A method, device, terminal equipment and storage medium for generating vehicle material information

Abstract

The present application discloses a vehicle material information generation method, device, terminal equipment and storage medium, and relates to the field of information processing technology. The generation method includes obtaining customer order data inputted in an operation interface; analyzing the customer order data through a pre-built extraction model to determine the target configuration module corresponding to the customer order data, and the pre-built extraction model includes a linear analysis model and a non-linear analysis model; using a vehicle configuration feature library to call an associated configuration module corresponding to the target configuration module from the engineering material table, and generating vehicle material information based on the target configuration module and the associated configuration module, and the vehicle configuration feature library includes the configuration relationship between the configuration modules corresponding to each of the engineering material tables. The method of the present application can be used to assemble the configuration modules, and accurately generate vehicle material information that meets the personalized needs of customers, with strong flexibility.

Description

Technical Field

[0001] The present application relates to the field of information processing technology, and in particular to a method, device, terminal equipment and storage medium for generating vehicle material information. Background Art

[0002] In modern society, cars have become an indispensable means of transportation for people, bringing great convenience to daily travel. With the continuous development of vehicle manufacturing technology, more and more car companies use bill of materials (BOM) to manage different models, that is, design a unique BOM data system for each model. However, this method cannot flexibly adapt to the increasingly diverse car purchase choices of consumers, nor can it match the multi-series and multi-variety production lines used in current vehicle manufacturing, which seriously affects production efficiency. Summary of the invention

[0003] In view of the above-mentioned defects or deficiencies in the related art, it is desired to provide a vehicle material information generation method, apparatus, terminal device and storage medium, which can accurately generate vehicle material information with greater flexibility.

[0004] In a first aspect, the present application provides a method for generating vehicle material information, the generating method comprising:

[0005] Get the customer order data entered in the operation interface;

[0006] Analyzing the customer order data by using a pre-built extraction model to determine a target configuration module corresponding to the customer order data, wherein the pre-built extraction model includes a linear analysis model and a non-linear analysis model;

[0007] The whole vehicle configuration feature library is used to call the associated configuration module corresponding to the target configuration module from the engineering material table, and the vehicle material information is generated based on the target configuration module and the associated configuration module. The whole vehicle configuration feature library includes the configuration relationship between the configuration modules corresponding to the engineering material table.

[0008] Optionally, the linear analysis model in some embodiments of the present application is constructed by the following steps:

[0009] Acquire a plurality of first historical order data and a first historical configuration module corresponding to the first historical order data from a database of a material requirement planning system;

[0010] The first historical order data is converted into standard list data, and the linear analysis model is obtained by fitting the standard list data and the first historical configuration module, wherein the standard list data includes budget items, function items, and module items.

[0011] Optionally, the nonlinear analysis model in some embodiments of the present application is constructed by the following steps:

[0012] Acquire a plurality of second historical order data and a second historical configuration module corresponding to the second historical order data from a database of the material requirement planning system;

[0013] The second historical configuration module is subjected to Bayesian classification in the case of pre-equipment selection of the device, and the nonlinear analysis model is established according to the classification result.

[0014] Optionally, in some embodiments of the present application, the Bayesian classification of the second historical configuration module in the case of pre-equipment selection device includes:

[0015] Counting the first priori probability of each of the second historical configuration modules selecting the candidate device and the second priori probability of not selecting the candidate device;

[0016] Calculating a first a posteriori probability when the device components with different attributes in the second historical configuration module select the candidate device component and a second a posteriori probability when the candidate device component is not selected;

[0017] A first classification probability when the alternative device is installed is calculated based on the first prior probability and the first a posteriori probability, and a second classification probability when the alternative device is not installed is calculated based on the second prior probability and the second a posteriori probability;

[0018] The first classification probability and the second classification probability are compared. If the first classification probability is greater than the second classification probability, the type corresponding to the first classification probability is used as the type corresponding to the second history configuration module. If the first classification probability is less than the second classification probability, the type corresponding to the second classification probability is used as the type corresponding to the second history configuration module.

[0019] Optionally, the alternative device component in some embodiments of the present application is one of multiple device components corresponding to the same function.

[0020] Optionally, in some embodiments of the present application, the generation method further includes, when exporting the vehicle material information, displaying in the property bar of the operation interface a logical mark string of the corresponding configuration relationship between the target configuration module and the associated configuration module.

[0021] In a second aspect, the present application provides a vehicle material information generating device, the generating device comprising:

[0022] The acquisition module is used to obtain the customer order data entered in the operation interface;

[0023] An analysis module, used to analyze the customer order data through a pre-built extraction model to determine a target configuration module corresponding to the customer order data, wherein the pre-built extraction model includes a linear analysis model and a non-linear analysis model;

[0024] A generation module is used to use the whole vehicle configuration feature library to call the associated configuration module corresponding to the target configuration module from the engineering material table, and generate vehicle material information based on the target configuration module and the associated configuration module. The whole vehicle configuration feature library includes the configuration relationship between the configuration modules corresponding to each of the engineering material tables.

[0025] Optionally, in some embodiments of the present application, the generating device further includes a display module, which is used to display a logical mark string of the corresponding configuration relationship between the target configuration module and the associated configuration module in the property bar of the operation interface when exporting the vehicle material information.

[0026] In a third aspect, the present application provides a terminal device, comprising a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the instruction, the program, the code set or the instruction set is loaded and executed by the processor to implement the steps of the generation method described in any one of the first aspects.

[0027] In a fourth aspect, the present application provides a computer-readable storage medium, which stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the generation method described in any one of the first aspects.

[0028] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0029] The embodiments of the present application provide a vehicle material information generation method, apparatus, terminal device and storage medium, which analyze the customer order data input in real time through a pre-built extraction model, and accurately determine the target configuration module corresponding to the customer order data, wherein the extraction model includes a linear analysis model and a nonlinear analysis model, and then the whole vehicle configuration feature library can be used to call the associated configuration module corresponding to the target configuration module from the engineering material table, so as to achieve ready-to-use and efficient and convenient assembly, wherein the whole vehicle configuration feature library includes the configuration relationship between the configuration modules corresponding to each engineering material table, thereby being able to quickly generate vehicle material information based on the target configuration module and the associated configuration module, with strong flexibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments 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 drawings can be obtained based on these drawings without paying creative work.

[0031] Figure 1 A schematic diagram of a process for generating vehicle material information provided in an embodiment of the present application;

[0032] Figure 2 A structural block diagram of a vehicle material information generating device provided in an embodiment of the present application;

[0033] Figure 3 A structural block diagram of another vehicle material information generating device provided in an embodiment of the present application;

[0034] Figure 4 A structural block diagram of a terminal device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0036] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0037] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. Figures 1 to 4 The vehicle material information generation method, device, terminal device and storage medium provided in the embodiments of the present application are described in detail.

[0038] Please refer to Figure 1 , which is a flow chart of a method for generating vehicle material information provided in an embodiment of the present application, the generating method specifically comprises the following steps:

[0039] S101, obtaining customer order data inputted in the operation interface.

[0040] For example, the customer order data may include, but is not limited to, wheel size, cruising range, pure electric or hybrid, presence or absence of a panoramic sunroof or skylight, electric seats or manual seats, and automatic parking function, etc.

[0041] S102, analyzing the customer order data through a pre-built extraction model to determine a target configuration module corresponding to the customer order data, wherein the pre-built extraction model includes a linear analysis model and a non-linear analysis model.

[0042] It should be noted that from the perspective of product structure, the main vehicle-level parameters include power system, chassis system, body system, interior and exterior decoration system, and electronic and electrical system. From the perspective of configuration segmentation, it can be divided into 6 major engineering systems, about 100 component systems and about 3,000 component parts. On this basis, tens of thousands of single-vehicle-level products can be combined. From a regional perspective, it can be divided into the control directions of power domain, braking domain, body domain and cockpit domain, with the electronic control unit (ECU) as the controller sample to achieve software and function integration. The embodiment of the present application decomposes the whole vehicle into a structure with clear hierarchy, independent system and unified standards according to the modularization of the whole vehicle, that is, the enterprise-level whole vehicle product architecture grouping, providing a common data sharing and comparison benchmark. The whole vehicle is divided into multiple engineering systems and component systems, and each component system can be divided into several standardized parts. In actual use, for example, customer order data includes the maximum cooling capacity of the air conditioner, and the corresponding engineering device is the windward area of ​​the air duct of the air conditioner. The required maximum cooling capacity and the windward area are linearly positively correlated, that is, the cooling capacity increases with the increase of the windward area of ​​the air duct. In this case, a linear analysis model is used, and a nonlinear analysis model is used otherwise.

[0043] For example, when constructing a linear analysis model, the embodiment of the present application first obtains a plurality of first historical order data and a first historical configuration module corresponding to the first historical order data from a database of a material requirement planning system (Enterprise Resource Planning, ERP), wherein the first historical order data may include the maximum cooling capacity of an air conditioner, and then converts the first historical order data into standard list data, and obtains a linear analysis model by fitting the standard list data and the first historical configuration module, wherein the standard list data includes budget items, functional items, and module items. For example,

[0044]

[0045] In formula (1), y1, y2, ..., y n Indicates the first historical configuration module, x 1p 、x 2p , …, x np Indicates standard inventory data, e1, e2, ..., e n represents a random variable.

[0046] Assumptions

[0047] Then y = βx + e, solving the least squares estimate β can accurately locate the customer order demand and finally determine the functional relationship of the relevant demand parts in the bill of materials. Using linear regression analysis, we know that there is only one β = (x T x) -1 x T y, so that Q(β)=y T (1-(x T x) -1 x T )y is small enough, where Q represents the sum of squares of the fitting residuals, representing the similarity between the analyzed data and the regression line.

[0048] For example, when constructing a nonlinear analysis model, the embodiment of the present application first obtains a plurality of second historical order data and a second historical configuration module corresponding to the second historical order data from the database of the material requirement planning system, wherein the second historical order data does not include the maximum cooling capacity of the air conditioner, and then performs Bayesian classification on the second historical configuration module under the condition of pre-equipping optional devices, and establishes a nonlinear analysis model based on the classification results. For example, in all the second historical order data, a limiting condition is optional attribute device a, optional attribute device b, optional attribute device c, optional attribute device d, and whether optional device A is selected. The four attribute values ​​abcd are composed into a vector X, and the optional A corresponds to type Y, and the non-optional A corresponds to type N. Then, the first classification probability of the four attribute values ​​X and type Y is P(Y|X), and the second classification probability of the four attribute values ​​X and type N is P(N|X).

[0049] When there is no null value for the attribute value, that is, all the attribute values ​​in X have values, and no attribute device is not selected, since the denominator in P(Y|X)=P(X|Y)P(Y) / P(X) and P(N|X)=P(X|N)P(N) / P(X) is P(X), only the numerator is compared. At this time, ① the first prior probability P(Y) when each second historical configuration module selects the optional device A and the second prior probability P(N) when the optional device A is not selected are counted. Assuming that 9 of the 14 second historical order data select the optional device A, then P(Y)=(|D c |+1) / (|D|+N i )=(9+1) / (14+2)=5 / 8, where N i Indicates the number of classification categories, namely YES / NO, N i=2, the calculation process of P(N) is the same; ② Calculate the first a posteriori probability P(X|Y) when selecting optional device A for different attribute devices in the second historical configuration module and the second a posteriori probability P(X|N) when not selecting optional device A, then P(X|Y)=P(X1|Y)*P(X2|Y)*P(X3|Y)*P(X4|Y), where P(X1|Y) represents the probability of selecting attribute device a while selecting optional device A, P(X1|Y)=(D1+1) / (|D|+N i ), where D1 represents the number of samples of optional attribute device a in the second historical order data. Similarly, P(X2|Y), P(X3|Y) and P(X4|Y) can be obtained, and then multiplied to obtain P(X|Y). The calculation process of P(X|N) is similar; ③ The first classification probability when the optional device A is selected is calculated according to the first prior probability P(Y) and the first posterior probability P(X|Y), and the second classification probability when the optional device A is not selected is calculated according to the second prior probability P(N) and the second posterior probability P(X|N); and then, ④ compare the size of the first classification probability and the second classification probability. If the first classification probability is greater than the second classification probability, the type corresponding to the first classification probability is used as the type corresponding to the second historical configuration module, and if the first classification probability is less than the second classification probability, the type corresponding to the second classification probability is used as the type corresponding to the second historical configuration module, that is, whether the second historical configuration module is selected when the optional device A is selected. Among them, the first classification probability and the second classification probability are equal only when the conditions are exactly the same, but this situation has no practical significance. Furthermore, in the embodiment of the present application, the alternative device component may also be one of multiple device components corresponding to the same function.

[0050] S103, using the vehicle configuration feature library to call the associated configuration module corresponding to the target configuration module from the engineering material table, and generate vehicle material information based on the target configuration module and the associated configuration module, wherein the vehicle configuration feature library includes the configuration relationship between the configuration modules corresponding to each engineering material table.

[0051] For example, the associated configuration modules include but are not limited to wiring harness modules, chassis modules, plugging cover modules and roof sealing modules, etc. For another example, when exporting vehicle material information, the logical mark string of the corresponding configuration relationship between the target configuration module and the associated configuration module is displayed in the property bar of the operation interface, that is, "and", "or", "not", which is convenient for subsequent engineering design.

[0052] The vehicle material information generation method provided in the embodiment of the present application analyzes the customer order data input in real time through a pre-built extraction model, and accurately determines the target configuration module corresponding to the customer order data, wherein the extraction model includes a linear analysis model and a nonlinear analysis model, and then the whole vehicle configuration feature library can be used to call the associated configuration module corresponding to the target configuration module from the engineering material table, so as to achieve ready-to-use and efficient and convenient assembly, wherein the whole vehicle configuration feature library includes the configuration relationship between the configuration modules corresponding to each engineering material table, thereby being able to quickly generate vehicle material information based on the target configuration module and the associated configuration module, with strong flexibility.

[0053] Based on the above embodiments, the present application provides a vehicle material information generation device. The vehicle material information generation device 100 can be applied to Figure 1 In the vehicle material information generation method of the corresponding embodiment. Please refer to Figure 2 , the vehicle material information generating device 100 comprises:

[0054] Acquisition module 101, used to acquire customer order data inputted through the operation interface;

[0055] An analysis module 102 is used to analyze the customer order data through a pre-built extraction model to determine a target configuration module corresponding to the customer order data, wherein the pre-built extraction model includes a linear analysis model and a non-linear analysis model;

[0056] The generation module 103 is used to use the vehicle configuration feature library to call the associated configuration module corresponding to the target configuration module from the engineering material table, and generate vehicle material information based on the target configuration module and the associated configuration module, wherein the vehicle configuration feature library includes the configuration relationship between the configuration modules corresponding to each engineering material table.

[0057] Optionally, in some embodiments of the present application, the analysis module 102 is further specifically configured to obtain a plurality of first historical order data and a first historical configuration module corresponding to the first historical order data from a database of the material requirement planning system;

[0058] The first historical order data is converted into standard list data, and a linear analysis model is obtained by fitting the standard list data and the first historical configuration module, wherein the standard list data includes budget items, function items, and module items.

[0059] Optionally, in some embodiments of the present application, the analysis module 102 is further specifically configured to obtain a plurality of second historical order data and a second historical configuration module corresponding to the second historical order data from a database of the material requirement planning system;

[0060] The second historical configuration module is subjected to Bayesian classification under the condition of pre-equipment selection, and a nonlinear analysis model is established based on the classification results.

[0061] Optionally, in some embodiments of the present application, the analysis module 102 is further specifically used to count the first priori probability when each second historical configuration module selects the equipment option component and the second priori probability when the equipment option component is not selected;

[0062] Calculate the first a posteriori probability when the device with different attributes is selected in the second historical configuration module and the second a posteriori probability when the device is not selected;

[0063] A first classification probability when selecting the optional device is calculated based on the first prior probability and the first a posteriori probability, and a second classification probability when not selecting the optional device is calculated based on the second prior probability and the second a posteriori probability;

[0064] The first classification probability and the second classification probability are compared. If the first classification probability is greater than the second classification probability, the type corresponding to the first classification probability is used as the type corresponding to the second history configuration module. If the first classification probability is less than the second classification probability, the type corresponding to the second classification probability is used as the type corresponding to the second history configuration module.

[0065] Optionally, in some embodiments of the present application, the alternative device component is one of multiple device components corresponding to the same function.

[0066] Alternatively, if Figure 3 As shown, in some embodiments of the present application, the vehicle material information generating device 100 includes a display module 104, which is used to display the logical mark string of the corresponding configuration relationship between the target configuration module and the associated configuration module in the property bar of the operation interface when exporting the vehicle material information.

[0067] It should be noted that, for the description of the same steps and the same contents in this embodiment as those in other embodiments, reference can be made to the description in other embodiments and will not be repeated here.

[0068] The vehicle material information generating device provided in the embodiment of the present application analyzes the customer order data input in real time through a pre-built extraction model, and accurately determines the target configuration module corresponding to the customer order data, wherein the extraction model includes a linear analysis model and a nonlinear analysis model, and then the whole vehicle configuration feature library can be used to call the associated configuration module corresponding to the target configuration module from the engineering material table, so as to achieve ready-to-use and efficient and convenient assembly, wherein the whole vehicle configuration feature library includes the configuration relationship between the configuration modules corresponding to each engineering material table, thereby being able to quickly generate vehicle material information based on the target configuration module and the associated configuration module, with strong flexibility.

[0069] Based on the above embodiments, the present application provides a terminal device. Figure 4 The terminal device 200 may include a processor 201 and a memory 202. The memory 202 stores at least one instruction, at least one program, code set or instruction set, which is loaded and executed by the processor 201 to implement Figure 1 The steps of the generation method in the corresponding embodiment.

[0070] As another aspect, the present invention provides a computer-readable storage medium for storing program code, the program code for executing the aforementioned Figure 1 The steps of the generation method in the corresponding embodiment.

[0071] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0072] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules 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 an indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms. The modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0073] In addition, each functional module in each embodiment of the present application may be integrated into a processing unit, or each module may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of a software functional unit. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium.

[0074] Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the generation method of each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0075] 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 specification.

[0076] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for generating vehicle material information, characterized in that: The generation method comprises: Get the customer order data entered in the operation interface; Analyzing the customer order data by using a pre-built extraction model to determine a target configuration module corresponding to the customer order data, wherein the pre-built extraction model includes a linear analysis model and a non-linear analysis model; The whole vehicle configuration feature library is used to call the associated configuration module corresponding to the target configuration module from the engineering material table, and the vehicle material information is generated based on the target configuration module and the associated configuration module. The whole vehicle configuration feature library includes the configuration relationship between the configuration modules corresponding to the engineering material table.

2. The generation method according to claim 1, characterized in that: The linear analysis model is constructed by the following steps: Acquire a plurality of first historical order data and a first historical configuration module corresponding to the first historical order data from a database of a material requirement planning system; The first historical order data is converted into standard list data, and the linear analysis model is obtained by fitting the standard list data and the first historical configuration module, wherein the standard list data includes budget items, function items, and module items.

3. The generation method according to claim 1, characterized in that: The nonlinear analysis model is constructed by the following steps: Acquire a plurality of second historical order data and a second historical configuration module corresponding to the second historical order data from a database of the material requirement planning system; The second historical configuration module is subjected to Bayesian classification in the case of pre-equipment selection of the device, and the nonlinear analysis model is established according to the classification result.

4. The generation method according to claim 3, characterized in that: The performing Bayesian classification on the second historical configuration module in the case of pre-equipping the selected device comprises: Counting the first priori probability of each of the second historical configuration modules selecting the candidate device and the second priori probability of not selecting the candidate device; Calculating a first a posteriori probability when the device components with different attributes in the second historical configuration module select the candidate device component and a second a posteriori probability when the candidate device component is not selected; A first classification probability when the alternative device is installed is calculated based on the first prior probability and the first a posteriori probability, and a second classification probability when the alternative device is not installed is calculated based on the second prior probability and the second a posteriori probability; The first classification probability and the second classification probability are compared. If the first classification probability is greater than the second classification probability, the type corresponding to the first classification probability is used as the type corresponding to the second history configuration module. If the first classification probability is less than the second classification probability, the type corresponding to the second classification probability is used as the type corresponding to the second history configuration module.

5. The generation method according to claim 3, characterized in that: The alternative device component is one of multiple device components corresponding to the same function.

6. The generation method according to any one of claims 1 to 5, characterized in that: The generation method further includes displaying a logic mark string of a corresponding configuration relationship between the target configuration module and the associated configuration module in a property column of the operation interface when exporting the vehicle material information.

7. A vehicle material information generating device, characterized in that: The generating device comprises: The acquisition module is used to obtain the customer order data entered in the operation interface; An analysis module, used to analyze the customer order data through a pre-built extraction model to determine a target configuration module corresponding to the customer order data, wherein the pre-built extraction model includes a linear analysis model and a non-linear analysis model; A generation module is used to use the whole vehicle configuration feature library to call the associated configuration module corresponding to the target configuration module from the engineering material table, and generate vehicle material information based on the target configuration module and the associated configuration module. The whole vehicle configuration feature library includes the configuration relationship between the configuration modules corresponding to each of the engineering material tables.

8. The generating device according to claim 7, characterized in that The generating device further comprises a display module, which is used to display a logic mark character string of the corresponding configuration relationship between the target configuration module and the associated configuration module in the attribute column of the operation interface when exporting the vehicle material information.

9. A terminal device, characterized in that: The terminal device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, code set or instruction set, and the instruction, the program, the code set or the instruction set is loaded and executed by the processor to implement the steps of the generation method described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the generation method described in any one of claims 1 to 6.

Images