Method and system of intelligent health digital human model

Abstract

The invention discloses an intelligent health digital human model method and system, belongs to the technical field of health management, and can improve the quality of a first intervention scheme selected by a digital human model. Comprising the following steps: acquiring physiological data, psychological data, physical examination report data, personality evaluation data and hobby and interest data of a first user; wherein the psychological data and the physiological data are respectively updated once according to corresponding preset first interval duration; aligning the timestamps of the physiological data and the psychological data to form a daily health data packet; performing standardization processing on the daily health data packet and various data; inputting the standardized data into a digital human model of the health management system, and mapping the standardized data into a preset high-dimensional chaotic iterative function to obtain updated data of the health state of the first user; updating the digital human model based on the update data of the health state; and simulating each predetermined intervention scheme based on the updated digital human model, and selecting the intervention scheme with the optimal simulation effect as a first intervention scheme.

Description

Technical Field

[0001] The present invention relates to the technical field of health management, and in particular to a method and system for an intelligent health digital human model. Background Art

[0002] At present, the diagnosis and treatment methods commonly used by the global medical system mainly rely on physical or chemical therapies such as drugs and surgery. These methods are based on a single linear model based on pathogens and symptoms. The control of the disease is often based on suppressing symptoms, and it is impossible to cure the disease. This method has a certain effect in dealing with emergency diseases such as infectious diseases, but it exposes limitations when dealing with chronic diseases and difficult and complicated diseases.

[0003] Specifically, chronic diseases and difficult and complicated diseases usually have complex multi-causal characteristics and are closely related to the patient's overall physiological and psychological state. Current treatment methods mostly focus on treating local symptoms of the body and ignore the overall recovery of the patient's system (including physiological and psychological systems). Therefore, treatment methods based on a single linear model based on pathogens and symptoms cannot effectively repair the overall health of the human body.

[0004] In order to solve the above technical problems, the invention patent application with application number 200610136844.4 filed by the applicant previously disclosed an intelligent health management method and system, which uses a digital human model to simulate each predetermined intervention plan and selects the intervention plan with the best simulation effect as the first intervention plan, which provides an effective solution selection method for health management to a certain extent. However, in the actual application process, this method gradually exposed some shortcomings: its simulation process may not be accurate enough, resulting in the selected first intervention plan not being the optimal plan in the true sense. Summary of the invention

[0005] The present application provides a method and system for an intelligent healthy digital human model, which can improve the quality of a first intervention plan selected by the digital human model.

[0006] To achieve the above objectives, the present application discloses the following technical solutions:

[0007] In a first aspect, an embodiment of the present application provides a method for an intelligent health digital human model, which is applied to an intelligent health management system, comprising the following steps:

[0008] Acquire the physiological data, psychological data, physical examination report data, personality assessment data, and hobby data of the first user; wherein the psychological data and the physiological data are updated once according to their corresponding preset first interval durations;

[0009] Align the timestamps of physiological and psychological data to form a daily health data package;

[0010] Standardize daily health data packages, physical examination report data, personality assessment data, and interest and hobby data;

[0011] Input the standardized daily health data package, physical examination report data, personality assessment data and hobby data into the digital human model of the health management system, map them into the preset high-dimensional chaotic iterative function, and obtain the updated data of the health status of the first user;

[0012] updating the digital human model based on the updated data of the health status of the first user;

[0013] Each predetermined intervention plan is simulated based on the updated digital human model, and the intervention plan with the best simulation effect is selected as the first intervention plan.

[0014] In the embodiment of the present application, by stipulating that the psychological data and the physiological data are updated according to the corresponding preset first interval duration, the timeliness and dynamics of the data can be ensured, and the user's latest status can be reflected in real time.

[0015] By aligning the timestamps of physiological data and psychological data and integrating them into daily health data packets, data from different sources but at the same time node can be integrated in an orderly manner, facilitating subsequent unified processing.

[0016] By standardizing the integrated daily health data packages and other related data, the data format can be made to meet the requirements of subsequent model operations and avoid deviations caused by inconsistencies in data scales.

[0017] By inputting the standardized data into the digital human model of the health management system and mapping it to the preset high-dimensional chaotic iterative function, the high-dimensional chaotic iterative function can simulate and calculate the user's health status based on the complex internal relationship and change law of the input data, thereby obtaining the updated data of the first user's health status, and then based on the updated digital human model of the updated data, simulate each predetermined intervention plan, select the best intervention plan as the first intervention plan, so that the digital human model can be dynamically adjusted according to the user's latest status. In this way, the digital human model can simulate the effects of each intervention plan more accurately, thereby improving the quality of the selected first intervention plan, and providing users with health management intervention plans that are more in line with their actual situation and have better effects.

[0018] In some possible implementations of the first aspect, the preset high-dimensional chaotic iterative function is as follows:

[0019] z n+1 =(x n +iy n ) 2 +(c x +ic y )

[0020] Where: x n : Indicates the user's current physical health status;

[0021] y n : Indicates the user's current mental health status;

[0022] c x and c y : Represents personalized health parameters, which are calculated based on input physiological data, psychological data, physical examination report data, personality assessment data, and interest and hobby data;

[0023] z n+1 : Indicates the new health status after this round of iteration, including the comprehensive results of physical health and mental health;

[0024] i: represents the imaginary unit, satisfying i 2 =-1.

[0025] In some possible implementations of the first aspect, the training method of the high-dimensional chaotic iterative function includes the following steps:

[0026] The standardized daily health data package and physical examination report data, personality assessment data and interest and hobby data are used as input data;

[0027] Map these data into the chaotic iteration formula to get the updated health status;

[0028] Calculate the difference between the updated health status data and the actual health status data based on a preset loss function;

[0029] Based on the difference, the model parameter c is adjusted using a preset optimization algorithm. x and c y , to minimize the loss function. In this way, the accuracy of the model's simulation of health status can be continuously improved, making the intervention plan simulated based on the model more reliable, and further improving the quality and effect of health management.

[0030] In some possible implementations of the first aspect, the preset loss function is:

[0031]

[0032] Where: L: is the value of the loss function;

[0033] N: is the sample size;

[0034] is the health status of the i-th sample predicted by the model;

[0035] y i: is the real health data of the i-th sample.

[0036] In some possible implementations of the first aspect, the first interval duration of the psychological data and the physiological data is 24 hours. In this way, the regularity and timeliness of data updates can be ensured, and delayed or inaccurate health management decisions caused by long-term non-update of data can be avoided.

[0037] In some possible implementations of the first aspect, the psychological data is obtained by the following steps:

[0038] Obtain the video data of the first user, which is updated once a day;

[0039] The acquired video data is analyzed using a preset algorithm to obtain the psychological data of the first user. In this way, the actual daily psychological state of the user can be captured more objectively and intuitively, and the accuracy and authenticity of the psychological data can be improved.

[0040] In a second aspect, an embodiment of the present application provides an intelligent health management system, including:

[0041] A first acquisition module is used to acquire physiological data, psychological data, physical examination report data, personality assessment data, and interest and hobby data of the first user; wherein the psychological data and physiological data are updated once according to their corresponding preset first interval durations;

[0042] The first alignment module is used to align the timestamps of physiological data and psychological data to form a daily health data package;

[0043] The first standardization module is used to standardize daily health data packets, physical examination report data, personality assessment data, and interest and hobby data;

[0044] The first updating module is used to input the standardized daily health data package, physical examination report data, personality assessment data and hobby data into the digital human model of the health management system, map them into the preset high-dimensional chaotic iterative function, and obtain the updated data of the health status of the first user;

[0045] A second updating module, configured to update the digital human model based on the updated data of the health status of the first user;

[0046] The first simulation module is used to simulate each predetermined intervention plan based on the updated digital human model, and select the intervention plan with the best simulation effect as the first intervention plan.

[0047] In some possible implementations of the second aspect, the preset high-dimensional chaotic iterative function is as follows:

[0048] z n+1 =(x n +iyn ) 2 +(c x +ic y )

[0049] Where: x n : Indicates the user's current physical health status;

[0050] y n : Indicates the user's current mental health status;

[0051] c x and c y : Represents personalized health parameters, which are calculated based on input physiological data, psychological data, physical examination report data, personality assessment data, and interest and hobby data;

[0052] z n+1 : Indicates the new health status after this round of iteration, including the comprehensive results of physical health and mental health;

[0053] i: represents the imaginary unit, satisfying i 2 =-1.

[0054] In some possible implementations of the second aspect, the training method of the high-dimensional chaotic iterative function includes the following steps:

[0055] The standardized daily health data package and physical examination report data, personality assessment data and interest and hobby data are used as input data;

[0056] Map these data into the chaotic iteration formula to get the updated health status;

[0057] Calculate the difference between the updated health status data and the actual health status data based on a preset loss function;

[0058] Based on the difference, the model parameter c is adjusted using a preset optimization algorithm. x and c y , to minimize the loss function.

[0059] In some possible implementations of the second aspect, the preset loss function is:

[0060]

[0061] Where: L: is the value of the loss function;

[0062] N: is the sample size;

[0063] is the health status of the i-th sample predicted by the model;

[0064] y i: is the real health data of the i-th sample.

[0065] In some possible implementations of the second aspect, the first interval duration of the psychological data and the physiological data is 24 hours.

[0066] In some possible implementations of the second aspect, the first acquisition module is specifically used to: acquire video data of the first user, where the video data is updated once a day; and analyze the acquired video data using a preset algorithm to obtain psychological data of the first user.

[0067] In a third aspect, an embodiment of the present application provides an electronic device, comprising one or more processors; a storage device on which one or more programs are stored; when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any technical solution of the first aspect.

[0068] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method described in any technical solution of the first aspect is implemented.

[0069] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the method described in any technical solution of the first aspect.

[0070] Among them, the technical effects brought about by any design method in the second to fifth aspects can refer to the technical effects brought about by different design methods in the first aspect, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0072] Figure 1 A flowchart of a method for an intelligent healthy digital human model provided in some embodiments of the present application;

[0073] Figure 2 A schematic diagram of the structure of an intelligent health management system provided in some embodiments of the present application;

[0074] Figure 3 It is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present application. DETAILED DESCRIPTION

[0075] Specific embodiments of the present invention will now be mentioned in detail. Although the present invention is described in conjunction with these specific embodiments, it should be appreciated that it is not intended to limit the present invention to these specific embodiments. On the contrary, these embodiments are intended to cover substitutions, changes or equivalent embodiments that may be included in the spirit and scope of the invention defined by the claims. In the following description, a large number of specific details are set forth in order to provide a comprehensive understanding of the present invention. The present invention may be implemented without some or all of these specific details.

[0076] When used in conjunction with "including," "methods comprising," or similar language in this specification and the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0077] The invention patent application numbered 200610136844.4 previously applied by the applicant discloses an intelligent health management method and system. For its specific scheme and implementation principle, please refer to the original application document. The present invention here inherits the invention technical scheme of the prior application "An Intelligent Health Management Method and System", and proposes a method and system for an intelligent health digital human model, so that the digital human model can more accurately simulate the effects of various intervention plans, thereby improving the quality of the selected first intervention plan, and providing users with a health management intervention plan that is more in line with their actual situation and has better effects.

[0078] The overall idea of ​​the technical solution provided by the present application is as follows: a method for an intelligent health digital human model, applied to an intelligent health management system, the method comprising the following steps: obtaining the physiological data, psychological data, physical examination report data, personality assessment data, and interest and hobby data of a first user; wherein the psychological data and the physiological data are updated once according to their corresponding preset first interval durations; aligning the timestamps of the physiological data and the psychological data to form a daily health data packet; standardizing the daily health data packet, physical examination report data, personality assessment data, and interest and hobby data; inputting the standardized daily health data packet, physical examination report data, personality assessment data, and interest and hobby data into the digital human model of the health management system, mapping them to a preset high-dimensional chaotic iterative function, and obtaining updated data on the health status of the first user; updating the digital human model based on the updated data on the health status of the first user; simulating each predetermined intervention plan based on the updated digital human model, and selecting the intervention plan with the best simulation effect as the first intervention plan.

[0079] The method can ensure the timeliness and dynamics of the data by stipulating that the psychological data and physiological data are updated according to the corresponding preset first interval time, and can reflect the latest status of the user in real time. By aligning the timestamps of the physiological data and the psychological data and integrating them into a daily health data package, data from different sources but at the same time node can be integrated in an orderly manner, which is convenient for subsequent unified processing. By standardizing the integrated daily health data package and other types of related data, the data format can meet the requirements of subsequent model operations and avoid deviations caused by inconsistent data scales. By inputting the standardized data into the digital human model of the health management system and mapping it to the preset high-dimensional chaotic iterative function, the high-dimensional chaotic iterative function can simulate and calculate the user's health status based on the complex internal relationship and change law of the input data, thereby obtaining the updated data of the first user's health status, and then simulating each predetermined intervention plan based on the updated digital human model after the updated data, and selecting the optimal intervention plan as the first intervention plan, so that the digital human model can be dynamically adjusted with the latest status of the user. In this way, the digital human model can more accurately simulate the effects of each intervention plan, thereby improving the quality of the selected first intervention plan and providing users with a health management intervention plan that is more in line with their actual situation and has better results.

[0080] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically introduced in conjunction with the accompanying drawings. Figure 1 The embodiment of the present application provides a method for an intelligent health digital human model, which is applied to an intelligent health management system, comprising the following steps:

[0081] S101: Acquire physiological data, psychological data, physical examination report data, personality assessment data, and hobby data of the first user; wherein the psychological data and physiological data are updated once according to their corresponding preset first interval durations;

[0082] Exemplarily, physiological data include but are not limited to heart rate, HRV, body temperature, and body fat. Specifically, the execution subject of the method of the intelligent health digital human model can be connected to the smart bracelet worn by the user and the body fat scale used by the user through a wireless connection to collect physiological data.

[0083] Psychological data includes but is not limited to changes in emotions and moods. Specifically, psychological data can be obtained through the following steps:

[0084] The first step is to obtain the video data of the first user, which is updated once a day;

[0085] Specifically, video data can be obtained by having users record a 1-minute short video through their mobile phones every day;

[0086] The second step is to analyze the acquired video data using a preset algorithm to obtain the psychological data of the first user. Specifically, the psychological data can be obtained by performing emotion recognition, voice feature analysis, and psychological state assessment on the video data.

[0087] Physical examination report data includes but is not limited to blood pressure, blood sugar, and cholesterol.

[0088] Personality assessment data were obtained through standardized psychometric tools (e.g., the Big Five personality assessment).

[0089] Hobby data is obtained through daily behavior patterns and psychological questionnaire data. Among them, physical examination report data, personality assessment data and hobby data can be manually uploaded by the user to the above execution.

[0090] Specifically, in some embodiments, the first interval duration of psychological data and physiological data is 24 hours. In this way, the regularity and timeliness of data updates can be guaranteed, and the delayed or inaccurate health management decisions caused by long-term non-update of data can be avoided. Of course, the present application is not limited to this. In other embodiments, the first interval durations corresponding to psychological data and physiological data can also be different.

[0091] On the basis of the above embodiment, the physical examination report data, the personality assessment data and the interest and hobby data are updated once according to their corresponding preset second interval durations, and each of the second interval durations is greater than the first interval duration. Of course, the present application is not limited to this. In other embodiments, the physical examination report data, the personality assessment data and the interest and hobby data can also be updated according to the first interval duration.

[0092] S102: Aligning the timestamps of physiological data and psychological data to form a daily health data packet;

[0093] S103: Standardizing daily health data packets, physical examination report data, personality assessment data, and hobby data;

[0094] Specifically, physiological data can be mapped to the same range through z-score normalization. The specific formula is as follows:

[0095]

[0096] Among them, μ P and σ P are the mean and standard deviation of the physiological data, respectively.

[0097] It is understandable that psychological data, physical examination report data, and personality assessment data can also be processed according to similar standardized methods to ensure that the dimensions of all data are consistent to facilitate subsequent iterative calculations.

[0098] S104: inputting the standardized daily health data package, physical examination report data, personality assessment data and hobby data into the digital human model of the health management system, mapping them into the preset high-dimensional chaotic iterative function, and obtaining updated data on the health status of the first user;

[0099] In some embodiments, the preset high-dimensional chaotic iterative function is as follows:

[0100] z n+1 =(x n +iy n ) 2 +(c x +ic y )

[0101] Where: x n : Indicates the user's current physical health status;

[0102] y n : Indicates the user's current mental health status;

[0103] c x and c y : Represents personalized health parameters, which are calculated based on input physiological data, psychological data, physical examination report data, personality assessment data, and interest and hobby data;

[0104] z n+1 : Indicates the new health status after this round of iteration, including the comprehensive results of physical health and mental health;

[0105] i: represents the imaginary unit, satisfying i 2 =-1.

[0106] The high-dimensional chaotic iterative function is trained through the following steps:

[0107] In the first step, the standardized daily health data package and physical examination report data, personality assessment data, and interest and hobby data are used as input data;

[0108] The second step is to map these data into the chaotic iteration formula to obtain the updated health status;

[0109] The third step is to calculate the difference between the updated health status data and the actual health data based on the preset loss function;

[0110] Specifically, the preset loss function is:

[0111]

[0112] Where: L: is the value of the loss function;

[0113] N: is the sample size;

[0114] is the health status of the i-th sample predicted by the model;

[0115] y i : is the real health data of the i-th sample.

[0116] The fourth step is to adjust the model parameter c using a preset optimization algorithm based on the difference. x and c y , to minimize the loss function. Exemplarily, the preset optimization algorithm may be a gradient descent algorithm.

[0117] In this way, the accuracy of the model's simulation of health status can be continuously improved, making the intervention plans simulated based on the model more reliable, further improving the quality and effectiveness of health management.

[0118] S105: updating the digital human model based on the updated data of the health status of the first user;

[0119] S106: Simulate each predetermined intervention plan based on the updated digital human model, and select the intervention plan with the best simulation effect as the first intervention plan.

[0120] See also Figure 2 Based on the same inventive concept as the method of a smart health digital human model in the aforementioned embodiment, the embodiment of the present application provides a smart health management system, including:

[0121] The first acquisition module 201 is used to acquire the physiological data, psychological data, physical examination report data, personality assessment data, and interest and hobby data of the first user; wherein the psychological data and the physiological data are updated once according to their corresponding preset first interval durations;

[0122] The first alignment module 202 is used to align the timestamps of physiological data and psychological data to form a daily health data packet;

[0123] The first standardization module 203 is used to standardize the daily health data packets, physical examination report data, personality assessment data and interest and hobby data;

[0124] The first updating module 204 is used to input the standardized daily health data package, physical examination report data, personality assessment data and hobby data into the digital human model of the health management system, and map them into the preset high-dimensional chaotic iterative function to obtain the updated data of the health status of the first user;

[0125] A second updating module 205, configured to update the digital human model based on the updated data of the health status of the first user;

[0126] The first simulation module 206 is used to simulate each predetermined intervention plan based on the updated digital human model, and select the intervention plan with the best simulation effect as the first intervention plan.

[0127] In some embodiments, the preset high-dimensional chaotic iterative function is as follows:

[0128] z n+1 =(x n +iy n ) 2 +(c x +ic y )

[0129] Where: x n : Indicates the user's current physical health status;

[0130] y n : Indicates the user's current mental health status;

[0131] c x and c y : Represents personalized health parameters, which are calculated based on input physiological data, psychological data, physical examination report data, personality assessment data, and interest and hobby data;

[0132] z n+1 : Indicates the new health status after this round of iteration, including the comprehensive results of physical health and mental health;

[0133] i: represents the imaginary unit, satisfying i 2 =-1.

[0134] In some embodiments, the training method of the high-dimensional chaotic iterative function comprises the following steps:

[0135] The standardized daily health data package and physical examination report data, personality assessment data and interest and hobby data are used as input data;

[0136] Map these data into the chaotic iteration formula to get the updated health status;

[0137] Calculate the difference between the updated health status data and the actual health status data based on a preset loss function;

[0138] Based on the difference, the model parameter c is adjusted using a preset optimization algorithm. x and c y , to minimize the loss function.

[0139] In some embodiments, the preset loss function is:

[0140]

[0141] Where: L: is the value of the loss function;

[0142] N: is the sample size;

[0143] is the health status of the i-th sample predicted by the model;

[0144] y i : is the real health data of the i-th sample.

[0145] In some embodiments, the first interval duration of the psychological data and the physiological data is 24 hours.

[0146] In some embodiments, the first acquisition module 201 is specifically used to: acquire video data of the first user, where the video data is updated once a day; and analyze the acquired video data using a preset algorithm to obtain psychological data of the first user.

[0147] It is understandable that the modules recorded in the intelligent health management system are similar to those in the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the intelligent health management system and the modules contained therein, and will not be repeated here.

[0148] See also Figure 3 , based on the inventive concept of a method of a smart health digital human model in the aforementioned embodiment, an embodiment of the present application provides an electronic device. The electronic device may include but is not limited to mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), etc., and fixed terminals such as digital TVs, desktop computers, etc. The electronic device includes a processing device 301 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a ROM 302 (read-only memory) or a program loaded from a storage device 308 to a RAM 303 (random access memory). In RAM 303, various programs and data required for the operation of the electronic device are also stored. The processing device 301, ROM 302, and RAM 303 are connected to each other via a bus 304. The input / output interface (i.e., an I / O interface 305) is also connected to the bus 304.

[0149] Typically, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data.

[0150] In particular, according to some embodiments of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the above-mentioned functions defined in the method of some embodiments of the present application are executed.

[0151] It should be noted that the computer-readable medium recorded in some embodiments of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present application, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In some embodiments of the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0152] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an adhoc peer-to-peer network), as well as any currently known or future developed network.

[0153] The computer-readable medium may be included in the electronic device; or it may exist independently without being assembled into the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains the physiological data, psychological data, physical examination report data, personality assessment data, and interest and hobby data of the first user; wherein the psychological data and the physiological data are updated once according to the corresponding preset first interval duration; aligns the timestamps of the physiological data and the psychological data to form a daily health data packet; standardizes the daily health data packet, the physical examination report data, the personality assessment data, and the interest and hobby data; inputs the standardized daily health data packet, the physical examination report data, the personality assessment data, and the interest and hobby data into the digital human model of the health management system, maps them to the preset high-dimensional chaotic iterative function, and obtains the updated data of the health status of the first user; updates the digital human model based on the updated data of the health status of the first user; simulates each predetermined intervention plan based on the updated digital human model, and selects the intervention plan with the best simulation effect as the first intervention plan.

[0154] The computer program code for performing the operation of some embodiments of the present application can be written in one or more programming languages ​​or a combination thereof, and the above-mentioned programming languages ​​include object-oriented programming languages, such as Java, Smalltalk, C++, and also include conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., using an Internet service provider to connect through the Internet).

[0155] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0156] The modules described in some embodiments of the present application may be implemented by software or by hardware. The modules described may also be set in a processor: for example, they may be described as: a first acquisition module, a first alignment module, a first standardization module, a first update module, a second update module, and a first simulation module. The names of these modules do not, in some cases, constitute limitations on the modules themselves. For example, the first acquisition module may also be described as a "multidimensional data acquisition module".

[0157] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0158] Some embodiments of the present application also provide a computer program product, including a computer program, which implements any of the above-mentioned methods of the intelligent health digital human model when executed by a processor.

[0159] Although the present invention has been described in detail above by general description and specific embodiments, it is obvious to those skilled in the art that some modifications or improvements can be made to the present invention. Therefore, these modifications or improvements made without departing from the spirit of the present invention all belong to the scope of protection claimed by the present invention.

Claims

1. A method for an intelligent healthy digital human model, characterized in that: Applied to intelligent health management system, including the following steps: Acquire the physiological data, psychological data, physical examination report data, personality assessment data, and interest and hobby data of the first user; wherein the psychological data and physiological data are updated once according to their corresponding preset first interval durations; Aligning the timestamps of the physiological data and the psychological data to form a daily health data packet; Standardizing the daily health data package, the physical examination report data, the personality assessment data, and the interest and hobby data; Input the standardized daily health data packet, the physical examination report data, the personality assessment data and the hobby data into the digital human model of the health management system, and map them into the preset high-dimensional chaotic iterative function to obtain the updated data of the health status of the first user; updating the digital human model based on the updated data of the health status of the first user; Each predetermined intervention plan is simulated based on the updated digital human model, and the intervention plan with the best simulation effect is selected as the first intervention plan.

2. The method of intelligent healthy digital human model according to claim 1, characterized in that: The preset high-dimensional chaotic iterative function is as follows: z n+1 =(x n +iy n ) 2 +(c x +ic y ) Where: x n : Indicates the user's current physical health status; y n : Indicates the user's current mental health status; c x and c y : Represents personalized health parameters, which are calculated based on input physiological data, psychological data, physical examination report data, personality assessment data, and interest and hobby data; z n+1 : Indicates the new health status after this round of iteration, including the comprehensive results of physical health and mental health; i: represents the imaginary unit, satisfying i 2 =-1.

3. The method of intelligent healthy digital human model according to claim 2, characterized in that: The training method of the high-dimensional chaotic iterative function comprises the following steps: Using the standardized daily health data package and the physical examination report data, the personality assessment data and the interest and hobby data as input data; Map these data into the chaotic iteration formula to get the updated health status; Calculate the difference between the updated health status data and the actual health status data based on a preset loss function; Based on the difference, the model parameter c is adjusted using a preset optimization algorithm. x and c y , to minimize the loss function.

4. The method of intelligent healthy digital human model according to claim 3, characterized in that: The preset loss function is: Where: L: is the value of the loss function; N: is the sample size; is the health status of the i-th sample predicted by the model; y i : is the real health data of the i-th sample.

5. The method for the intelligent healthy digital human model according to any one of claims 1 to 4, characterized in that: The first interval durations of the psychological data and the physiological data are both 24 hours.

6. The method of intelligent healthy digital human model according to claim 4, characterized in that: The psychological data is obtained through the following steps: Acquire video data of a first user, where the video data is updated once a day; The acquired video data is analyzed using a preset algorithm to obtain the psychological data of the first user.

7. An intelligent health management system, characterized in that: include: A first acquisition module is used to acquire physiological data, psychological data, physical examination report data, personality assessment data, and interest and hobby data of the first user; wherein the psychological data and physiological data are updated once according to their corresponding preset first interval durations; A first alignment module, used for aligning the timestamps of the physiological data and the psychological data to form a daily health data packet; A first standardization module is used to standardize the daily health data packet, the physical examination report data, the personality assessment data and the interest and hobby data; A first updating module is used to input the standardized daily health data packet, the physical examination report data, the personality assessment data and the hobby data into the digital human model of the health management system, and map them into a preset high-dimensional chaotic iterative function to obtain updated data on the health status of the first user; A second updating module, configured to update the digital human model based on the updated data of the health status of the first user; The first simulation module is used to simulate each predetermined intervention plan based on the updated digital human model, and select the intervention plan with the best simulation effect as the first intervention plan.

8. An electronic device, characterized in that: include: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processing device, the method according to any one of claims 1 to 6 is implemented.

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

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