A method for constructing a health prediction model and a terminal device

Abstract

The present invention is applicable to the field of data processing technology, and provides a method and device for constructing a health prediction model, including: obtaining user body sample data, and performing symbolic regression and horizontal federated learning based on the user body sample data to obtain representative feature data corresponding to the user body sample data, and further, constructing a health prediction model based on the representative feature data. In this way, the present invention proposes a feature engineering solution for user body sample data based on symbolic regression in a horizontal federated learning scenario in a scenario based on symbolic regression, constructs different nonlinear body feature data based on the body feature data of different user body sample data, increases the diversity of the final body feature data, and improves the final model effect from the perspective of digging deeper into the body feature data, thereby improving the accuracy of the output of the health prediction model, that is, improving the accuracy of health prediction.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and in particular relates to a method for constructing a health prediction model and a terminal device. Background Art

[0002] With the rapid development of medical technology and the improvement of people's living standards, timely and accurate prediction of personal health can reflect the user's physical health status, which is conducive to prompting users to pay attention to their health and receive timely treatment, improve the success rate of disease treatment, and effectively relieve pain. How to improve the accuracy of health prediction is an urgent problem to be solved by technicians in this field. Summary of the invention

[0003] In view of this, an embodiment of the present invention provides a method for constructing a health prediction model and a terminal device to solve the problem of how to improve the accuracy of health prediction in the prior art.

[0004] A first aspect of an embodiment of the present invention provides a method for constructing a health prediction model, comprising:

[0005] Obtain user body sample data;

[0006] Performing symbolic regression and horizontal federated learning according to the user body sample data to obtain representative feature data corresponding to the user body sample data;

[0007] A user's health prediction model is constructed based on the representative feature data.

[0008] Optionally, the user body sample data includes body feature data and labels corresponding to the body feature data; performing symbolic regression and horizontal federated learning based on the user body sample data to obtain representative feature data corresponding to the user body sample data includes:

[0009] Performing data amplification processing on the body feature data to obtain a number of amplified body feature data corresponding to the body feature data;

[0010] Determine representative feature data according to the plurality of augmented body feature data corresponding to the body feature data; wherein the representative feature data is the augmented body feature data with the highest fitness of the label corresponding to the body feature data among the plurality of augmented body feature data corresponding to the body feature data;

[0011] Determine whether an iteration condition is met; wherein the iteration condition is that the number of iterations of the physical feature data meets a preset threshold;

[0012] If the iteration condition is not met, the representative feature data is used as the body feature data, and the steps of performing data amplification processing on the body feature data to obtain a plurality of amplified body feature data corresponding to the body feature data are re-executed until the iteration condition is met.

[0013] Optionally, the data augmentation processing is a random deformation combination processing.

[0014] Optionally, determining representative characteristic data according to a plurality of augmented physical characteristic data corresponding to the physical characteristic data includes:

[0015] Calculating the fitness of the labels corresponding to the body feature data respectively for the plurality of augmented body feature data;

[0016] Among the fitnesses respectively corresponding to the plurality of augmented body feature data, the augmented body feature data with the highest fitness is determined, and the body feature data with the highest fitness is used as the representative feature data.

[0017] Optionally, the physical characteristic data includes physical indicator data, user identity information, and user location information; and the label corresponding to the physical characteristic data is health.

[0018] A second aspect of an embodiment of the present invention provides a device for constructing a health prediction model, including:

[0019] An acquisition unit, used for acquiring user body sample data;

[0020] A determination unit, configured to perform symbolic regression and horizontal federated learning according to the user body sample data to obtain representative feature data corresponding to the user body sample data;

[0021] A construction unit is used to construct a user's health prediction model based on the representative feature data.

[0022] Optionally, the user body sample data includes body feature data and a label corresponding to the body feature data; the determining unit is specifically used to:

[0023] Performing data amplification processing on the body feature data to obtain a number of amplified body feature data corresponding to the body feature data;

[0024] Determine representative feature data according to the body feature data and a number of augmented body feature data corresponding to the body feature data; wherein the representative feature data is the body feature data with the highest fitness of the label corresponding to the body feature data among the body feature data and a number of augmented body feature data corresponding to the body feature data;

[0025] Determine whether an iteration condition is met; wherein the iteration condition is that the number of iterations of the physical feature data meets a preset threshold;

[0026] If the iteration condition is not met, the representative feature data is used as the body feature data, and the steps of performing data amplification processing on the body feature data to obtain a plurality of amplified body feature data corresponding to the body feature data are re-executed until the iteration condition is met.

[0027] Optionally, the determining unit is further specifically configured to:

[0028] Calculating the fitness of the body feature data and the label corresponding to the body feature data;

[0029] Calculating the fitness of the labels corresponding to the body feature data respectively for the plurality of augmented body feature data;

[0030] Among the fitness corresponding to the physical feature data and the fitness corresponding to each of the several augmented physical feature data, the physical feature data with the highest fitness is determined, and the physical feature data with the highest fitness is used as the representative feature data.

[0031] A third aspect of an embodiment of the present invention provides a terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any one of the methods described in the first aspect when executing the computer program.

[0032] A fourth aspect of an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and is characterized in that when the computer program is executed by a processor, the steps of the method described in any one of the first aspects are implemented.

[0033] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: the present invention provides a method for constructing a health prediction model, which obtains user body sample data, and performs symbolic regression and horizontal federated learning based on the user body sample data to obtain representative feature data corresponding to the user body sample data. Further, a health prediction model can be constructed based on the representative feature data. In this way, the present invention proposes a feature engineering solution for user body sample data based on symbolic regression in a horizontal federated learning scenario in a scenario based on symbolic regression, constructs different nonlinear body feature data based on the body feature data of different user body sample data, increases the diversity of the final body feature data, and improves the final model effect from the perspective of digging deeper into the body feature data, thereby improving the accuracy of the output of the health prediction model, that is, improving the accuracy of health prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0035] Figure 1 It is a schematic diagram of the implementation flow of the method for constructing a health prediction model provided by an embodiment of the present invention;

[0036] Figure 2 is a schematic diagram of a device for constructing a health prediction model provided by an embodiment of the present invention;

[0037] Figure 3 It is a schematic diagram of a terminal device for constructing a health prediction model provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0038] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0039] In order to illustrate the technical solution of the present invention, a specific embodiment is provided below for illustration.

[0040] See also Figure 1 , shows a method for constructing a health prediction model in an embodiment of the present invention, wherein the method can be completely applied to a terminal device (e.g., a mobile device such as a mobile phone, a notebook, an electronic communication watch, etc.), or can be completely applied to a server, or some steps can be applied to a terminal device and some steps can be applied to a server. In this embodiment, the method may, for example, include the following steps:

[0041] S101: Obtain user body sample data.

[0042] In one implementation, the user's body sample data may include body feature data and labels corresponding to the body feature data. It should be noted that the user's body sample data may include multiple sets of body feature data and labels corresponding to each set of body feature data. The body feature data includes body index data (such as blood sugar and blood lipid indicators in the past three months, the average or maximum value of morning blood sugar, etc.), user identity information (such as gender, age), and user location information (such as home address, place of work); the label corresponding to the body feature data is health.

[0043] S102: Perform symbolic regression and horizontal federated learning based on the user body sample data to obtain representative feature data corresponding to the user body sample data.

[0044] Symbolic regression, as a supervised learning method, attempts to discover a hidden mathematical formula to predict the target variable using feature variables. The advantage of symbolic regression is that it can build a symbolic model for a nonlinear system without relying on prior knowledge or models.

[0045] Horizontal Federated Learning: It can also be called Feature-Aligned Federated Learning, that is, the data features of the participants in horizontal federated learning are aligned; this method is suitable for situations where the data features of the participants overlap a lot, but the sample IDs overlap less, for example, customer data of two companies in different regions; the word "horizontal" comes from the "horizontal partitioning (aka sharding)" of data; this method combines multiple rows of samples with the same features from multiple participants for federated learning, that is, the training data of each participant is divided horizontally, which is called horizontal federated learning. Horizontal federated learning increases the total number of training samples.

[0046] In this embodiment, the physical characteristic data may be firstly subjected to data amplification processing to obtain a number of amplified physical characteristic data corresponding to the physical characteristic data (for example, 3,000 amplified physical characteristic data may be amplified). It should be noted that when there are multiple groups of physical characteristic data, one or more groups of physical characteristic data may be extracted for data amplification processing. In one implementation, the data amplification processing is a random deformation and combination processing. For example, the physical characteristic data may be mutated (i.e., randomly deformed and combined). For example, when a group of physical characteristic data is: "age+blood sugar+uric acid" may be combined into / length of service / consumption level, or, (age+sex)*income / city level.

[0047] Then, representative feature data can be determined based on the several augmented body feature data corresponding to the body feature data; wherein the representative feature data is the augmented body feature data with the highest fitness of the label corresponding to the body feature data among the several augmented body feature data corresponding to the body feature data. Specifically, the fitness of each of the several augmented body feature data with the label corresponding to the body feature data is calculated. For example, the correlation coefficient between the augmented body feature data and the label corresponding to the body feature data can be calculated. In the case of binary classification of the label (Y value), the IV value can be calculated, that is, the predictive ability of the variant augmented body feature data for the Y value can be evaluated); then, among the fitness corresponding to each of the several augmented body feature data, the augmented body feature data with the highest fitness is determined, and the augmented body feature data with the highest fitness is used as the representative feature data.

[0048] Determine whether the iteration condition is met; wherein, the iteration condition is that the number of iterative screening times of the physical feature data meets a preset threshold, for example, the preset threshold may be 300. It should be noted that the preset threshold may be set according to actual needs.

[0049] If the iteration condition is not met, the representative feature data is used as the body feature data, and the data amplification processing of the body feature data is re-executed to obtain several steps of amplified body feature data corresponding to the body feature data, until the iteration condition is met. In this way, the winner (that is, the representative feature data with strong prediction ability for the Y value) enters the next round of inheritance; the entrants in the next round (the winners of the previous round: representative feature data) perform crossover mutation or self-mutation (the specific mutation logic can be seen in the mutation logic of symbolic regression), and then perform fitness judgment on the Y value, and then inherit and mutate again. The total number of mutation rounds (that is, the preset threshold) is set in advance, and after the preset threshold number of rounds, the final winner (that is, the amplified body feature data) is output as the output representative feature data.

[0050] S103: Constructing a user's health prediction model based on the representative feature data.

[0051] After obtaining representative feature data, that is, representative feature data with strong prediction ability for the label corresponding to the physical feature data (i.e., health), a health prediction model can be constructed based on the representative feature data. For example, a health prediction model can be constructed based on the representative feature data based on logistic regression xgboost. In other words, a health prediction model can be constructed based on the relationship between the representative feature data and the label corresponding to the representative feature data (i.e., the label of the physical feature data corresponding to the representative feature data). In this way, after receiving a physical feature data to be predicted, the physical feature data to be predicted can be predicted based on the constructed health prediction model to obtain the label corresponding to the physical feature data to be predicted, i.e., health.

[0052] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0053] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: the present invention provides a method for constructing a health prediction model, which obtains user body sample data, and performs symbolic regression and horizontal federated learning based on the user body sample data to obtain representative feature data corresponding to the user body sample data. Further, a health prediction model can be constructed based on the representative feature data. In this way, the present invention proposes a feature engineering solution for user body sample data based on symbolic regression in a horizontal federated learning scenario in a scenario based on symbolic regression, constructs different nonlinear body feature data based on the body feature data of different user body sample data, increases the diversity of the final body feature data, and improves the final model effect from the perspective of digging deeper into the body feature data, thereby improving the accuracy of the output of the health prediction model, that is, improving the accuracy of health prediction.

[0054] Corresponding to the above-mentioned method for constructing a health prediction model, an embodiment of the present invention provides a device for constructing a health prediction model, the structure of which is as follows: Figure 2 As shown, including:

[0055] An acquisition unit 201 is used to acquire user body sample data;

[0056] A determination unit 202, configured to perform symbolic regression and horizontal federated learning according to the user body sample data to obtain representative feature data corresponding to the user body sample data;

[0057] The construction unit 203 is used to construct a health prediction model according to the representative feature data.

[0058] Optionally, the user body sample data includes body feature data and a label corresponding to the body feature data; the determining unit 202 is specifically configured to:

[0059] Performing data amplification processing on the body feature data to obtain a number of amplified body feature data corresponding to the body feature data;

[0060] Determine representative feature data according to the body feature data and a number of augmented body feature data corresponding to the body feature data; wherein the representative feature data is the body feature data with the highest fitness of the label corresponding to the body feature data among the body feature data and a number of augmented body feature data corresponding to the body feature data;

[0061] Determine whether an iteration condition is met; wherein the iteration condition is that the number of iterations of the physical feature data meets a preset threshold;

[0062] If the iteration condition is not met, the representative feature data is used as the body feature data, and the steps of performing data amplification processing on the body feature data to obtain a plurality of amplified body feature data corresponding to the body feature data are re-executed until the iteration condition is met.

[0063] Optionally, the determining unit 202 is further specifically configured to:

[0064] Calculating the fitness of the body feature data and the label corresponding to the body feature data;

[0065] Calculating the fitness of the labels corresponding to the body feature data respectively for the plurality of augmented body feature data;

[0066] Among the fitness corresponding to the physical feature data and the fitness corresponding to each of the several augmented physical feature data, the physical feature data with the highest fitness is determined, and the physical feature data with the highest fitness is used as the representative feature data.

[0067] Optionally, the data augmentation processing is a random deformation combination processing.

[0068] Optionally, the physical characteristic data includes physical indicator data, user identity information, and user location information; and the label corresponding to the physical characteristic data is health.

[0069] Figure 3 FIG. 1 is a schematic diagram of a device / terminal device for constructing a health prediction model provided by an embodiment of the present invention. Figure 3As shown, the health prediction model construction device / terminal device 3 of this embodiment includes: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30, such as a health prediction model construction program. When the processor 30 executes the computer program 32, the steps in the above-mentioned health prediction model construction method embodiments are implemented, such as Figure 1 Alternatively, when the processor 30 executes the computer program 32, the functions of each module / unit in the above-mentioned device embodiments are realized, for example Figure 2 Functions of modules 201 to 203 are shown.

[0070] Exemplarily, the computer program 32 can be divided into one or more modules / units, and the one or more modules / units are stored in the memory 31 and executed by the processor 30 to complete the present invention. The one or more modules / units can be a series of computer program instruction segments that can complete specific functions, and the instruction segments are used to describe the execution process of the computer program 32 in the health prediction model construction device / terminal device 3. For example, the computer program 32 can be divided into a synchronization module, a summary module, an acquisition module, and a return module (module in a virtual device), and the specific functions of each module are as follows:

[0071] The health prediction model construction device / terminal device 3 can be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The health prediction model construction device / terminal device can include, but is not limited to, a processor 30 and a memory 31. Those skilled in the art can understand that Figure 3 It is only an example of the health prediction model construction device / terminal device 3 and does not constitute a limitation of the health prediction model construction device / terminal device 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the health prediction model construction device / terminal device may also include input and output devices, network access devices, buses, etc.

[0072] The processor 30 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0073] The memory 31 may be an internal storage unit of the device / terminal device 3 for constructing the health prediction model, such as a hard disk or memory of the device / terminal device 3 for constructing the health prediction model. The memory 31 may also be an external storage device of the device / terminal device 3 for constructing the health prediction model, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the device / terminal device 3 for constructing the health prediction model. Further, the memory 31 may also include both an internal storage unit and an external storage device of the device / terminal device 3 for constructing the health prediction model. The memory 31 is used to store the computer program and other programs and data required by the device / terminal device for constructing the health prediction model. The memory 31 may also be used to temporarily store data that has been output or is to be output.

[0074] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0075] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0076] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0077] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0078] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0079] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0080] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0081] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for constructing a health prediction model, characterized in that: include: Obtain user body sample data; Performing symbolic regression and horizontal federated learning according to the user body sample data to obtain representative feature data corresponding to the user body sample data; Constructing a health prediction model for the user based on the representative feature data; The user body sample data includes body feature data and labels corresponding to the body feature data; the performing symbolic regression and horizontal federated learning according to the user body sample data to obtain representative feature data corresponding to the user body sample data includes: Performing data amplification processing on the body feature data to obtain a number of amplified body feature data corresponding to the body feature data; Determine representative feature data according to the plurality of augmented body feature data corresponding to the body feature data; wherein the representative feature data is the augmented body feature data with the highest fitness of the label corresponding to the body feature data among the plurality of augmented body feature data corresponding to the body feature data; Determine whether an iteration condition is met; wherein the iteration condition is that the number of iterations of the physical feature data meets a preset threshold; If the iteration condition is not met, the representative feature data is used as the body feature data, and the step of performing data amplification processing on the body feature data to obtain a plurality of amplified body feature data corresponding to the body feature data is re-executed until the iteration condition is met; The data augmentation process is a random deformation combination process; The determining representative characteristic data according to the plurality of augmented physical characteristic data corresponding to the physical characteristic data comprises: Calculating the fitness of the labels corresponding to the body feature data respectively for the plurality of augmented body feature data; Determine the amplified body feature data with the highest fitness among the fitnesses corresponding to the plurality of amplified body feature data, and use the amplified body feature data with the highest fitness as representative feature data; The physical characteristic data includes physical indicator data, user identity information, and user location information; the label corresponding to the physical characteristic data is health.

2. A device for constructing a health prediction model, characterized in that: include: An acquisition unit, used for acquiring user body sample data; A determination unit, configured to perform symbolic regression and horizontal federated learning according to the user body sample data to obtain representative feature data corresponding to the user body sample data; A construction unit, used to construct a health prediction model according to the representative feature data; The user body sample data includes body feature data and labels corresponding to the body feature data; the determination unit is specifically used to: Performing data amplification processing on the body feature data to obtain a number of amplified body feature data corresponding to the body feature data; Determine representative feature data according to the body feature data and a number of augmented body feature data corresponding to the body feature data; wherein the representative feature data is the body feature data with the highest fitness of the label corresponding to the body feature data among the body feature data and a number of augmented body feature data corresponding to the body feature data; Determine whether an iteration condition is met; wherein the iteration condition is that the number of iterations of the physical feature data meets a preset threshold; If the iteration condition is not met, the representative feature data is used as the body feature data, and the step of performing data amplification processing on the body feature data to obtain a plurality of amplified body feature data corresponding to the body feature data is re-executed until the iteration condition is met; The determining unit is further specifically configured to: Calculating the fitness of the body feature data and the label corresponding to the body feature data; Calculating the fitness of the labels corresponding to the body feature data respectively for the plurality of augmented body feature data; Determine the body feature data with the highest fitness among the fitness corresponding to the body feature data and the fitness corresponding to each of the plurality of augmented body feature data, and use the body feature data with the highest fitness as representative feature data; The data augmentation process is a random deformation combination process; The physical characteristic data includes physical indicator data, user identity information, and user location information; the label corresponding to the physical characteristic data is health.

3. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to claim 1 are implemented.

4. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to claim 1 are implemented.

Images