Vehicle body scheme prediction method and device, electronic equipment and storage medium

Abstract

The invention discloses a vehicle body scheme prediction method and device, electronic equipment and a storage medium. The method comprises the steps that vehicle body scheme constraint conditions are acquired; constructing a vehicle body scheme prediction model according to the vehicle body scheme constraint conditions; and inputting the vehicle body scheme constraint condition into the vehicle body scheme prediction model, performing scheme prediction by using the vehicle body scheme prediction model, and outputting a corresponding vehicle body scheme prediction result. The method is simple and efficient, automatic prediction of the vehicle body scheme can be realized in combination with the vehicle body scheme constraint condition and the vehicle body scheme prediction model, the design process of the vehicle body scheme is simplified, the prediction efficiency and accuracy of the vehicle body scheme are improved, and the method can be widely applied to the technical field of vehicle manufacturing.

Description

Technical Field

[0001] The present application relates to the field of vehicle manufacturing technology, and in particular to a vehicle body solution prediction method, device, electronic device and storage medium. Background Art

[0002] In order for the vehicle's body-in-white (BIW) to meet manufacturing requirements, the BIW solution needs to be planned and evaluated. Currently, the main method used to determine the BIW solution is empirical. The proportion of aluminum alloys, the locations where aluminum alloys are used, the connection methods and the approximate amount of aluminum alloys used, as well as the expected cost and weight of the BIW, all need to be determined manually based on empirical experience. This method is time-consuming and labor-intensive. When the data indicators of the vehicle model to be designed differ significantly from the data of existing vehicle models, inaccurate data judgments of the solution may occur. Repeated simulations are required to verify the feasibility of the solution. At the same time, the boundary conditions and load assumptions in the simulation may not match the actual working conditions, which may affect the accuracy of the body solution results. In addition, the design process of the body solution is complex, which will greatly extend the design cycle of the BIW solution. Summary of the Invention

[0003] The main purpose of the embodiments of the present application is to propose a body scheme prediction method, device, electronic device and storage medium, which can realize the automatic prediction of body schemes and improve the prediction efficiency and accuracy of body schemes.

[0004] In one aspect, an embodiment of the present application provides a vehicle body solution prediction method, the method comprising the following steps:

[0005] Obtain vehicle body plan constraints;

[0006] Constructing a vehicle body solution prediction model according to the vehicle body solution constraint conditions;

[0007] The vehicle body solution constraint conditions are input into the vehicle body solution prediction model, the vehicle body solution prediction model is used to perform solution prediction, and the corresponding vehicle body solution prediction result is output.

[0008] In some embodiments, constructing a vehicle body solution prediction model according to the vehicle body solution constraint conditions specifically includes:

[0009] Constructing the vehicle body solution prediction model;

[0010] Determining corresponding model optimization features according to the vehicle body solution constraints;

[0011] The model optimization features are embedded into the vehicle body solution prediction model.

[0012] In some embodiments, the method further comprises:

[0013] Acquire a training data set; the training data set includes a plurality of vehicle body scheme sample data with vehicle body design target labels; the vehicle body design target labels include vehicle body weight and vehicle body cost;

[0014] The vehicle body solution prediction model embedded with the model optimization features is trained using the training data set.

[0015] In some embodiments, obtaining a training data set specifically includes:

[0016] Acquire a plurality of vehicle body solution sample data and a preset training data filling template;

[0017] Filling the training data filling template with data according to each of the vehicle body solution sample data, and determining the training data entry corresponding to each of the vehicle body solution sample data;

[0018] The training data set is determined according to each of the training data entries.

[0019] In some embodiments, inputting the vehicle body solution constraint condition into the vehicle body solution prediction model, performing solution prediction using the vehicle body solution prediction model, and outputting a corresponding vehicle body solution prediction result specifically includes:

[0020] Inputting the vehicle body solution constraint conditions into the vehicle body solution prediction model, and using the vehicle body solution prediction model to output a plurality of vehicle body design prediction solutions that meet the vehicle body solution constraint conditions, wherein the vehicle body solution constraint conditions include a vehicle body weight constraint and a vehicle body cost constraint;

[0021] According to each of the vehicle body design prediction schemes, the vehicle body scheme prediction result is determined.

[0022] In some embodiments, the filling of the training data filling template with data according to each of the vehicle body solution sample data to determine the training data entry corresponding to each of the vehicle body solution sample data specifically includes:

[0023] Filling the template with the training data, determining data categories corresponding to the plurality of pre-filled data items;

[0024] According to the data category corresponding to each of the pre-filled data items, determining target pre-filled vehicle body data that matches the data category of each of the pre-filled data items from the vehicle body solution sample data;

[0025] For each target pre-filled vehicle body data, the target pre-filled vehicle body data is filled into a corresponding target template position, where the target template position is a data writing position of the pre-filled data item in the training data filling template.

[0026] In some embodiments, the method further comprises:

[0027] Template management is performed on the training data filling template, and the template management includes adding, deleting and modifying each of the pre-filled data items.

[0028] On the other hand, an embodiment of the present application provides a vehicle body solution prediction device, the device comprising:

[0029] The first module is used to obtain the constraints of the vehicle body solution;

[0030] The second module is used to build a body scheme prediction model according to the body scheme constraint conditions;

[0031] The third module is used to input the vehicle body solution constraint conditions into the vehicle body solution prediction model, use the vehicle body solution prediction model to perform solution prediction, and output the corresponding vehicle body solution prediction result.

[0032] On the other hand, an embodiment of the present application proposes an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the vehicle body solution prediction method described above when executing the computer program.

[0033] On the other hand, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the vehicle body solution prediction method described above.

[0034] Embodiments of the present application include at least the following beneficial effects: The present application provides a vehicle body solution prediction method, device, electronic device, and storage medium. The method obtains vehicle body solution constraints, constructs a vehicle body solution prediction model based on the constraints, inputs the constraints into the model, performs solution prediction using the model, and outputs the corresponding vehicle body solution prediction results. This application is simple and efficient, combining vehicle body solution constraints with the model to achieve automated prediction of vehicle body solutions, simplifying the vehicle body solution design process and improving the efficiency and accuracy of vehicle body solution prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0037] Figure 1 This is a flow chart of a vehicle body solution prediction method provided by an embodiment of the present application;

[0038] Figure 2 This is a schematic structural diagram of a vehicle body solution prediction device provided in an embodiment of the present application;

[0039] Figure 3 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are merely examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the appended claims.

[0041] It will be understood that the terms "first", "second", etc. used in this application may be used herein to describe various concepts, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0042] The terms "at least one", "plurality", "each", "any", etc. used in this application include "at least one", "two" or more, "plurality" or "each", "any" or "any one", "each" or "any one" in the context of the present invention, and "at least one" or "at least one" includes one, two or more, "plurality" or "any one" includes two or more, "each" or "each one" in the context of the present invention, and "any" or "any one

[0043] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0044] Reference Figure 1 , Figure 1 This is an optional flowchart of a vehicle body solution prediction method provided by an embodiment of the present application. The method may include but is not limited to steps S101 to S103:

[0045] Step S101, obtaining vehicle body solution constraints;

[0046] Step S102, constructing a vehicle body solution prediction model according to vehicle body solution constraints;

[0047] Step S103: input the vehicle body solution constraint conditions into the vehicle body solution prediction model, perform solution prediction using the vehicle body solution prediction model, and output the corresponding vehicle body solution prediction result.

[0048] In some embodiments, step S102 may include but is not limited to steps S201 to S203:

[0049] Step S201, constructing a vehicle body solution prediction model;

[0050] Step S202: determining corresponding model optimization features according to vehicle body solution constraints;

[0051] Step S203: embedding the model optimization features into the vehicle body solution prediction model.

[0052] In some embodiments, the body solution prediction model may be a random forest algorithm model, a logistic regression algorithm model, a linear regression algorithm model, etc., and a corresponding body solution prediction model is constructed according to actual body solution prediction requirements.

[0053] In some embodiments, the corresponding model optimization calculation item, i.e., the aforementioned model optimization feature, is determined based on the vehicle body solution constraint. For example, assuming that the vehicle body solution constraint is based on vehicle body weight as the target constraint, a calculation formula for the volume density of the vehicle body-in-white is constructed as a model optimization calculation item. The calculation formula for the volume density of the vehicle body-in-white is shown as follows:

[0054] ρ=m / V;

[0055] V=[B*H+(LB)*0.33*H+(LB)*0.67*H*0.5]*W;

[0056] Among them, ρ is the volume density of the vehicle body in white, m is the weight of the vehicle body, V is the volume of the vehicle body in white, L is the length of the vehicle body, B is the wheelbase of the vehicle body, H is the height of the vehicle body, and W is the width of the vehicle body. When the calculation formula of the volume density of the vehicle body in white is embedded in the body scheme prediction model as a model optimization calculation item, the prediction accuracy of the body scheme prediction model is improved. When the body cost is used as the target constraint, the total price calculation formula of the connection process, the total price calculation formula of the materials, and the remaining transportation cost calculation formulas are constructed. The total price calculation formula of the connection process, the total price calculation formula of the materials, and the remaining transportation cost calculation formulas are all embedded in the body scheme prediction model as model optimization calculation items. The remaining transportation cost calculation formulas are determined based on cost factors that vary little with changes in vehicle models, such as labor cost, transportation cost, and time cost. The total price calculation formula for the connection process is the number of each connection process * the unit price of each connection process, and the total price calculation formula for the materials is the unit price of each material * the weight of each material.

[0057] After embedding the model optimization calculation items into the body scheme prediction model, the body scheme prediction model's processing capabilities for data in the training dataset, such as computational efficiency and iteration step length, can be improved while the main functions of the body scheme prediction model remain unchanged, thereby improving the prediction accuracy of the body scheme prediction model.

[0058] In some embodiments, the body solution prediction model is model verified according to a plurality of preset model verification indicators to determine whether to continue training the body solution prediction model. Specifically, a plurality of model verification indicators are pre-set, and the model verification indicators may be accuracy (Accuracy), precision (Precision), recall rate (Recall) and F1 score (F1Score), etc. The body solution prediction model is model verified according to each model verification indicator. When the actual model verification value corresponding to each model verification indicator is greater than the preset model verification indicator threshold, the training of the body solution prediction model is stopped.

[0059] In step S103 of some embodiments, specifically, the body scheme constraints are input into the body scheme prediction model, and the body scheme prediction model is used to output several body design prediction schemes that meet the body scheme constraints, wherein the body scheme constraints include body weight constraints and body cost constraints. Then, the body scheme prediction results are determined based on each body design prediction scheme.

[0060] For example, the vehicle body solution constraint condition is used as input data and input into the vehicle body solution prediction model. Assuming that the vehicle body solution constraint condition is that the vehicle body weight is less than m s , the vehicle body scheme prediction model is used to predict the scheme and output several vehicle body weights less than m s The car body design prediction scheme, assuming that the car body scheme constraint condition is that the car body cost is less than ps , the vehicle body solution prediction model is used to predict the solution and output several vehicle bodies with a cost less than p s The car body design prediction scheme, assuming that the car body scheme constraint condition is that the car body cost is less than p s And the vehicle weight is less than m s , the vehicle body solution prediction model is used to predict the solution and output several vehicle body solutions with a cost less than p s And the vehicle weight is less than m s The car body design prediction scheme, and so on.

[0061] The vehicle body design prediction plan may include but is not limited to information such as the proportion of each material, each material model, the location where each material is used, the unit price of each material, the type and quantity of connection processes, the unit price of the connection process, the weight of the white body, landing cost, labor cost, transportation cost, time cost, scrap rate, etc.

[0062] In some embodiments, the above method may further include steps S301 to S302:

[0063] Step S301, obtaining a training data set; the training data set includes a plurality of vehicle body solution sample data with vehicle body design target labels; the vehicle body design target labels include vehicle body weight and vehicle body cost;

[0064] Step S302 : Using the training data set, the vehicle body solution prediction model embedded with the model optimization features is trained.

[0065] In some embodiments, the body plan sample data is the key data required for the body-in-white design plan stage, and may include but is not limited to information such as the proportion of each material, each material model, the use location of each material, the unit price of each material, the type and quantity of connection processes, the unit price of connection processes, labor costs, transportation costs, time costs, body-in-white weight and landing costs.

[0066] In some embodiments, step S301 may include but is not limited to steps S401 to S403:

[0067] Step S401, obtaining a plurality of vehicle body solution sample data and a preset training data filling template;

[0068] Step S402 , filling the training data filling template with data according to each vehicle body solution sample data, and determining the training data entry corresponding to each vehicle body solution sample data;

[0069] Step S403: Determine a training data set based on each training data entry.

[0070] In some embodiments, a template is filled according to training data to establish training data entries corresponding to each body solution sample data, and each training data entry is stored in a database to obtain a training data set, which includes all training data entries corresponding to the body solution sample data.

[0071] In some embodiments, step S402 may include but is not limited to steps S501 to S503:

[0072] Step S501, filling a template according to training data, and determining data categories corresponding to a plurality of pre-filled data items;

[0073] Step S502 , determining target pre-filled vehicle body data that matches the data category of each pre-filled data item from the vehicle body solution sample data according to the data category corresponding to each pre-filled data item;

[0074] Step S503 , for each target pre-filled vehicle body data, fill the target pre-filled vehicle body data into the corresponding target template position, where the target template position is the data writing position of the pre-filled data item in the training data filling template.

[0075] In some embodiments, optionally, data categories may include but are not limited to the proportion of each material, each material model, the use location of each material, the unit price of each material, the type and quantity of connection processes, the unit price of connection processes, the weight of the body in white, the landing cost, labor cost, transportation cost, time cost, scrap rate, etc. Each data category has a corresponding pre-filled data item and a data writing position in the training data filling template. According to the data category corresponding to each pre-filled data item, the body solution sample data is identified and screened, and the target pre-filled body data corresponding to each pre-filled data item is determined from the body solution sample data. The target pre-filled body data is filled into the data writing position corresponding to the pre-filled data item to complete the data filling, thereby achieving effective integration of the body solution sample data and improving the data orderliness and uniformity of the training data set.

[0076] In some embodiments, the above method further includes step S601:

[0077] Step S601 : performing template management on the training data filling template, where the template management includes adding, deleting and modifying each pre-filled data item.

[0078] In some embodiments, users can manage the training data filling templates according to actual body solution prediction needs, implement customized configuration of the training data filling templates, thereby establishing different types of training data sets, and then use different types of training data sets to train the body solution prediction model, thereby improving the prediction diversity of the body solution prediction model.

[0079] Reference Figure 2 , Figure 2 : is an optional structural diagram of a vehicle body solution prediction device provided in an embodiment of the present application. The device is used to implement the above-mentioned vehicle body solution prediction method. The device may include:

[0080] The first module is used to obtain the constraints of the vehicle body solution;

[0081] The second module is used to build a body scheme prediction model based on the body scheme constraints;

[0082] The third module is used to input the body scheme constraint conditions into the body scheme prediction model, use the body scheme prediction model to perform scheme prediction, and output the corresponding body scheme prediction results.

[0083] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0084] The present application also provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the vehicle body solution prediction method. The electronic device can be any smart terminal, such as a tablet computer.

[0085] It can be understood that the contents of the above method embodiments are applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0086] See also Figure 3 , Figure 3 The hardware structure of an electronic device according to another embodiment is shown. The electronic device includes:

[0087] The processor 901 can be implemented as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0088] The memory 902 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and is called by the processor 901 to execute the vehicle body solution prediction method of the embodiments of this application.

[0089] Input / output interface 903, used to implement information input and output;

[0090] Communication interface 904, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.);

[0091] Bus 905 , which transmits information between various components of the device (e.g., processor 901 , memory 902 , input / output interface 903 , and communication interface 904 );

[0092] The processor 901 , the memory 902 , the input / output interface 903 and the communication interface 904 are connected to each other in communication within the device via a bus 905 .

[0093] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned vehicle body solution prediction method is implemented.

[0094] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiment, the functions specifically implemented by the present storage medium embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0095] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0096] The embodiments of the present application provide a body scheme prediction method, device, electronic device and storage medium, which are simple and efficient, and can realize automatic prediction of body schemes by combining body scheme constraints and body scheme prediction models, thereby simplifying the design process of body schemes and improving the prediction efficiency and accuracy of body schemes.

[0097] The embodiments described in the embodiments of this application are intended to more clearly illustrate the technical solutions of the embodiments of this application and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0098] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0099] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0100] Those skilled in the art will appreciate that all or some of the steps in the methods, systems, and functional modules / units in the devices disclosed above may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0101] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0102] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0103] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods, 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.

[0104] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0105] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0106] It should be appreciated that embodiments of the present invention may be implemented or practiced by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer-readable memory. The methods may be implemented in a computer program using standard programming techniques, including a non-transitory computer-readable storage medium configured with a computer program, wherein the storage medium so configured causes the computer to operate in a specific and predefined manner according to the methods and drawings described in the specific embodiments. Each program may be implemented in a high-level procedural or object-oriented programming language to communicate with the computer system. However, if desired, the program may be implemented in assembly or machine language. In any case, the language may be a compiled or interpreted language. In addition, the program may be run on a programmed application-specific integrated circuit for this purpose.

[0107] If the integrated 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 technical solution of the present application is essentially 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. The computer software product is stored in a storage medium and includes multiple instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments 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, and other media that can store programs.

[0108] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

Claims

1. A vehicle body solution prediction method, characterized in that: The method comprises the following steps: Obtain vehicle body plan constraints; Constructing a vehicle body solution prediction model according to the vehicle body solution constraint conditions; The vehicle body solution constraint conditions are input into the vehicle body solution prediction model, the vehicle body solution prediction model is used to perform solution prediction, and the corresponding vehicle body solution prediction result is output.

2. The vehicle body solution prediction method according to claim 1, characterized in that: The step of constructing a vehicle body solution prediction model according to the vehicle body solution constraint conditions specifically includes: Constructing the vehicle body solution prediction model; Determining corresponding model optimization features according to the vehicle body solution constraints; The model optimization features are embedded into the vehicle body solution prediction model.

3. The vehicle body solution prediction method according to claim 2, characterized in that: The method further comprises: Acquire a training data set; the training data set includes a plurality of vehicle body scheme sample data with vehicle body design target labels; the vehicle body design target labels include vehicle body weight and vehicle body cost; The vehicle body solution prediction model embedded with the model optimization features is trained using the training data set.

4. The vehicle body solution prediction method according to claim 3, characterized in that: The obtaining of the training data set specifically includes: Acquire a plurality of vehicle body solution sample data and a preset training data filling template; Filling the training data filling template with data according to each of the vehicle body solution sample data, and determining the training data entry corresponding to each of the vehicle body solution sample data; The training data set is determined according to each of the training data entries.

5. The vehicle body solution prediction method according to claim 1, characterized in that: The step of inputting the vehicle body solution constraint condition into the vehicle body solution prediction model, performing solution prediction using the vehicle body solution prediction model, and outputting a corresponding vehicle body solution prediction result specifically includes: Inputting the vehicle body solution constraint conditions into the vehicle body solution prediction model, and using the vehicle body solution prediction model to output a plurality of vehicle body design prediction solutions that meet the vehicle body solution constraint conditions, wherein the vehicle body solution constraint conditions include a vehicle body weight constraint and a vehicle body cost constraint; According to each of the vehicle body design prediction schemes, the vehicle body scheme prediction result is determined.

6. The vehicle body solution prediction method according to claim 4, characterized in that: The step of filling the training data filling template with data according to each of the vehicle body solution sample data to determine the training data entry corresponding to each of the vehicle body solution sample data specifically includes: Filling the template with the training data, determining data categories corresponding to the plurality of pre-filled data items; According to the data category corresponding to each of the pre-filled data items, determining target pre-filled vehicle body data that matches the data category of each of the pre-filled data items from the vehicle body solution sample data; For each target pre-filled vehicle body data, the target pre-filled vehicle body data is filled into a corresponding target template position, where the target template position is a data writing position of the pre-filled data item in the training data filling template.

7. The vehicle body solution prediction method according to claim 4, characterized in that: The method further comprises: Template management is performed on the training data filling template, and the template management includes adding, deleting and modifying each of the pre-filled data items.

8. A vehicle body scheme prediction device, characterized in that: The device comprises: The first module is used to obtain the constraints of the vehicle body solution; The second module is used to build a body scheme prediction model according to the body scheme constraint conditions; The third module is used to input the vehicle body solution constraint conditions into the vehicle body solution prediction model, use the vehicle body solution prediction model to perform solution prediction, and output the corresponding vehicle body solution prediction result.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the vehicle body solution prediction method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the vehicle body solution prediction method according to any one of claims 1 to 7 is implemented.

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