Intelligent vehicle body strength evaluation method based on big data

Abstract

The invention discloses an intelligent vehicle body strength evaluation method based on big data, and relates to the field of vehicle body strength, and the method comprises the following steps: setting an intelligent vehicle database, obtaining vehicle attribute information, inputting the vehicle attribute information into the intelligent vehicle database in a classified manner, and constructing a geometric attribute model according to the vehicle attribute information; constructing scene simulation setting, placing the geometric attribute model into the scene simulation setting, carrying out simulation collision test, obtaining vehicle body stress data, and analyzing the vehicle body stress data to obtain vehicle body stress state data; according to the vehicle attribute information, carrying out unitary analysis and evaluation on the vehicle body to obtain a vehicle body elastic evaluation value, and according to the vehicle body stress data, carrying out binary analysis and evaluation on the vehicle body to obtain a vehicle body state evaluation value; and according to the vehicle body bomb evaluation value and the vehicle body state evaluation value, the vehicle body strength is comprehensively evaluated, the vehicle body is optimized according to the evaluation result, the vehicle body strength is optimized, and the vehicle driving safety and reliability are improved.

Description

Technical Field

[0001] The present invention relates to the field of vehicle body strength, and in particular to a method for intelligently evaluating vehicle body strength based on big data. Background Art

[0002] In current automobile body development, in the early stages of product development, a multi-body dynamics model is built based on multi-body dynamics software, and then strength conditions are defined based on traditional experience, such as vertical impact conditions, to obtain the loads at the connection points between the body and the chassis shock absorber, and the loads at the connection points between the body and the chassis spring. This method requires the construction of a multi-body dynamics model of the entire vehicle, which takes a long time. However, the development cycle of new energy vehicles is getting shorter and shorter, and the layout optimization and selection of the body in the early development is much earlier than the chassis system design. Therefore, the technology of solving strength loads based on multi-body dynamics modeling is difficult to adapt to the needs of product body development. In addition, the loads extracted based on multi-body dynamics simulation technology cannot consider the dynamic load characteristics of random loads on the body, that is, it cannot consider the influence of the dynamic load coefficient on the body strength. In general, an empirical safety factor is artificially set, resulting in over-design. The car body is subjected to various loads during use, during which time it is required to be able to fulfill its load-bearing function as a structural body. Therefore, the car body should not produce plastic deformation, cracks or damage during use, which requires the car body to have the necessary static strength and fatigue strength. The car body is subjected to various loads during use. During this period, the car body is required to be able to fulfill its load-bearing function as a structural body. Therefore, the car body should neither produce plastic deformation nor cracks and damage during use. This requires the car body to have the necessary static strength and fatigue strength. The purpose of the car body strength and stiffness test is to understand and verify whether the car body has the strength, durability and stiffness to fully exert its required performance under various use conditions and environmental conditions. This is a problem we need to solve. To this end, we now provide a car body strength intelligent assessment method based on big data. Summary of the Invention

[0003] In order to solve the above technical problems, the present invention aims to provide a method for intelligently evaluating vehicle body strength based on big data, comprising the following steps: Step S1: Setting up an intelligent vehicle database, obtaining vehicle attribute information, and classifying and entering the vehicle attribute information into the intelligent vehicle database, and constructing a geometric attribute model based on the vehicle attribute information; Step S2: Constructing a scene simulation setting, placing the geometric attribute model into the scene simulation setting, performing a simulated collision test, obtaining vehicle body impact response data, and analyzing the vehicle body impact response data to obtain vehicle body response state data; Step S3: Based on the vehicle attribute information, a univariate analysis and evaluation is performed on the vehicle body to obtain a vehicle body elastic evaluation value; based on the vehicle body response data, a bivariate analysis and evaluation is performed on the vehicle body to obtain a vehicle body state evaluation value; Step S4: Comprehensively evaluate the vehicle body strength based on the vehicle body elasticity evaluation value and the vehicle body state evaluation value, and optimize the vehicle body based on the evaluation results.

[0004] Furthermore, the process of setting up an intelligent vehicle database, obtaining vehicle attribute information, and classifying and entering the vehicle attribute information into the intelligent vehicle database includes: The intelligent vehicle database includes vehicle registration categories and vehicle body categories; the vehicle body categories include material data, structure data, and weight data; The vehicle attribute information includes vehicle brand, vehicle model, body structure data, body material data and body weight data; The vehicle attribute information is classified and entered according to the vehicle registration category and body category in the intelligent vehicle database.

[0005] Furthermore, the process of constructing a geometric attribute model based on vehicle attribute information includes: According to the body structure data of the vehicle attribute information, the physical entity of the vehicle in the physical space is obtained, and the physical entity is three-dimensionally modeled to obtain a three-dimensional model, and the body structure data, body material data and body weight data of the vehicle attribute information are obtained. According to the vehicle attribute information, attributes are assigned to the three-dimensional model corresponding to the vehicle to obtain a geometric attribute model.

[0006] Furthermore, the process of constructing a scene simulation setting, placing the geometric attribute model into the scene simulation setting, performing a simulated collision test, and obtaining the vehicle body impact response data includes: The scene simulation setting includes a two-dimensional collision setting, a three-dimensional opposite collision setting, and a three-dimensional directional collision setting; according to the scene simulation setting, a collision monitoring module is set, and the collision monitoring module is set in the geometric attribute model; The collision monitoring module collects the body impact data generated after the geometric attribute model collides; The vehicle body impact data includes impact force, force-bearing area, deformation area, impact deformation and elastic deformation.

[0007] Furthermore, the process of analyzing the vehicle body impact data and obtaining the vehicle body response data includes: Obtaining a stress value based on the impact force and the area receiving the force, wherein the stress value is the magnitude of the impact force per unit area; Obtaining a strain value based on the deformation area and the force-bearing area, wherein the strain value represents the degree of deformation of the vehicle body after the impact force; The elastic modulus is obtained based on the elastic deformation and impact deformation. The elastic modulus refers to the ability of the vehicle body material to restore elasticity. The stress value, strain value and elastic modulus are recorded as vehicle body response data.

[0008] Furthermore, based on the vehicle attribute information, a univariate analysis and evaluation of the vehicle body is performed to obtain the vehicle body elastic evaluation value, including the following process: Obtain body structure data, body material data and body weight data, obtain all types of body materials and the corresponding strength and stiffness of all types, and analyze the body structure based on the weight of the body structure to obtain a body elasticity evaluation value.

[0009] Furthermore, based on the vehicle body state data, a binary analysis and evaluation is performed on the vehicle body to obtain a vehicle body state evaluation value. The process includes: According to the simulated collision test, the body stress data generated by each simulated collision test is analyzed, and the sub-state strength value is obtained based on the stress value, strain value and elastic modulus of the body stress data; Set collision constraints and collision loading conditions; The collision constraint conditions include an intensity threshold x value and an intensity threshold y value; The collision loading conditions include an intensity fluctuation x value and an intensity fluctuation y value; According to the collision constraint conditions and collision loading conditions, the sub-state strength values ​​are screened to obtain the positive strength value, negative strength value and abnormal strength value; Obtain the negative distance state intensity value according to the negative state intensity value, the intensity threshold y value, and the intensity threshold x value; Obtain the heterodyne intensity value according to the heterodyne intensity value, the intensity fluctuation y value, and the intensity fluctuation x value; The vehicle body state evaluation value is obtained according to the negative distance state strength value and the abnormal distance state strength value.

[0010] Furthermore, based on the body elasticity evaluation value and the body state evaluation value, the body strength is comprehensively evaluated, and the body is optimized based on the evaluation results. The process includes: Evaluate the vehicle body structure and materials based on the vehicle body elastic evaluation value, and send the vehicle body elastic evaluation value to the vehicle body evaluator. The vehicle body evaluator evaluates the vehicle body structure and materials based on the evaluation value and generates a vehicle body structure material evaluation result; Evaluate the vehicle body collision according to the vehicle body state evaluation value, and send the vehicle body state evaluation value to the vehicle body evaluator, who then evaluates the vehicle body collision according to the evaluation value and generates a vehicle body collision evaluation result; Obtain a comprehensive strength value based on the vehicle body elasticity evaluation value and the vehicle body state evaluation value; The comprehensive strength value is sent to the body evaluator, who evaluates the body strength based on the comprehensive strength value, the body structure material evaluation results and the body collision evaluation results, generates a comprehensive body evaluation result, and optimizes the body strength based on the comprehensive body evaluation result.

[0011] Compared with the prior art, the beneficial effects of the present invention are: setting up an intelligent vehicle database, obtaining vehicle attribute information, and classifying the vehicle attribute information into the intelligent vehicle database, and constructing a geometric attribute model based on the vehicle attribute information, simulating the vehicle entity, and improving the authenticity and accuracy of the vehicle body collision simulation; constructing a scene simulation setting, placing the geometric attribute model into the scene simulation setting, performing a simulated collision test, obtaining vehicle body impact data, and analyzing the vehicle body impact data to obtain vehicle body response data, and conducting a comprehensive test of the vehicle body strength according to a variety of scene settings to improve the comprehensiveness of vehicle body strength detection; conducting a univariate analysis and evaluation of the vehicle body according to the vehicle attribute information to obtain a vehicle body elastic evaluation value, and conducting a binary analysis and evaluation of the vehicle body according to the vehicle body response data to obtain a vehicle body state evaluation value, thereby improving the safety and reliability of the vehicle body strength, optimizing the problems of vehicle body strength one by one, and improving the optimization efficiency of vehicle body evaluators. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 This is a schematic diagram of a method for intelligently evaluating vehicle body strength based on big data according to an embodiment of the present application. DETAILED DESCRIPTION

[0013] like Figure 1 As shown, a vehicle body strength intelligent assessment method based on big data includes the following steps: Step S1: Setting up an intelligent vehicle database, obtaining vehicle attribute information, and classifying and entering the vehicle attribute information into the intelligent vehicle database, and constructing a geometric attribute model based on the vehicle attribute information; Step S2: Constructing a scene simulation setting, placing the geometric attribute model into the scene simulation setting, performing a simulated collision test, obtaining vehicle body impact response data, and analyzing the vehicle body impact response data to obtain vehicle body response state data; Step S3: Based on the vehicle attribute information, a univariate analysis and evaluation is performed on the vehicle body to obtain a vehicle body elastic evaluation value; based on the vehicle body response data, a bivariate analysis and evaluation is performed on the vehicle body to obtain a vehicle body state evaluation value; Step S4: Comprehensively evaluate the vehicle body strength based on the vehicle body elasticity evaluation value and the vehicle body state evaluation value, and optimize the vehicle body based on the evaluation results.

[0014] It should be further explained that, in the specific implementation process, the process of setting up an intelligent vehicle database, obtaining vehicle attribute information, classifying the vehicle attribute information and entering it into the intelligent vehicle database, and constructing a geometric attribute model based on the vehicle attribute information includes: The intelligent vehicle database includes vehicle registration categories and vehicle body categories; the vehicle registration categories include vehicle registration region, vehicle brand, vehicle model, and vehicle identification code; the vehicle body category includes material data, structural data, and weight data; the material data refers to the materials required for vehicle body production, including but not limited to steel, aluminum alloy, and carbon fiber; the structural data refers to the structural framework of the vehicle body, including but not limited to the body frame, roof, and doors; the weight data refers to the weight value of the vehicle body and the weight value of each part of the vehicle body structure; The vehicle attribute information includes vehicle brand, vehicle model, body structure data, body material data and body weight data; Classify and enter vehicle attribute information according to the vehicle registration category and vehicle body category in the intelligent vehicle database; It should be further explained that, in the specific implementation process, several sub-databases are set up in the intelligent vehicle database, and each sub-database is used to enter the same vehicle brand, which facilitates the intelligent management of vehicle data; Obtaining a physical entity of the vehicle in physical space based on the vehicle body structure data of the vehicle attribute information, performing three-dimensional modeling on the physical entity to obtain a three-dimensional model, obtaining the vehicle body structure data, vehicle body material data, and vehicle body weight data of the vehicle attribute information, and assigning attributes to the three-dimensional model corresponding to the vehicle based on the vehicle attribute information to obtain a geometric attribute model; It should be further explained that, in the specific implementation process, the specific process of assigning attributes to the three-dimensional model corresponding to the vehicle includes: extracting key structure data of the vehicle body structure data through semantic analysis, digitizing the key structure data, obtaining key structure values, and inputting the key structure values ​​into the three-dimensional model to perform a primary simulation of the vehicle body structure; extracting characteristic material data of the vehicle body material data through semantic analysis, digitizing the characteristic material data, obtaining characteristic material values, and inputting the characteristic material values ​​into the three-dimensional model to perform a secondary simulation of the vehicle body material; and based on the vehicle body weight data, performing a vehicle body weight simulation on the three-dimensional model, assigning vehicle weight attributes, and obtaining a geometric attribute model.

[0015] It should be further explained that, in the specific implementation process, the process of constructing a scene simulation setting, placing the geometric attribute model into the scene simulation setting, conducting a simulated collision test, obtaining vehicle body impact data, and analyzing the vehicle body impact data to obtain vehicle body response data includes: The scenario simulation settings include two-dimensional collision settings, three-dimensional opposite collision settings, and three-dimensional directional collision settings; the two-dimensional collision settings include impact type, impact weight, and impact hardness; the three-dimensional opposite collision settings include impact type, impact weight, impact structure, and impact speed; the three-dimensional directional collision settings include impact type, impact weight, impact structure, and impact speed; It should be further explained that, in the specific implementation process, the scenario simulation setting refers to various situations existing in the vehicle collision test. The two-dimensional collision setting refers to the collision between the vehicle and the impact object in a stationary state, the three-dimensional opposite collision setting refers to the collision between the vehicle and the impact object in relative motion, and the three-dimensional directional collision setting refers to the collision between the vehicle and the impact object in opposite motion. According to the scenario simulation settings, the geometric attribute model is subjected to collision testing, and a collision monitoring module is set up. The collision monitoring module is set up in the geometric attribute model to collect deformation data generated after the vehicle collision impact; The collision monitoring module collects the body impact data generated after the geometric attribute model collides; The vehicle body impact response data includes impact force, force-bearing area, deformation area, impact deformation and elastic deformation; It should be further explained that, in specific implementations, the impact force refers to the magnitude of the force exerted on the vehicle body when it collides with another object; the force-bearing area refers to the contact area of ​​the vehicle body at the moment of collision with the other object; the deformation area refers to the total area deformed after the collision; the impact deformation refers to the depth of the dent in the vehicle body after the impact force is applied; and the elastic deformation refers to the depth of the recoverable deformation of the vehicle body after the impact force is applied. The ratio of the impact force to the area under force is calculated to obtain a stress value, which is recorded as L. The stress value is the magnitude of the impact force per unit area; The difference between the deformed area and the force-bearing area is calculated by ratio with the force-bearing area to obtain a strain value, which is recorded as B. The strain value is the degree of deformation of the vehicle body after the impact force; The elastic modulus is calculated by calculating the ratio of the elastic deformation to the impact deformation, and is recorded as T. The elastic modulus refers to the ability of the vehicle body material to recover its elasticity. The stress value, strain value and elastic modulus are recorded as the vehicle body stress data.

[0016] It should be further explained that, in the specific implementation process, based on the vehicle attribute information, a univariate analysis and evaluation of the vehicle body is performed to obtain the vehicle body elastic evaluation value, and based on the vehicle body state data, a binary analysis and evaluation of the vehicle body is performed to obtain the vehicle body state evaluation value. The process includes: Obtaining vehicle body structure data, vehicle body material data, and vehicle body weight data, obtaining all types of vehicle body materials and their corresponding strengths and stiffnesses, and the weight of the vehicle body structure, analyzing the vehicle body structure, and obtaining a vehicle body elasticity assessment value, recorded as ST; ; in, is the strength safety factor, which is related to the vehicle body structure data. The strength of the body material, is the stiffness of the body material, is the weight of the vehicle body structure, Refers to the type of car body material; It should be further explained that, in the specific implementation process, the strength and stiffness of the vehicle body material are known data and can be directly obtained; According to the simulated collision test, the body stress data generated by each simulated collision test is analyzed, and the sub-state strength value is obtained based on the stress value L, strain value B and elastic modulus T of the body stress data, which is recorded as CQ; ; in, It is the collision strength coefficient, which is related to the impact type, impact weight, impact hardness, impact structure and impact speed; Set collision constraints and collision loading conditions; The collision constraint conditions include an intensity threshold x value and an intensity threshold y value; The collision loading conditions include an intensity fluctuation x value and an intensity fluctuation y value; It should be further explained that, in the specific implementation process, the strength threshold x value in the collision constraint condition refers to the minimum value limiting the next-state strength value, the strength threshold y value refers to the maximum value limiting the next-state strength value, the strength fluctuation x value in the collision loading condition refers to the minimum value limiting the change of the next-state strength value, and the strength fluctuation y value refers to the maximum value limiting the change of the next-state strength value; According to the collision constraint conditions and the collision loading conditions, the sub-state strength values ​​are screened, and the sub-state strength values ​​that meet the collision constraint conditions and the collision loading conditions are recorded as normal strength values, the sub-state strength values ​​that do not meet the collision constraint conditions are recorded as negative strength values, and the sub-state strength values ​​that do not meet the collision loading conditions are recorded as abnormal strength values; Obtain the maximum and minimum values ​​of the negative intensity value, calculate the difference between the maximum value of the negative intensity value and the intensity threshold y value, and the minimum value of the negative intensity value and the intensity threshold x value, and take the maximum value to obtain the negative distance intensity value, which is recorded as Fj; Obtain the maximum and minimum values ​​of the abnormal intensity value change, calculate the difference between the maximum value of the abnormal intensity value change and the intensity fluctuation y value, and the minimum value of the abnormal intensity value change and the intensity fluctuation x value, and take the maximum value to obtain the abnormal intensity value, which is recorded as Yj; According to the negative distance state strength value Fj and the different distance state strength value Yj, the negative distance state strength value Fj and the different distance state strength value Yj are multiplied and the reciprocal is taken to obtain the vehicle body state evaluation value, which is recorded as CS.

[0017] It should be further explained that, in the specific implementation process, the process of comprehensively evaluating the vehicle body strength based on the vehicle body elasticity evaluation value and the vehicle body state evaluation value, and optimizing the vehicle body based on the evaluation results includes: Evaluate the vehicle body structure and materials based on the vehicle body elastic evaluation value, and send the vehicle body elastic evaluation value to the vehicle body evaluator. The vehicle body evaluator evaluates the vehicle body structure and materials based on the evaluation value and generates a vehicle body structure material evaluation result; Evaluate the vehicle body collision according to the vehicle body state evaluation value, and send the vehicle body state evaluation value to the vehicle body evaluator, who then evaluates the vehicle body collision according to the evaluation value and generates a vehicle body collision evaluation result; According to the body elasticity evaluation value ST and the body state evaluation value CS, the comprehensive strength value is obtained, which is recorded as QZ; ; in, is the comprehensive strength assessment factor; The comprehensive strength value is sent to the body evaluator, who evaluates the body strength based on the comprehensive strength value, the body structure material evaluation results and the body collision evaluation results, generates a comprehensive body evaluation result, and optimizes the body strength based on the comprehensive body evaluation result.

[0018] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A vehicle body strength intelligent assessment method based on big data, characterized in that: The following steps are involved: Step S1: Setting up an intelligent vehicle database, obtaining vehicle attribute information, and classifying and entering the vehicle attribute information into the intelligent vehicle database, and constructing a geometric attribute model based on the vehicle attribute information; Step S2: Constructing a scene simulation setting, placing the geometric attribute model into the scene simulation setting, performing a simulated collision test, obtaining vehicle body impact response data, and analyzing the vehicle body impact response data to obtain vehicle body response state data; Step S3: Based on the vehicle attribute information, a univariate analysis and evaluation is performed on the vehicle body to obtain a vehicle body elastic evaluation value; based on the vehicle body response data, a bivariate analysis and evaluation is performed on the vehicle body to obtain a vehicle body state evaluation value; Step S4: Comprehensively evaluate the vehicle body strength based on the vehicle body elasticity evaluation value and the vehicle body state evaluation value, and optimize the vehicle body based on the evaluation results.

2. The method for intelligent vehicle body strength assessment based on big data according to claim 1, characterized in that: The process of setting up an intelligent vehicle database, obtaining vehicle attribute information, and classifying and entering the vehicle attribute information into the intelligent vehicle database includes: The intelligent vehicle database includes vehicle registration categories and vehicle body categories; the vehicle body categories include material data, structure data, and weight data; The vehicle attribute information includes vehicle brand, vehicle model, body structure data, body material data and body weight data; The vehicle attribute information is classified and entered according to the vehicle registration category and body category in the intelligent vehicle database.

3. The method for intelligent vehicle body strength assessment based on big data according to claim 2, characterized in that: The process of constructing a geometric attribute model based on vehicle attribute information includes: According to the body structure data of the vehicle attribute information, the physical entity of the vehicle in the physical space is obtained, and the physical entity is three-dimensionally modeled to obtain a three-dimensional model, and the body structure data, body material data and body weight data of the vehicle attribute information are obtained. According to the vehicle attribute information, attributes are assigned to the three-dimensional model corresponding to the vehicle to obtain a geometric attribute model.

4. The method for intelligent vehicle body strength assessment based on big data according to claim 3 is characterized in that: The process of building a scene simulation setting, inserting the geometric attribute model into the scene simulation setting, performing a simulated collision test, and obtaining the vehicle body impact response data includes: The scene simulation setting includes a two-dimensional collision setting, a three-dimensional opposite collision setting, and a three-dimensional directional collision setting; according to the scene simulation setting, a collision monitoring module is set, and the collision monitoring module is set in the geometric attribute model; The collision monitoring module collects the body impact data generated after the geometric attribute model collides; The vehicle body impact data includes impact force, force-bearing area, deformation area, impact deformation and elastic deformation.

5. The method for intelligent vehicle body strength assessment based on big data according to claim 4 is characterized in that: The process of analyzing vehicle body impact data and obtaining vehicle body response data includes: Obtaining a stress value based on the impact force and the area receiving the force, wherein the stress value is the magnitude of the impact force per unit area; Obtaining a strain value based on the deformation area and the force-bearing area, wherein the strain value represents the degree of deformation of the vehicle body after the impact force; The elastic modulus is obtained based on the elastic deformation and impact deformation. The elastic modulus refers to the ability of the vehicle body material to restore elasticity. The stress value, strain value and elastic modulus are recorded as vehicle body response data.

6. The method for intelligent vehicle body strength assessment based on big data according to claim 5 is characterized in that: Based on the vehicle attribute information, the process of performing a univariate analysis and evaluation of the vehicle body to obtain the vehicle body elastic evaluation value includes: Obtain body structure data, body material data and body weight data, obtain all types of body materials and the corresponding strength and stiffness of all types, and analyze the body structure based on the weight of the body structure to obtain a body elasticity evaluation value.

7. The method for intelligent vehicle body strength assessment based on big data according to claim 6, characterized in that: Based on the vehicle body response data, a binary analysis and evaluation is performed on the vehicle body to obtain the vehicle body response evaluation value. The process includes: According to the simulated collision test, the body stress data generated by each simulated collision test is analyzed, and the sub-state strength value is obtained based on the stress value, strain value and elastic modulus of the body stress data; Set collision constraints and collision loading conditions; The collision constraint conditions include an intensity threshold x value and an intensity threshold y value; The collision loading conditions include an intensity fluctuation x value and an intensity fluctuation y value; According to the collision constraint conditions and collision loading conditions, the sub-state strength values ​​are screened to obtain the positive strength value, negative strength value and abnormal strength value; Obtain the negative distance state intensity value according to the negative state intensity value, the intensity threshold y value, and the intensity threshold x value; Obtain the heterodyne intensity value according to the heterodyne intensity value, the intensity fluctuation y value, and the intensity fluctuation x value; The vehicle body state evaluation value is obtained according to the negative distance state strength value and the abnormal distance state strength value.

8. The method for intelligent vehicle body strength assessment based on big data according to claim 7 is characterized in that: The process of comprehensively evaluating the vehicle body strength based on the vehicle body elasticity evaluation value and the vehicle body state evaluation value and optimizing the vehicle body based on the evaluation results includes: Evaluate the vehicle body structure and materials based on the vehicle body elastic evaluation value, and send the vehicle body elastic evaluation value to the vehicle body evaluator. The vehicle body evaluator evaluates the vehicle body structure and materials based on the evaluation value and generates a vehicle body structure material evaluation result; Evaluate the vehicle body collision according to the vehicle body state evaluation value, and send the vehicle body state evaluation value to the vehicle body evaluator, who then evaluates the vehicle body collision according to the evaluation value and generates a vehicle body collision evaluation result; Obtain a comprehensive strength value based on the vehicle body elasticity evaluation value and the vehicle body state evaluation value; The comprehensive strength value is sent to the body evaluator, who evaluates the body strength based on the comprehensive strength value, the body structure material evaluation results and the body collision evaluation results, generates a comprehensive body evaluation result, and optimizes the body strength based on the comprehensive body evaluation result.

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