health data; compares the corresponding type of user health data at each time point within the period with the mean value to obtain the steady-state distribution index of the corresponding type of user health data at the corresponding time point; according to the comparison result, calculates the absolute value of the difference between the corresponding type of user health data at each time point within the period and the mean value, and calculates the ratio of the calculated data to the mean value to obtain the fluctuation index of the corresponding type of user health data at each time point within the period;

[0032] Combining the steady-state distribution index and the fluctuation index of the corresponding type of user health data at each time point within the period, constructs the distribution feature vector of the corresponding type of user health data at the corresponding time point; constructs a periodic user status matrix for the user's health data within the period based on the distribution feature vectors of the corresponding type of user health data at each time point within the period.

[0033] Further, the state offset evaluation module includes a periodic state transition analysis unit and a periodic health data offset evaluation unit;

[0034] The periodic state transition analysis unit randomly selects adjacent-period user state matrices, calculates the absolute value of the difference between adjacent-period user state matrices, calculates the absolute value of the difference between elements at the same coordinate positions in adjacent-period user state matrices respectively, and obtains the absolute value calculation data of the distribution feature vectors of user health data of corresponding types at corresponding time points; according to the calculation results, obtains the transition data of the distribution feature vectors of user health data of corresponding types at corresponding time points in adjacent-period user state matrices, and constructs an adjacent-period user state transition matrix;

[0035] The periodic health data offset evaluation unit performs offset divergence analysis on the distribution feature vectors of user health data of corresponding types at corresponding time points of elements at the same positions respectively based on adjacent-period user state matrices; according to the offset divergence analysis results of each corresponding element at the same position in adjacent-period user state matrices, analyzes the comprehensive offset divergence from the previous-period user state matrix to the next-period user state matrix in adjacent-period user state matrices; obtains the corresponding vector modulus data of the adjacent-period user state transition matrix by performing vector transformation on the adjacent-period user state transition matrix; obtains the offset index of adjacent-period user health data by multiplying the comprehensive offset divergence analysis data of adjacent-period user state matrices by the corresponding vector modulus data of the adjacent-period user state transition matrix, and judges the abnormal offset of user health data within adjacent periods by comparing with the index threshold.

[0036] Further, the visualization output module includes a visualization output unit and an abnormal prompt unit;

[0037] The visualization output unit outputs the periodic health data of users through a visualization interface;

[0038] The abnormal prompt unit outputs the offset evaluation data of the periodic health data of users, marks the periodic health data with abnormal offset, and prompts data abnormality.

[0039] Compared with the prior art, the beneficial effects of the present invention are:

[0040] The present invention realizes the collection of real-time health data of users by integrating multi-channel user health data; determines periodic health data, and conducts state matrix analysis to determine the steady-state distribution and fluctuation of the health data of users in different periods; conducts state transition analysis on the state matrix data of users in adjacent periods, and combines the offset divergence of each health data in the state matrix data of users in adjacent periods, thereby determining the offset of the health data of users within adjacent periods; and thus determines the data abnormality of the health data of users within adjacent periods; the present invention can integrate multi-channel health data of users, and then provide an integrated platform for users to view their own health data at any time; and through a series of health data comparison and evaluation analyses, it helps users intuitively obtain their own health status; this application realizes providing a unified digital health management platform for users, combines intelligent recognition and archiving of personal medical records and reports, and text entry of health diary data, establishes a complete health big data management platform for users, and assigns a unique identification code for users to record the health management of the entire life cycle, and can be applied to aspects such as personal health management prevention and improvement of medical consultation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 FIG. is a schematic structural diagram of a health record management system based on data visualization according to the present invention;

[0042] Figure 2 FIG. is a schematic flow diagram of a health record management method based on data visualization according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0044] Embodiment: As Figure 1 shown, the present invention provides a technical solution:

[0045] A health record management system based on data visualization, the system includes a user health data acquisition module, a user state analysis module, a state offset evaluation module, and a visualization output module;

[0046] The user health data acquisition module acquires the user's historical health data and constructs a health dynamic record library to collect the user's health data in real time; the user status analysis module analyzes the steady-state distribution of the user's periodic health data based on the user's health dynamic record library; constructs the user status matrix for the corresponding period according to the steady-state distribution data of the user's periodic health data; the status deviation evaluation module analyzes the status transfer data of the user within adjacent periods by combining the user status matrices of adjacent periods to obtain the user status transfer matrix of adjacent periods; analyzes the deviation degree of the user's health data within adjacent periods based on the user status transfer matrix of adjacent periods, and gives an anomaly prompt based on the analysis data; the visualization output module visually outputs the user's periodic health data and gives a prompt for abnormal data.

[0047] Further, the user health data acquisition module includes a user health data acquisition unit and a health dynamic record library construction unit;

[0048] The user health data acquisition unit obtains the user's historical health record data by the user uploading electronic health record data and by document scanning of the user's paper health record data;

[0049] The health dynamic record library construction unit stores the user's historical health record data by constructing a health dynamic record library, and collects the user's real-time health data by associating with the user's real-time health data acquisition device; realizes user identity authentication by generating a user dynamic identity code for the health dynamic record library, and opens data access and operation permissions based on the verification result.

[0050] Further, the user status analysis module includes a periodic health data division unit and a periodic user status matrix construction unit;

[0051] The periodic health data division unit divides the user health data recorded in the health dynamic record library according to the data acquisition period set by the user based on the data acquisition time data; numbers and marks each period based on the periodic division of the user health data, and analyzes the steady-state distribution of the user health data in each period;

[0052] The periodic user status matrix construction unit retrieves the health data of users in each period, and respectively performs anomaly judgment on the health data corresponding to each time point in the period; by introducing the reference interval of the health data of the corresponding type of users for comparison, if there is data that does not belong to the interval, it marks it as an anomaly; otherwise, it calculates the mean value of the health data of the corresponding type of users in the period, and performs steady-state distribution analysis on the health data of the corresponding type of users in the period based on the mean value of the health data of the corresponding type of users; compares the health data of the corresponding type of users at each time point in the period with the mean value to obtain the steady-state distribution index of the health data of the corresponding type of users at the corresponding time point; according to the comparison result, calculates the absolute value of the difference between the health data of the corresponding type of users at each time point in the period and the mean value, and calculates the ratio of the calculated data to the mean value to obtain the fluctuation index of the health data of the corresponding type of users at each time point in the period.

[0053] Combining the steady-state distribution index and the fluctuation index of the health data of the corresponding type of users at each time point in the period, constructs the distribution feature vector of the health data of the corresponding type of users at the corresponding time point; based on the distribution feature vectors of the health data of the corresponding type of users at each time point in the period, constructs the periodic user status matrix for the health data of the users in the period.

[0054] Further, the state offset evaluation module includes a periodic state transition analysis unit and a periodic health data offset evaluation unit;

[0055] The periodic state transition analysis unit arbitrarily selects the user status matrices of adjacent periods, and calculates the absolute value of the difference between the user status matrices of adjacent periods, and respectively calculates the absolute value of the difference between the elements at the same coordinate positions in the user status matrices of adjacent periods to obtain the calculation data of the absolute value of the difference between the distribution feature vectors of the health data of the corresponding type of users at the corresponding time point; according to the calculation result, obtains the transfer data of the distribution feature vectors of the health data of the corresponding type of users at the corresponding time point in the user status matrices of adjacent periods, and constructs the adjacent period user state transition matrix;

[0056] The periodic health data offset evaluation unit performs offset divergence analysis on the distribution feature vectors of user health data of corresponding types at corresponding time points of elements at the same position based on the user state matrices of adjacent periods; according to the offset divergence analysis results of each corresponding element at the same position in the user state matrices of adjacent periods, analyzes the comprehensive offset divergence from the previous-period user state matrix to the next-period user state matrix in the user state matrices of adjacent periods; obtains the corresponding vector modulus data of the adjacent-period user state transition matrix by performing vector transformation on the adjacent-period user state transition matrix; multiplies the comprehensive offset divergence analysis data of the adjacent-period user state matrix by the corresponding vector modulus data of the adjacent-period user state transition matrix to obtain the offset index of the adjacent-period user health data, and judges the abnormal offset of the user's health data within the adjacent period according to the comparison with the index threshold.

[0057] Further, the visualization output module includes a visualization output unit and an anomaly prompt unit;

[0058] The visualization output unit outputs the user's periodic health data through a visualization interface; by constructing a data management platform on the visualization interface of the service application, records and displays the user's periodic health data and series analysis data, and generates a unique exclusive QR code for the user's full-life cycle health management, for transmitting or transferring the user's health data, etc.;

[0059] The anomaly prompt unit outputs the periodic health data offset evaluation data of the user, marks the periodic health data with abnormal offset, and prompts data anomalies;

[0060] As Figure 2 shown, the present invention provides another technical solution:

[0061] A health record management method based on data visualization, the method includes the following steps:

[0062] Obtain the user's historical health data, and construct a health dynamic record library to collect the user's health data in real time;

[0063] Analyze the steady-state distribution of the user's periodic health data based on the user's health dynamic record library; construct the corresponding periodic user state matrix according to the steady-state distribution data of the user's health data in each period;

[0064] Analyze the state transition data of the user within the adjacent periods by combining the user state matrices of adjacent periods to obtain the adjacent-period user state transition matrix; analyze the offset degree of the user's health data within the adjacent periods based on the adjacent-period user state transition matrix, and perform anomaly prompts based on the analysis data.

[0065] Further, the user's historical health data includes the user's electronic health record data and paper health record data; the user's historical health record data is obtained by the user uploading the electronic health record data and by scanning the user's paper health record data.

[0066] The user's historical health record data is stored by constructing a health dynamic record library, and the real-time user health data is collected by associating with the user's real-time health data collection device; the health data includes the user's heart rate data, blood pressure data, body temperature data, etc.; the associated user's real-time health data collection device realizes data collection through signal transmission via the Internet of Things.

[0067] The user's identity is authenticated by generating a user dynamic identity code for the health dynamic record library, and data access and operation permissions are opened based on the verification result; among them, the user dynamic identity code is sent to the user's mobile device, and after the user successfully authenticates the identity through the user dynamic identity code, the user can access and operate the data in the health dynamic record library.

[0068] Further, according to the data collection period set by the user, the user's health data recorded in the health dynamic record library is divided into periods according to the data collection time data; based on the period division of the user's health data, each period is numbered and marked, and the data steady-state distribution analysis is carried out on the user's health data in each period.

[0069] The user's health data in each period is retrieved, and the abnormality of the corresponding health data at each time point within the period is judged; by introducing the reference interval of the corresponding type of user health data for comparison, if there is data that does not belong to the interval, it is marked as abnormal; otherwise, the mean value of the corresponding type of user health data within the period is calculated, and the data steady-state distribution analysis of the corresponding type of user health data within the period is carried out based on the mean value of the corresponding type of user health data; the corresponding type of user health data at each time point within the period is compared with the mean value to obtain the steady-state distribution index of the corresponding type of user health data at the corresponding time point; if it is greater than the mean value, the steady-state distribution index of the corresponding type of user health data at the corresponding time point is recorded as 1; if it is equal to the mean value, the steady-state distribution index of the corresponding type of user health data at the corresponding time point is recorded as 0; if it is less than the mean value, the steady-state distribution index of the corresponding type of user health data at the corresponding time point is recorded as -1; according to the comparison result, the absolute value of the difference between the corresponding type of user health data at each time point within the period and the mean value is calculated, and the calculated data is compared with the mean value to obtain the fluctuation index of the corresponding type of user health data at each time point within the period.

[0070] Combined with the steady-state distribution index and the fluctuation index of the health data of users of corresponding types at each time point within the period, construct the distribution feature vector of the health data of users of corresponding types at the corresponding time point, denoted as a(x,y); where x is the steady-state distribution index; y is the fluctuation index; based on the distribution feature vectors of the health data of users of corresponding types at each time point within the period, construct the periodic user state matrix for the health data of users within the period, denoted as A n (m×t); where n is the period number; m is the numbering of the types of user health data included in the vertical axis of the periodic user state matrix; t is the numbering of the time points within the period on the horizontal axis of the periodic user state matrix; among them, each element within the periodic user state matrix is the distribution feature vector of the health data of users of corresponding types at the corresponding time point.

[0071] Furthermore, arbitrarily select the user state matrices of adjacent periods, calculate the absolute value of the difference between the user state matrices of adjacent periods, calculate the absolute value of the difference for the elements at the same coordinate positions in the user state matrices of adjacent periods respectively, and obtain the absolute value calculation data of the difference of the distribution feature vectors of the health data of users of corresponding types at the corresponding time points; according to the calculation results, obtain the transfer data of the distribution feature vectors of the health data of users of corresponding types at the corresponding time points in the user state matrices of adjacent periods, and construct the user state transition matrix of adjacent periods;

[0072] Based on the user state matrices of adjacent periods, perform offset divergence analysis on the distribution feature vectors of the health data of users of corresponding types at the corresponding time points of the elements at the same positions respectively; according to the offset divergence analysis results of the elements at the same corresponding positions in the user state matrices of adjacent periods, analyze the comprehensive offset divergence from the user state matrix of the previous period to the user state matrix of the next period in the user state matrices of adjacent periods; through vector transformation of the user state transition matrix of adjacent periods, obtain the corresponding vector modulus data of the user state transition matrix of adjacent periods; by multiplying the comprehensive offset divergence analysis data of the user state matrices of adjacent periods by the corresponding vector modulus data of the user state transition matrix of adjacent periods, obtain the offset index of the health data of adjacent periods, and judge the abnormal offset of the health data of users within adjacent periods according to the comparison of the index threshold; among them, if the offset index of the health data of adjacent periods is greater than the threshold, it is prompted that the data offset is abnormal; otherwise, it is normal, and continue to perform periodic offset analysis on the health data of users; among them, performing offset divergence analysis on the distribution feature vectors of the health data of users of corresponding types at the corresponding time points of the elements at the same positions respectively is specifically

[0073]

[0074] Among them, D(A n (a ij ) : A n+1 (a ij)) corresponds to the offset divergence of the elements at the same position in the user status matrices of adjacent cycles; |A n (a ij )| and |A n+1 (a ij )| correspond to the modulus of the distribution feature vector of the health data of the corresponding type of users at the corresponding time points of the elements at the same position in the user status matrices of adjacent cycles; i and j are the element position coordinates in the matrix;

[0075] Analyze the comprehensive offset divergence of the previous cycle user status matrix pointing to the next cycle user status matrix in the adjacent cycle user status matrix, specifically

[0076]

[0077] Among them, D(A n : A n+1 ) is the comprehensive offset divergence of the adjacent cycle user status matrix.

[0078] Furthermore, output the periodic health data of the user through a visualization interface; the visualization interface includes displaying the health data of the user in the forms of tables, pie charts, curves, and human models; by constructing a data management platform in the visualization interface of the service application, record and display the periodic health data and series analysis data of the user, and generate a unique exclusive QR code for the user's full life cycle health management, and transmit or transfer the user's health data, etc.;

[0079] Output the offset evaluation data of the user's periodic health data, and mark the periodic health data with abnormal offsets to prompt data anomalies; among them, for the offset evaluation data of the user's health data, the evaluation results of single-type periodic data or the combined evaluation results of multi-type periodic data can be output according to the user's needs.

[0080] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present invention. Any reference signs in the claims should not be regarded as limiting the claimed rights.

Claims

1. A health record management method based on data visualization, characterized in that: The method includes the following steps: Obtain the user's historical health data, and construct a health dynamic record library to collect the user's health data in real time; Analyze the steady-state distribution of the user's periodic health data based on the user's health dynamic record library; construct the user state matrix for the corresponding period according to the steady-state distribution data of the user's periodic health data; Analyze the state transition data of the user within adjacent periods by combining the user state matrices of adjacent periods to obtain the user state transition matrix of adjacent periods; analyze the degree of deviation of the user's health data within adjacent periods based on the user state transition matrix of adjacent periods, and perform an anomaly prompt based on the analysis data.

2. A health record management method based on data visualization according to claim 1, characterized in that: The user's historical health data includes the user's electronic health record data and paper health record data; the user's historical health record data is obtained by the user uploading the electronic health record data and by scanning the user's paper health record data; Store the user's historical health record data by constructing a health dynamic record library, and collect the user's real-time health data by associating the user's real-time health data collection device; Authenticate the user's identity by generating a user dynamic identification code for the health dynamic record library, and open the data access and operation permissions based on the verification result.

3. A health record management method based on data visualization according to claim 2, characterized in that: Divide the user's health data recorded in the health dynamic record library according to the data collection period set by the user based on the data collection time data; based on the periodic division of the user's health data, number and mark each period, and perform a steady-state distribution analysis on the user's health data for each period; Retrieve the user's health data for each period, and respectively perform an anomaly judgment on the corresponding health data at each time point within the period; by introducing a reference interval for the corresponding type of user health data for comparison, if there is data that does not belong to the interval, mark it as an anomaly; otherwise, calculate the average value of the corresponding type of user health data within the period, and perform a steady-state distribution analysis on the corresponding type of user health data within the period based on the average value of the corresponding type of user health data; compare the corresponding type of user health data at each time point within the period with the average value to obtain the steady-state distribution index of the corresponding type of user health data at the corresponding time point; according to the comparison result, calculate the absolute value of the difference between the corresponding type of user health data at each time point within the period and the average value, and calculate the ratio of the calculated data to the average value to obtain the fluctuation index of the corresponding type of user health data at each time point within the period; Combine the steady-state distribution index and the fluctuation index of the corresponding type of user health data at each time point within the period to construct the distribution feature vector of the corresponding type of user health data at the corresponding time point; based on the distribution feature vector of the corresponding type of user health data at each time point within the period, construct the user state matrix for the user's health data within the period.

4. A health record management method based on data visualization according to claim 3, characterized in that: Arbitrarily select adjacent - period user - status matrices, and calculate the absolute value of the difference between adjacent - period user - status matrices. Calculate the absolute value of the difference for elements at the same coordinate positions in adjacent - period user - status matrices respectively, to obtain the absolute - value calculation data of the distribution feature vectors of user health data of corresponding types at corresponding time points. According to the calculation results, obtain the transfer data of the distribution feature vectors of user health data of corresponding types at corresponding time points in adjacent - period user - status matrices, and construct an adjacent - period user - status transfer matrix. Based on adjacent - period user - status matrices, perform offset - divergence analysis on the distribution feature vectors of user health data of corresponding types at corresponding time points of elements at the same position respectively. According to the offset - divergence analysis results of each corresponding element at the same position in adjacent - period user - status matrices, analyze the comprehensive offset divergence from the previous - period user - status matrix to the next - period user - status matrix in adjacent - period user - status matrices. By performing vector transformation on the adjacent - period user - status transfer matrix, obtain the corresponding vector - norm data of the adjacent - period user - status transfer matrix. By multiplying the comprehensive offset - divergence analysis data of adjacent - period user - status matrices by the corresponding vector - norm data of adjacent - period user - status transfer matrices, obtain the offset index of adjacent - period user health data, and judge the abnormal offset of user health data within adjacent periods according to the comparison with the index threshold.

5. The method for health record management based on data visualization according to claim 4, characterized in that: Output the periodic health data of the user through a visualization interface. Output the offset - evaluation data of the user's periodic health data, and mark the abnormally - offset periodic health data to prompt data anomalies.

6. A health record management system based on data visualization, characterized in that: The system includes a user - health - data acquisition module, a user - status analysis module, a status - offset evaluation module, and a visualization output module. The user - health - data acquisition module acquires the user's historical health data and constructs a health - dynamic record library to collect the user's health data in real - time. The user - status analysis module analyzes the steady - state distribution of the user's periodic health data based on the user's health - dynamic record library. Construct a corresponding - period user - status matrix according to the steady - state distribution data of the user's health data in each period. The status - offset evaluation module analyzes the state - transfer data of the user within adjacent periods by combining adjacent - period user - status matrices to obtain an adjacent - period user - status transfer matrix. Analyze the offset degree of the user's health data within adjacent periods based on the adjacent - period user - status transfer matrix, and give an abnormal prompt based on the analysis data. The visualization output module visually outputs the user's periodic health data and gives a prompt for abnormal data.

7. A health record management system based on data visualization according to claim 6, characterized in that: The user - health - data acquisition module includes a user - health - data acquisition unit and a health - dynamic record - library construction unit. The user - health - data acquisition unit obtains the user's historical health record data by the user uploading electronic health - record data and by scanning the user's paper - based health - record data. The health dynamic record library construction unit stores the user's historical health record data by constructing a health dynamic record library, and collects the real-time user health data by associating with the real-time health data collection device of the user; authenticates the user's identity by generating a user dynamic identity code for the health dynamic record library, and opens the data access and operation permissions based on the verification result.

8. A health record management system based on data visualization according to claim 7, characterized in that: The user status analysis module includes a periodic health data division unit and a periodic user status matrix construction unit; The periodic health data division unit divides the user health data recorded in the health dynamic record library according to the data collection period set by the user based on the data collection time data; Based on the periodic division of the user health data, each period is numbered and marked, and the data steady-state distribution analysis is performed on the user health data of each period; The periodic user status matrix construction unit retrieves the user health data of each period, and respectively performs an abnormality judgment on the corresponding health data at each time point within the period; by introducing the reference interval of the corresponding type of user health data for comparison, if there is data that does not belong to the interval, it is marked as abnormal; otherwise, the average value of the corresponding type of user health data within the period is calculated, and the steady-state distribution analysis of the corresponding type of user health data within the period is performed based on the average value of the corresponding type of user health data; the corresponding type of user health data at each time point within the period is compared with the average value to obtain the steady-state distribution index of the corresponding type of user health data at the corresponding time point; according to the comparison result, the absolute value of the difference between the corresponding type of user health data at each time point within the period and the average value is calculated, and the calculated data is divided by the average value to obtain the fluctuation index of the corresponding type of user health data at each time point within the period; Combining the steady-state distribution index and the fluctuation index of the corresponding type of user health data at each time point within the period, a distribution feature vector of the corresponding type of user health data at the corresponding time point is constructed; based on the distribution feature vectors of the corresponding type of user health data at each time point within the period, a periodic user status matrix of the user's health data within the period is constructed.

9. The health record management system based on data visualization according to claim 8, wherein: The status deviation evaluation module includes a periodic status transfer analysis unit and a periodic health data deviation evaluation unit; The periodic status transfer analysis unit arbitrarily selects adjacent periodic user status matrices, calculates the absolute value of the difference between the adjacent periodic user status matrices, and respectively calculates the absolute value of the difference between the elements at the same coordinate positions in the adjacent periodic user status matrices to obtain the absolute value calculation data of the distribution feature vectors of the corresponding type of user health data at the corresponding time points; according to the calculation result, the transfer data of the distribution feature vectors of the corresponding type of user health data at the corresponding time points in the adjacent periodic user status matrices is obtained, and an adjacent periodic user status transfer matrix is constructed; The periodic health data offset evaluation unit analyzes the offset divergence of the distribution feature vectors of the corresponding type of user health data at the corresponding time points of the elements at the same position based on the user state matrices of adjacent periods; analyzes the comprehensive offset divergence from the user state matrix of the previous period to the user state matrix of the next period in the user state matrices of adjacent periods according to the offset divergence analysis results of the corresponding elements at the same position in the user state matrices of adjacent periods; obtains the vector modulus data corresponding to the user state transition matrix of adjacent periods by performing vector transformation on the user state transition matrix of adjacent periods; obtains the offset index of the user health data of adjacent periods by multiplying the comprehensive offset divergence analysis data of the user state matrices of adjacent periods by the vector modulus data corresponding to the user state transition matrix of adjacent periods, and judges the abnormal offset of the user's health data within adjacent periods according to the comparison of the index threshold.

10. A health record management system based on data visualization according to claim 9, characterized in that: The visualization output module includes a visualization output unit and an anomaly prompt unit; The visualization output unit outputs the periodic health data of the user through a visualization interface; The anomaly prompt unit outputs the offset evaluation data of the periodic health data of the user, marks the periodic health data with abnormal offset, and prompts data anomalies.

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