Intelligent assessment method and device for physical and psychological health state

Abstract

The invention relates to an intelligent assessment method and device for a physical and psychological health state, computer equipment, a readable storage medium and a program product. The method comprises the following steps: acquiring target health data of a target user; mapping the target health data to a target body mapping value and a target psychological mapping value; matching the target body mapping value with a Mandelbrot Mandebt dataset to determine the body state of the target user, and matching the target psychological mapping value with a Julia Zhitou dataset to determine the psychological state of the target user; and determining a health result of the target user according to the physical state and the psychological state of the target user. By adopting the method, the accuracy of health assessment can be improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium and computer program product for intelligent assessment of physical and mental health status. Background Art

[0002] As the pace of society accelerates, life pressure and other factors lead to increased physical and mental burdens on people, physical and mental health issues are becoming increasingly prominent, prompting people to pay attention to the maintenance of physical and mental health. Traditional health assessment methods usually use simple linear systems to assess the user's physical and mental health status, which has the problem of low accuracy of health assessment. Summary of the invention

[0003] Based on this, it is necessary to provide an intelligent assessment method, device, computer equipment, computer-readable storage medium and computer program product for physical and mental health status that can improve accuracy in response to the above technical problems.

[0004] In a first aspect, the present application provides an intelligent assessment method for physical and mental health status, comprising:

[0005] Obtain target health data of target users;

[0006] mapping the target health data to a target body mapping value and a target mental mapping value;

[0007] Matching the target body mapping value with the Mandelbrot dataset to determine the physical state of the target user, and matching the target psychological mapping value with the Julia dataset to determine the psychological state of the target user;

[0008] A health outcome of the target user is determined according to the physical state and psychological state of the target user.

[0009] In one embodiment, mapping the target health data to a target body mapping value and a target psychological mapping value comprises:

[0010] Convert each of the target health data into a complex health matrix;

[0011] Get the dimension reduction transformation matrix;

[0012] Based on the complex health matrix and the dimension reduction transformation matrix, a target body mapping value and a target psychological mapping value are obtained.

[0013] In one embodiment, converting each of the target health data into a complex health matrix includes:

[0014] For each of the target health data, the target health data is upgraded to a two-dimensional complex plane to obtain first health data; the two-dimensional complex plane includes a real axis and an imaginary axis, the real axis is used to represent the degree of impact on the body, and the imaginary axis is used to represent the degree of impact on the psychology;

[0015] A complex health matrix is ​​obtained based on each of the first health data.

[0016] In one of the embodiments, the number of periodic points corresponding to the subset in the Mandelbrot data set is negatively correlated with the health of the target user's physical state, and the discreteness of the Julia data set is negatively correlated with the health of the target user's psychological state.

[0017] In one embodiment, the method further comprises:

[0018] Get training health data;

[0019] mapping the training health data to training body mapping values ​​and training mind mapping values;

[0020] Generate a Mandelbrot data set and a Julia data set based on the training body mapping value and the training psychological mapping value;

[0021] Adjusting parameters of the chaos model according to the Mandelbrot data set and the Julia data set until a trained chaos model is obtained;

[0022] The step of mapping the target health data to target body mapping values ​​and target psychological mapping values ​​is performed through the trained chaotic model.

[0023] In one embodiment, generating a Mandelbrot data set and a Julia data set based on the training body mapping value and the training psychological mapping value comprises:

[0024] Controlling the target psychological mapping value to remain unchanged, obtaining the aggregate value of the target body mapping value, and obtaining a Mandelbrot data set based on the aggregate values ​​of each target body mapping value;

[0025] The target body mapping value is controlled to remain unchanged, an aggregate value of the target psychological mapping value is obtained, and a Julia data set is obtained based on the aggregate values ​​of the target psychological mapping values.

[0026] In a second aspect, the present application also provides an intelligent assessment device for physical and mental health status, including:

[0027] A data acquisition module is used to acquire target health data of target users;

[0028] A data mapping module, used for mapping the target health data to a target body mapping value and a target psychological mapping value;

[0029] A user state determination module, used to match the target body mapping value with the Mandelbrot data set to determine the physical state of the target user, and to match the target psychological mapping value with the Julia data set to determine the psychological state of the target user;

[0030] The health result determination module is used to determine the health result of the target user according to the physical state and psychological state of the target user.

[0031] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0032] Obtain target health data of target users;

[0033] mapping the target health data to a target body mapping value and a target mental mapping value;

[0034] Matching the target body mapping value with the Mandelbrot dataset to determine the physical state of the target user, and matching the target psychological mapping value with the Julia dataset to determine the psychological state of the target user;

[0035] A health outcome of the target user is determined according to the physical state and psychological state of the target user.

[0036] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0037] Obtain target health data of target users;

[0038] mapping the target health data to a target body mapping value and a target mental mapping value;

[0039] Matching the target body mapping value with the Mandelbrot dataset to determine the physical state of the target user, and matching the target psychological mapping value with the Julia dataset to determine the psychological state of the target user;

[0040] A health outcome of the target user is determined according to the physical state and psychological state of the target user.

[0041] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0042] Obtain target health data of target users;

[0043] mapping the target health data to a target body mapping value and a target mental mapping value;

[0044] Matching the target body mapping value with the Mandelbrot dataset to determine the physical state of the target user, and matching the target psychological mapping value with the Julia dataset to determine the psychological state of the target user;

[0045] A health outcome of the target user is determined according to the physical state and psychological state of the target user.

[0046] The above-mentioned intelligent assessment method, device, computer equipment, computer-readable storage medium and computer program product of physical and mental health status obtain the target health data of the target user, map the target health data to the target body mapping value and the target psychological mapping value, match the target body mapping value with the Mandelbrot data set, and can accurately determine the physical state of the target user, and match the target psychological mapping value with the Julia data set to accurately determine the psychological state of the target user; then, based on the physical state and psychological state of the target user, the health outcome of the target user can be more accurately determined. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0048] Figure 1 is a flow chart of an intelligent assessment method for physical and mental health status in one embodiment;

[0049] Figure 2 A schematic diagram of a Mandelbrot data set in one embodiment;

[0050] Figure 3 A framework diagram of an intelligent assessment method for physical and mental health status in one embodiment;

[0051] Figure 4 is a structural block diagram of an intelligent assessment device for physical and mental health status in one embodiment;

[0052] Figure 5 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0054] In one embodiment, Figure 1 As shown, a method for intelligent assessment of physical and mental health status is provided. This embodiment uses the method applied to a computer device as an example for illustration. The computer device can be a terminal or a server. It can be understood that the method can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. Among them, the terminal can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car devices, projection devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The head-mounted device can be a virtual reality (VR) device, an augmented reality (AR) device, smart glasses, etc. The server can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0055] In this embodiment, the intelligent assessment method for physical and mental health status includes the following steps:

[0056] Step S102, obtaining target health data of the target user.

[0057] Among them, the target health data may include body-related data (such as heart rate, blood pressure, weight, etc.), nutritional information (such as diet records), psychological-related data (such as emotions, stress levels) and behavioral habits (such as exercise volume, sleep quality), etc., but are not limited to these.

[0058] Optionally, the wearable device detects the target user, obtains the target health data of the target user through the mobile application of the wearable device, and uploads the target health data to the computer device; the computer device obtains the uploaded target health data.

[0059] Optionally, the computer device can obtain the uploaded target health data in real time or at a scheduled time.

[0060] It can be understood that through various sensors and data input interfaces of wearable devices, the accuracy and timeliness of data collection can be ensured.

[0061] Optionally, the computer device preprocesses the target health data to obtain preprocessed target health data; and maps the preprocessed target health data to target body mapping values ​​and target psychological mapping values.

[0062] Among them, preprocessing can clean and standardize the collected target health data, eliminate noise and outliers, and ensure data quality; use data standardization and data normalization to ensure data consistency; apply dimensionality reduction techniques (such as PCA) to simplify data structure and reduce computational complexity.

[0063] Step S104, mapping the target health data to the target body mapping value and the target psychological mapping value.

[0064] The target body mapping value is a representation value of the target user's physical state, and the target psychological mapping value is a representation value of the target user's psychological state.

[0065] Optionally, the target health data is input into a trained chaotic model, the target health data is mapped to target body mapping values ​​and target psychological mapping values ​​through the trained chaotic model, the target body mapping values ​​are matched with the Mandelbrot data set to determine the physical state of the target user, the target psychological mapping values ​​are matched with the Julia data set to determine the psychological state of the target user, and the health outcome of the target user is determined based on the physical and psychological state of the target user.

[0066] The computer equipment applies chaos theory to establish a trained chaos model, which is used to identify the complex relationship between the user's health data. The mathematical properties of the Mandelbrot dataset and the Julia dataset are used to study the complexity and self-similarity of the user's physical and mental health status. The Mandelbrot dataset and the Julia dataset can visualize the dynamic changes of health data and help identify potential health risks. By analyzing the fractal characteristics of these sets, the mutual influence of users in physical and mental health is revealed, thereby providing deeper health insights.

[0067] Optionally, mapping the target health data to target body mapping values ​​and target psychological mapping values ​​includes: converting each target health data into a complex health matrix; obtaining a dimensionality reduction transformation matrix; and obtaining target body mapping values ​​and target psychological mapping values ​​based on the complex health matrix and the dimensionality reduction transformation matrix.

[0068] Among them, the complex health matrix is ​​a 1-row N-column matrix, the dimension reduction transformation matrix is ​​an N-row 2-column matrix, and N is the number of target health data. The dimension reduction transformation matrix can be set as needed and is an empirical parameter.

[0069] Optionally, the computer device normalizes each target health data to obtain each normalized target health data, and converts each normalized target health data into a complex health matrix.

[0070] Optionally, each target health data is converted into a complex health matrix, including: for each target health data, upgrading the target health data to a two-dimensional complex plane to obtain first health data; the two-dimensional complex plane includes a real axis and an imaginary axis, the real axis is used to characterize the degree of impact on the body, and the imaginary axis is used to characterize the degree of impact on the mind; based on each first health data, a complex health matrix is ​​obtained.

[0071] Optionally, the computer device obtains N target health data For each target health data, the following formula (1) is used to upgrade the target health data to a two-dimensional complex plane to obtain the first health data: (1) in, is the kth first health data, is the kth target health data, is an empirical parameter.

[0074] It can be understood that the two-dimensional complex plane includes a real axis and an imaginary axis. After the target health data is upgraded to the two-dimensional complex plane, the data of the first health data on the real axis represents the degree of impact on the target user's body, and the data of the first health data on the imaginary axis represents the degree of impact on the target user's psychology.

[0075] The computer device takes each first health data as a dimension to construct a 1*N (1 row and N columns) complex health matrix.

[0076] Optionally, the computer device multiplies the complex health matrix and the dimensionality reduction transformation matrix to obtain target body mapping values ​​and target psychological mapping values.

[0077] The computer device can use the following formula (2) to obtain the target body mapping value and the target psychological mapping value:

[0078] (2)

[0079] in, is the complex health matrix, is the dimension reduction transformation matrix, E is the target mental mapping value, and C is the target physical mapping value.

[0080] Step S106, matching the target body mapping value with the Mandelbrot dataset to determine the target user's physical state, and matching the target psychological mapping value with the Julia dataset to determine the target user's psychological state.

[0081] The Mandelbrot data set is a set of points on the complex plane that satisfy a specific iterative function condition. Specifically, if a complex number C is in the iterative function Under the action of , after multiple iterations, its absolute value will not diverge (that is, it will remain within a finite range), then the complex number C belongs to the Mandelbrot data set. The Julia data set is a set of points that form a fractal on the complex plane; the Julia data set can be obtained by repeatedly iterating the formula: For a fixed complex number C, taking a certain Z value, you can get a sequence, which may diverge to infinity or always stay within a certain range and converge to a certain value. The set of z values ​​that will make it not diverge is called the Julia set.

[0082] Optionally, the number of periodic points corresponding to the subset in the Mandelbrot data set is negatively correlated with the health of the target user's physical state, and the discreteness of the Julia data set is negatively correlated with the health of the target user's psychological state.

[0083] Among them, the Mandelbrot data set includes multiple subsets, and the number of periodic points corresponding to each subset is submitted in sequence, and the periodic points are also the convergence points. The fewer the number of periodic points corresponding to the subsets in the Mandelbrot data set, the healthier the target user's physical condition is, that is, the healthier the target user is. For example, if the target body mapping value of the target user matches the subset with the number of periodic points of 1 in the Mandelbrot data set, it means that the target user's physical condition is healthy; if the target body mapping value of the target user matches the subset of the Mandelbrot data set except the subset with the number of periodic points of 1, it means that the target user's physical condition is sub-healthy, and the larger the number of periodic points in the subset, the worse the physical condition of the target user and the more symptoms he has. Figure 2As shown, the number of periodic points indicates the number of data points that the subset converges to. The subset with a periodic point of 1 is mainly heart-shaped: main area, heart-shaped area; the subset with a periodic point of 2 is mainly disk: largest disk, first-level disk; and so on. The smaller the number of periodic points in a subset, the larger the area, and the healthier the user's physical condition corresponding to the area to which the subset belongs. ‌‌

[0084] Step S108, determining the health result of the target user according to the physical state and psychological state of the target user.

[0085] Optionally, the computer device analyzes the target user's physical and psychological state according to the target user's physical and psychological state to determine the target user's health results. The health results may include physical results and psychological results.

[0086] Optionally, the computer device performs real-time evaluation on the health status of the target user and generates a health score and an analysis report. The health result includes the health score and the analysis report.

[0087] Optionally, the computer device displays the health results of the target user on the screen.

[0088] Optionally, the computer device uses machine learning and recommendation algorithms to generate personalized health advice based on the health results of the target user, and displays the personalized health advice on the screen; the personalized health advice includes precise nutrition plans, healthy eating plans, healthy lifestyles, and psychological counseling and companionship.

[0089] Computer equipment can be designed with user-friendly interfaces to display health assessment results and recommendations so that target users can easily obtain information; provide personalized health management plans and allow users to record feedback to promote user participation and compliance; implement data encryption and anonymization technologies to ensure the security and privacy of user health data; comply with relevant laws and regulations, establish a user data protection mechanism, and enhance user trust in the system.

[0090] It can be understood that based on the constructed Mandelbrot dataset and Julia dataset, the user's health status can be evaluated in real time to generate health scores and analysis reports, which can combine the user's individual characteristics and analyze the relative importance of different health indicators, thereby providing a comprehensive health assessment result.

[0091] The above-mentioned intelligent assessment method of physical and mental health status obtains the target health data of the target user, maps the target health data to the target body mapping value and the target psychological mapping value, matches the target body mapping value with the Mandelbrot data set, and can accurately determine the physical state of the target user, and matches the target psychological mapping value with the Julia data set, and can accurately determine the psychological state of the target user; then, based on the physical state and psychological state of the target user, the health outcome of the target user can be more accurately determined.

[0092] In health monitoring technology: it involves real-time monitoring and evaluation of users' health status through technical means; in personalized medicine: it focuses on providing customized health management plans based on individual characteristics and needs; in artificial intelligence and data analysis: it uses chaos models and data analysis techniques to process users' physical, nutritional, psychological and behavioral indicators to provide scientific health advice; in biomedical engineering: it involves related technologies for biosignal acquisition and processing; in mental health and behavioral science: it considers the impact of users' psychological and behavioral factors on health.

[0093] It can be understood that by obtaining various health data, various health data of users can be effectively integrated to comprehensively evaluate the health status of users; chaos models can be used to analyze the unique health data of individuals to generate targeted and personalized health intervention plans; there are complex nonlinear relationships between human health indicators. Through the application of chaos models, the ability to identify and predict dynamic changes between health indicators can be improved; real-time monitoring and feedback issues can achieve real-time monitoring of user health status and provide timely feedback, which is the key to improving health management efficiency. Through advanced algorithms and models, it aims to achieve efficient real-time data processing and feedback mechanisms; improving user participation and compliance in health management is a key factor in the success of health intervention. By providing personalized suggestions and easy-to-understand information, users' sense of participation and enthusiasm can be enhanced; in the process of collecting and processing user health data, data security and user privacy protection are ensured; which helps to improve the effectiveness of personalized health monitoring systems and user experience.

[0094] In one embodiment, the above method also includes: obtaining training health data; mapping the training health data to training body mapping values ​​and training psychological mapping values; generating a Mandelbrot data set and a Julia data set based on the training body mapping values ​​and the training psychological mapping values; adjusting parameters of the chaos model according to the Mandelbrot data set and the Julia data set until a trained chaos model is obtained; and executing the step of mapping the target health data to the target body mapping values ​​and the target psychological mapping values ​​through the trained chaos model.

[0095] The training health data includes individual health data and public health data. Individual health data includes data related to individual health, and public health data includes health data of public nature such as race, ethnicity and gender.

[0096] Optionally, the computer device preprocesses the training health data to obtain preprocessed training health data, and maps the preprocessed training health data to the training body mapping value and the training psychological mapping value. The preprocessing method is as described above and will not be repeated here.

[0097] Optionally, the computer device converts each training health data into a complex health matrix, obtains a dimensionality reduction transformation matrix, and obtains a training body mapping value and a training psychological mapping value based on the complex health matrix and the dimensionality reduction transformation matrix corresponding to the training health data.

[0098] For each training health data, the training health data is upgraded to a two-dimensional complex plane to obtain second health data; the two-dimensional complex plane includes a real axis and an imaginary axis, the real axis is used to characterize the degree of impact on the body, and the imaginary axis is used to characterize the degree of impact on the psychology; based on each second health data, a complex health matrix corresponding to the training health data is obtained.

[0099] Optionally, based on the training body mapping values ​​and the training psychological mapping values, a Mandelbrot data set and a Julia data set are generated, including: controlling the target psychological mapping value to remain unchanged, obtaining the aggregate value of the target body mapping value, and obtaining the Mandelbrot data set based on the aggregate value of each target body mapping value; controlling the target body mapping value to remain unchanged, obtaining the aggregate value of the target psychological mapping value, and obtaining the Julia data set based on the aggregate value of each target psychological mapping value.

[0100] Among them, aggregate value refers to the content selection, analysis, and classification of data in information science, and ultimately obtaining the desired results.

[0101] In this embodiment, the computer device can obtain the trained chaotic model more accurately by training the chaotic model, and then more accurately map the target health data to the target body mapping value and the target psychological mapping value, and determine the user's health results.

[0102] In one embodiment, Figure 3As shown, the computer device obtains training health data through a data source, and the training health data includes individual health data and public health data, and the training health data includes height, weight, gender, age, liver function, kidney function, blood routine, blood lipids, blood pressure, blood sugar, body fat information, sub-health symptom information, interpersonal relationship and mental health information, heart rate, body temperature, body movement and sleep; the training health data is input into the chaos model layer, and the training health data is mapped to the training body mapping value and the training psychological mapping value through the chaos model layer; based on the training body mapping value and the training psychological mapping value, the Mandelbrot data set and the Julia data set are generated; through The test training manager performs model training and evaluation optimization based on the Mandelbrot dataset and the Julia dataset, and the fine-tuning manager adjusts the parameters of each sub-model in the application fine-tuning layer, such as the effective operating points of the core model in immune system management, the effective operating points of the Lorenz model in sleep quality management, the effective operating points of the Vanderpol model in basal body temperature management, the effective operating points of the digital human model in digital human management, and the effective operating points of the symptom model in pain management. Accurate nutrition plans, healthy diet plans, healthy lifestyles, and psychological counseling and companionship in the execution layer can be obtained.

[0103] In this embodiment, chaos theory is introduced into personalized health monitoring for the first time, revealing the nonlinear dynamic characteristics of health data and enhancing the understanding of complex health relationships; multi-dimensional data integration: by comprehensively analyzing multi-dimensional health data such as physical, nutritional, psychological and behavioral data, a comprehensive health assessment is provided, which makes up for the limitations of traditional health monitoring; the use of Mandelbrot set and Julia set models: innovatively applying the Mandelbrot set and Julia set models, using their fractal characteristics to analyze the complexity of physical and mental health, and providing visual dynamic changes in health; personalized health advice generation: based on deep data analysis and machine learning algorithms, customized health advice is generated for users to achieve truly personalized health management; real-time feedback mechanism: a dynamic feedback mechanism is established to adjust health advice in a timely manner according to the user's health changes and feedback, thereby improving the effectiveness of intervention; data security and privacy protection: in the process of health data collection and processing, the security and privacy protection of user data are emphasized to enhance user trust.

[0104] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0105] Based on the same inventive concept, the embodiment of the present application also provides an intelligent assessment device for physical and mental health status for implementing the intelligent assessment method for physical and mental health status involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of one or more intelligent assessment devices for physical and mental health status provided below can refer to the limitations of the intelligent assessment method for physical and mental health status above, and will not be repeated here.

[0106] In an exemplary embodiment, Figure 4 As shown, a smart assessment device for physical and mental health status is provided, including: a data acquisition module 402, a data mapping module 404, a user status determination module 406 and a health result determination module 408, wherein:

[0107] The data acquisition module 402 is used to acquire the target health data of the target user;

[0108] A data mapping module 404, for mapping target health data to target body mapping values ​​and target psychological mapping values;

[0109] A user state determination module 406 is used to match the target body mapping value with the Mandelbrot data set to determine the physical state of the target user, and to match the target psychological mapping value with the Julia data set to determine the psychological state of the target user;

[0110] The health result determination module 408 is used to determine the health result of the target user according to the physical state and psychological state of the target user.

[0111] The above-mentioned intelligent assessment device for physical and mental health status obtains the target health data of the target user, maps the target health data to the target body mapping value and the target psychological mapping value, matches the target body mapping value with the Mandelbrot data set, and can accurately determine the physical state of the target user, and matches the target psychological mapping value with the Julia data set, and can accurately determine the psychological state of the target user; then, based on the physical state and psychological state of the target user, the health results of the target user can be more accurately determined.

[0112] In one embodiment, the data mapping module 404 is also used to convert each target health data into a complex health matrix; obtain a dimensionality reduction transformation matrix; and obtain a target body mapping value and a target psychological mapping value based on the complex health matrix and the dimensionality reduction transformation matrix.

[0113] In one embodiment, the above-mentioned data mapping module 404 is also used to upgrade the target health data to a two-dimensional complex plane for each target health data to obtain first health data; the two-dimensional complex plane includes a real axis and an imaginary axis, the real axis is used to characterize the degree of impact on the body, and the imaginary axis is used to characterize the degree of impact on the psychology; based on each first health data, a complex health matrix is ​​obtained.

[0114] In one embodiment, the number of periodic points corresponding to the subset in the Mandelbrot data set is negatively correlated with the health of the target user's physical state, and the discreteness of the Julia data set is negatively correlated with the health of the target user's psychological state.

[0115] In one embodiment, the above-mentioned device also includes a chaos model training module; the chaos model training module is used to obtain training health data; map the training health data to training body mapping values ​​and training psychological mapping values; generate a Mandelbrot data set and a Julia data set based on the training body mapping values ​​and the training psychological mapping values; adjust the parameters of the chaos model according to the Mandelbrot data set and the Julia data set until a trained chaos model is obtained; and execute the step of mapping the target health data to the target body mapping value and the target psychological mapping value through the trained chaos model.

[0116] In one embodiment, the chaos model training module is also used to control the target body mapping value to remain unchanged, obtain the aggregate value of the target psychological mapping value, and obtain the Mandelbrot data set based on the aggregate value of each target psychological mapping value; control the target psychological mapping value to remain unchanged, obtain the aggregate value of the target body mapping value, and obtain the Julia data set based on the aggregate value of each target body mapping value.

[0117] Each module in the above-mentioned intelligent assessment device for physical and mental health status can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0118] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as target health data, trained chaos models, Mandelbrot Mandelbrot data sets and Julia Julia data sets. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an intelligent assessment method for physical and mental health status is realized.

[0119] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0120] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0121] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0122] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0123] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0124] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0125] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0126] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. An intelligent assessment method for physical and mental health status, characterized in that: The method comprises: Obtain target health data of target users; mapping the target health data to a target body mapping value and a target mental mapping value; Matching the target body mapping value with the Mandelbrot dataset to determine the physical state of the target user, and matching the target psychological mapping value with the Julia dataset to determine the psychological state of the target user; A health outcome of the target user is determined according to the physical state and psychological state of the target user.

2. The method according to claim 1, characterized in that Mapping the target health data to a target body mapping value and a target psychological mapping value comprises: Convert each of the target health data into a complex health matrix; Get the dimension reduction transformation matrix; Based on the complex health matrix and the dimension reduction transformation matrix, a target body mapping value and a target psychological mapping value are obtained.

3. The method according to claim 2, characterized in that The step of converting each of the target health data into a complex health matrix comprises: For each of the target health data, the target health data is upgraded to a two-dimensional complex plane to obtain first health data; the two-dimensional complex plane includes a real axis and an imaginary axis, the real axis is used to represent the degree of impact on the body, and the imaginary axis is used to represent the degree of impact on the psychology; A complex health matrix is ​​obtained based on each of the first health data.

4. The method according to claim 1, characterized in that: The number of periodic points corresponding to the subset in the Mandelbrot data set is negatively correlated with the health level of the target user's physical state, and the discreteness of the Julia data set is negatively correlated with the health level of the target user's psychological state.

5. The method according to any one of claims 1 to 4, characterized in that The method further comprises: Get training health data; mapping the training health data to training body mapping values ​​and training mind mapping values; Generate a Mandelbrot data set and a Julia data set based on the training body mapping value and the training psychological mapping value; Adjusting parameters of the chaos model according to the Mandelbrot data set and the Julia data set until a trained chaos model is obtained; The step of mapping the target health data to target body mapping values ​​and target psychological mapping values ​​is performed through the trained chaotic model.

6. The method according to claim 5, characterized in that The step of generating a Mandelbrot data set and a Julia data set based on the training body mapping value and the training psychological mapping value comprises: Controlling the target psychological mapping value to remain unchanged, obtaining an aggregate value of the target body mapping value, and obtaining a Mandelbrot data set based on the aggregate values ​​of each target body mapping value; The target body mapping value is controlled to remain unchanged, an aggregate value of the target psychological mapping value is obtained, and a Julia data set is obtained based on the aggregate values ​​of the target psychological mapping values.

7. An intelligent device for assessing physical and mental health, characterized in that: The device comprises: A data acquisition module is used to acquire target health data of target users; A data mapping module, used for mapping the target health data to a target body mapping value and a target psychological mapping value; A user state determination module, used to match the target body mapping value with the Mandelbrot data set to determine the physical state of the target user, and to match the target psychological mapping value with the Julia data set to determine the psychological state of the target user; The health result determination module is used to determine the health result of the target user according to the physical state and psychological state of the target user.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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