Body-in-white structure design method, computer equipment and storage medium

Abstract

This application provides a body-in-white (BIW) structure design method, computer device, and storage medium. The method includes: determining a target performance indicator for the BIW structure to be designed; based on the target performance indicator, using a preset system-level decision tree model, determining a target component indicator for the BIW structure to be designed; based on the target component indicator, using a preset component-level decision tree model, determining a range of values ​​for design parameters of components corresponding to the BIW structure; the range of values ​​for the component design parameters is used to design the BIW structure so that the BIW structure achieves the target performance indicator. Using this application, the design efficiency of BIW structures can be improved.

Description

Technical Field

[0001] The present application relates to the field of automobile design, and in particular to a body-in-white structure design method, computer equipment, and storage medium. Background Art

[0002] In the field of automotive design, the design of the body-in-white (BIW) structure is an extremely complex system engineering project, and its design results are crucial to the performance of the entire vehicle. In related technologies, BIW design mainly relies on simulation and testing to optimize design parameters.

[0003] However, since complex structures such as the body-in-white have a large number of design parameters and there is interaction between different design parameters, the above method requires a large number of simulations and experiments to obtain better design results, resulting in low design efficiency of the body-in-white structure. Summary of the Invention

[0004] Embodiments of the present application provide a body-in-white (BIW) structure design method, a computer device, and a storage medium to solve the problem of low design efficiency of the BIW structure.

[0005] In a first aspect, an embodiment of the present application provides a body-in-white structure design method, the method comprising: determining a target performance indicator corresponding to the body-in-white structure to be designed; based on the target performance indicator, using a preset system-level decision tree model, determining a target component indicator corresponding to the body-in-white structure to be designed; based on the target component indicator, using a preset component-level decision tree model, determining a value range of design parameters of components corresponding to the body-in-white structure, the value range of the design parameters being used to design the body-in-white structure so that the body-in-white structure achieves the target performance indicator.

[0006] In some embodiments, determining the target performance index corresponding to the body-in-white structure to be designed includes: obtaining the whole vehicle performance index corresponding to the vehicle to be designed; parsing the whole vehicle performance index to obtain the target performance index of the body-in-white structure corresponding to the vehicle.

[0007] In some embodiments, based on the target performance indicator, a preset system-level decision tree model is used to determine the target component indicator corresponding to the body-in-white structure to be designed, including: traversing the system-level decision tree model to obtain a performance indicator that matches the target performance indicator; and determining that the component performance indicator corresponding to the matched performance indicator is the target component indicator corresponding to the body-in-white structure to be designed.

[0008] In some embodiments, based on the target component index, a preset component-level decision tree model is used to determine the value range of the design parameters of the component corresponding to the body-in-white structure, including: traversing the component-level decision tree model to obtain a component performance index that matches the target component index; and determining that the value range of the design parameters of the component corresponding to the matching component performance index is the value range of the design parameters of the component corresponding to the body-in-white structure.

[0009] In some embodiments, the method further includes: constructing an indicator-parameter framework corresponding to the body-in-white structure to be designed based on the design data corresponding to the designed body-in-white structure; determining basic data using the indicator-parameter framework; and constructing the system-level decision tree model and the component-level decision tree model based on the indicator-parameter framework and the basic data.

[0010] In some embodiments, the constructing of an indicator-parameter framework corresponding to the body-in-white structure to be designed based on the design data corresponding to the designed body-in-white structure includes: parsing the design data to obtain performance indicators, component performance indicators and design parameters of the body-in-white structure to be designed; using the performance indicators as the first node of the indicator-parameter framework; using the component performance indicators as the second node of the indicator-parameter framework; using the design parameters as the third node of the indicator-parameter framework; and constructing the indicator-parameter framework based on the first node, the second node and the third node.

[0011] In some embodiments, the use of the indicator-parameter framework to determine basic data includes: determining vehicle type information corresponding to the body-in-white structure to be designed; determining initial finite element models of multiple designed body-in-white structures that match the vehicle type information; obtaining a finite element model of the body-in-white structure to be designed based on multiple initial finite element models; generating simulation data of the body-in-white structure to be designed using the indicator-parameter framework and the finite element model; obtaining test data of the body-in-white structure to be designed, and combining the simulation data with the test data to obtain the basic data.

[0012] In some embodiments, constructing the system-level decision tree model and the component-level decision tree model based on the indicator-parameter framework and the basic data includes: using the indicator-parameter framework to parse the basic data to obtain first data and second data, the first data including performance indicators and component performance indicators of multiple designed body-in-white structures, and the second data including component performance indicators and design parameters of multiple designed body-in-white structures; based on the first data, using a first decision tree algorithm to obtain the system-level decision tree model; based on the second data, using a second decision tree algorithm to obtain the component-level decision tree model.

[0013] In a second aspect, an embodiment of the present application provides a body-in-white structure design device, the device comprising: an indicator determination module for determining a target performance indicator corresponding to the body-in-white structure to be designed; the indicator determination module is further used to determine, based on the target performance indicator, a target component indicator corresponding to the body-in-white structure to be designed using a preset system-level decision tree model; a parameter determination module for determining, based on the target component indicator, a value range of design parameters of components corresponding to the body-in-white structure using a preset component-level decision tree model, the value range of the design parameters being used to design the body-in-white structure so that the body-in-white structure achieves the target performance indicator.

[0014] In a third aspect, an embodiment of the present application provides a computer device, comprising a processor and a memory, wherein the processor is configured to implement any one of the above-described methods for designing a body-in-white structure when executing a computer program stored in the memory.

[0015] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. The computer program is executed by a processor to implement the above-mentioned body-in-white structure design method.

[0016] The above-mentioned body-in-white structure design method provided in an embodiment of the present application determines a target performance indicator corresponding to the body-in-white structure to be designed; based on the target performance indicator, a preset system-level decision tree model is used to determine a target component indicator corresponding to the body-in-white structure to be designed; based on the target component indicator, a preset component-level decision tree model is used to determine a value range of design parameters for the components corresponding to the body-in-white structure; the value range of the design parameters is used to design the body-in-white structure so that the body-in-white structure achieves the target performance indicator. The above-mentioned method describes the relationship between the design parameters of the components corresponding to the body-in-white structure and the performance indicator of the body-in-white structure through a system-level decision tree model and a component-level decision tree model, and uses the system-level decision tree model and the component-level decision tree model to guide the design of the body-in-white structure. This method can reduce the number of simulations and experiments during the optimization design process of the body-in-white structure, reduce development costs, and improve the design efficiency of the body-in-white structure. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 2 is a diagram of a computer device for executing the body-in-white structure design method provided in an embodiment of the present application.

[0018] Figure 2 This is a flow chart of a body-in-white structure design method provided in an embodiment of the present application.

[0019] Figure 3 Schematic diagram of a component-level decision tree model provided in an embodiment of the present application.

[0020] Figure 4 This is a flow chart of a method for constructing a decision tree model provided in an embodiment of the present application.

[0021] Figure 5 It is a structural diagram of an indicator-parameter framework provided in an embodiment of the present application.

[0022] Figure 6 It is a structural schematic diagram of a finite element model provided in an embodiment of the present application.

[0023] Figure 7 This is a flow chart of a simulation data determination and expansion process provided in an embodiment of the present application.

[0024] Figure 8 A schematic diagram illustrating a process of training a decision tree model provided in an embodiment of the present application.

[0025] Figure 9 It is a structural schematic diagram of a body-in-white structure design device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] It should be noted that the terms "first" and "second" in the description, claims and drawings of this application are used to distinguish similar objects, rather than to describe a specific order or sequence.

[0027] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete manner. The following embodiments and features in the embodiments may be combined with each other unless there is a conflict.

[0028] The body-in-white (BIW) structure is a rigid spatial structure typically consisting of primary load-bearing components such as longitudinal beams, crossbeams, and struts, as well as the sheet metal components connecting them. The BIW structure serves as the mounting base for body components (such as wheels, seats, and steering wheels). Its design is crucial to overall vehicle performance, including dynamics, lightweighting, noise, vibration, and harshness (NVH), safety, and durability.

[0029] In the related art, the design of the body-in-white structure mainly relies on simulation and testing to optimize the design parameters. For example, the design parameters are studied one by one, and a single design parameter is changed under the premise of limiting other design parameters to analyze the impact of the design parameter on the performance of the body-in-white structure. However, since there are a large number of design parameters in complex structures such as the body-in-white structure, and there are interactions between different design parameters, the above method requires a large number of simulations and tests to obtain a better design result, and the design efficiency of the body-in-white structure is low. For another example, a large number of simulations and tests can be carried out to determine the better body-in-white structure under the premise of setting optimization goals. However, the above method requires a large number of simulations and tests, and the body-in-white structure obtained by this method is unique. The body-in-white structure cannot be used to guide the secondary improvement of the structure, resulting in a large amount of time and cost to conduct similar body-in-white structure optimization design research again during the development of each vehicle, and the design efficiency of the body-in-white structure is low.

[0030] In view of this, the present application provides a body-in-white structure design method, computer equipment and storage medium, which can reduce the number of simulations and tests during the optimization design process of the body-in-white structure, reduce development costs, and improve the design efficiency of the body-in-white structure.

[0031] The following will describe some embodiments with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features therein may be combined with each other.

[0032] Combine Figure 1 A diagram illustrating a computer device for executing the body-in-white structure design method provided in an embodiment of the present application. The body-in-white structure design method can be applied to a computer device 10, which can include a computer, a mobile phone, a laptop, a tablet, a server, and an in-vehicle device, among which the server can be a cloud server or a server cluster. The present application does not limit the type of computer device 10. Figure 1 As shown, the computer device 10 includes a communication module 101, a memory 102, a processor 103, an input / output (I / O) interface 104, and a bus 105. The processor 103 is coupled to the communication module 101, the memory 102, and the input / output interface 104 via the bus 105.

[0033] In some embodiments, the communication module 101 may include a wired communication module and / or a wireless communication module.

[0034] In some embodiments, the memory 102 is configured to store one or more computer programs. The one or more computer programs are configured to be executed by the processor 103. The one or more computer programs include a plurality of instructions that, when executed by the processor 103, implement the body-in-white structure design method executed on the computer device 10.

[0035] In some embodiments, the processor 103 provides computing and control capabilities. For example, the processor 103 is configured to execute a computer program stored in the memory 102 to implement the above-mentioned body-in-white structure design method.

[0036] In some embodiments, the input / output interface 104 is used to provide a channel for user input or output. For example, the input / output interface 104 can be used to connect various input and output devices, such as a mouse, keyboard, touch device, display screen, etc., so that the user can enter information or visualize information.

[0037] In some embodiments, the bus 105 is at least used to provide a channel for mutual communication among the communication module 101 , the memory 102 , the processor 103 , and the input / output interface 104 in the computer device 10 .

[0038] In some embodiments, in a body-in-white structure design scenario, a user (e.g., a designer) may input vehicle performance indicators corresponding to the vehicle to be designed into the computer device 10 through the input / output interface 104. The processor 103 analyzes the vehicle performance indicators to obtain target performance indicators corresponding to the body-in-white structure to be designed, and determines the value ranges of design parameters of components corresponding to the body-in-white structure to be designed based on the target performance indicators, the system-level decision tree model, and the component-level decision tree model. The value ranges of the design parameters are used to design the body-in-white structure so that the body-in-white structure achieves the target performance indicators.

[0039] In the computer device 10 provided in the embodiment of the present application, the relationship between the design parameters of the corresponding components of the body-in-white structure and the performance indicators of the body-in-white is described by a system-level decision tree model and a component-level decision tree model, and the system-level decision tree model and the component-level decision tree model are used to guide the design of the body-in-white structure, which can reduce the number of simulations and tests of the body-in-white structure during the optimization design process, reduce development costs, and improve the design efficiency of the body-in-white structure.

[0040] It should be understood that the structures illustrated in the embodiments of the present application do not constitute a specific limitation on the computer device 10. In other embodiments of the present application, the computer device 10 may include more or fewer components than shown, or may combine or separate certain components, or arrange the components differently. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0041] Figure 2 is a flow chart of a body-in-white structure design method provided by an embodiment of the present application, the body-in-white structure design method is applied to a computer device (for example, Figure 1 Computer device 10 in FIG. Figure 2 As shown, the body-in-white structure design method includes the following steps. According to different requirements, the order of the steps in the flowchart can be changed, and some steps can be omitted.

[0042] S11, determining target performance indicators corresponding to the body-in-white structure to be designed.

[0043] In at least one embodiment of the present application, the target performance index may represent a preset performance index of the body-in-white structure. The target performance index may be input by a user or obtained by analyzing preset vehicle performance indexes.

[0044] This embodiment of the present application illustrates the process of parsing preset vehicle performance indicators to obtain target performance indicators. In some embodiments, determining the target performance indicators corresponding to the body-in-white structure to be designed may include: obtaining the vehicle performance indicators corresponding to the vehicle to be designed; and parsing the vehicle performance indicators to obtain the target performance indicators for the body-in-white structure corresponding to the vehicle. By parsing the vehicle performance indicators to obtain the target performance indicators for the body-in-white structure, this embodiment of the present application can accurately match vehicle design requirements and improve the accuracy of body-in-white structure design.

[0045] Among them, the performance indicators of the whole vehicle can be entered into the computer device by the user. The performance indicators of the whole vehicle can be set according to actual needs. For example, the performance indicators of the whole vehicle may include dynamic indicators, lightweight indicators, NVH indicators, safety indicators and durability indicators. There is a correlation between the performance indicators of the whole vehicle and the performance indicators of the body-in-white structure. By analyzing the performance indicators of the whole vehicle, the performance indicators of the body-in-white structure related to the performance indicators of the whole vehicle can be obtained. For example, taking the performance indicators of the whole vehicle as NVH indicators as an example, the performance indicators of the body-in-white structure may include first-order bending / torsion mode, static bending / torsion stiffness and dynamic stiffness characteristics of the suspension system. Among them, the first-order bending / torsion can represent the maximum deformation that the body-in-white structure can withstand when subjected to bending and torsional loads. The static bending / torsion stiffness can represent the ability of the body-in-white structure to resist bending and torsional deformation under static loads. The dynamic stiffness characteristics of the suspension system can represent the natural frequency and stiffness characteristics of the front and rear suspension systems during torsional vibration.

[0046] In some embodiments, the user can analyze the performance indicators of the entire vehicle based on relevant experience to obtain the target performance indicators of the vehicle's corresponding body-in-white structure. In other embodiments, the indicator analysis model can also be pre-trained, and the indicator analysis model can be used to analyze the performance indicators of the entire vehicle to obtain the target performance indicators of the vehicle's corresponding body-in-white structure. The input data of the indicator analysis model are the performance indicators of the entire vehicle, and the output data are the performance indicators of the body-in-white structure. The indicator analysis model can be a Convolutional Neural Networks (CNNs) model, a Recurrent Neural Networks (RNNs) model, a Generative Adversarial Networks (GANs) model, etc., and there is no limitation here. The training method of the indicator analysis model can include supervised training and unsupervised training. The training process of the model can refer to relevant technologies and will not be described in detail here.

[0047] S12, based on the target performance indicators, using a preset system-level decision tree model, determining target component indicators corresponding to the body-in-white structure to be designed.

[0048] In at least one embodiment of the present application, a system-level decision tree model is used to predict related component performance indicators based on the performance indicators of the body-in-white structure. The system-level decision tree model uses the performance indicators of the body-in-white structure as leaf node content and the component performance indicators as decision node content. Among them, the component performance indicators can represent the performance indicators of components that have a major impact on the performance indicators of the body-in-white structure. Taking the performance indicator of the body-in-white structure as the first-order bending / torsion mode as an example, the component performance indicators can include the beam natural frequency, beam bending / torsion stiffness, test point stiffness, joint stiffness, joint mass, constraint point stiffness, and the number of joints.

[0049] In some embodiments, determining the target component indicator corresponding to the BIW structure to be designed based on the target performance indicator using a preset system-level decision tree model includes: traversing the system-level decision tree model to obtain a performance indicator that matches the target performance indicator; and determining the component performance indicator corresponding to the matching performance indicator as the target component indicator corresponding to the BIW structure to be designed. This embodiment of the present application describes the relationship between the performance indicator and component performance indicator of the BIW structure using a system-level decision tree model, and uses the system-level decision tree model to determine the target component indicator corresponding to the target performance indicator of the BIW structure. This can reduce the number of simulations and experiments during the optimization design process of the BIW structure, lower development costs, and improve the design efficiency of the BIW structure.

[0050] Among them, the performance indicators of the body-in-white structure can be set to excellent, general and poor according to actual needs, and the performance indicators of components can be set to an indicator range according to actual needs, and the indicator range can include a numerical range. Traverse the system-level decision tree model, select the performance indicators that are the same as the target performance indicators as the matching performance indicators, and use the component performance indicators on the same branch as the matching performance indicators as the target component indicators corresponding to the body-in-white structure to be designed. In some embodiments, the number of matching performance indicators may be 1 or more. When the number of matching performance indicators is multiple, a preset screening rule can be used to select a preset number of performance indicators from multiple matching performance indicators. Among them, the screening rules can be set according to actual needs. For example, screening can be performed based on the discreteness of the branches. The preset number can be 1 or more, and there is no restriction here.

[0051] Continuing with the above embodiment, when the performance indicator of the body-in-white structure is a first-order bending / torsion mode, the component performance indicators may include the beam natural frequency, beam bending / torsion stiffness, test point stiffness, joint stiffness, joint mass, constraint point stiffness, and number of joints. For example, if the target performance indicator is used to indicate excellent performance of the first-order bending / torsion mode, by traversing the system-level decision tree model, the target component indicators obtained are used to indicate that the performance indicator of the beam natural frequency is within a first numerical range, the performance indicator of the beam bending / torsion stiffness is within a second numerical range, and the performance indicator of the test point stiffness is within a third numerical range. The first numerical range, the second numerical range, and the third numerical range can be set according to actual needs and are not limited here.

[0052] S13, based on the target component index, using a preset component-level decision tree model, determining the value range of the design parameters of the components corresponding to the body-in-white structure, the value range of the design parameters of the components is used to design the body-in-white structure so that the body-in-white structure achieves the target performance index.

[0053] In at least one embodiment of the present application, a component-level decision tree model is used to predict the value ranges of the design parameters of related components based on component performance indicators. The component-level decision tree model uses component performance indicators as leaf node content and the value ranges of component design parameters as decision node content. Among them, component performance indicators can represent performance indicators of components that have a major impact on the performance indicators of the body-in-white structure, and the value ranges of component design parameters can represent the value ranges corresponding to adjustable design parameters that have a major impact on the performance indicators of the body-in-white structure. Taking the component performance indicator as the natural frequency of the beam as an example, the value ranges of the component design parameters can include the rocker beam thickness range, the crossbeam thickness range, the longitudinal beam thickness range, the rocker beam material, the crossbeam material, and the longitudinal beam material, etc. By providing the value ranges of the design parameters, the embodiment of the present application diversifies the design results of the body-in-white structure and has a stronger design fault tolerance and inclusiveness.

[0054] In some embodiments, determining the range of design parameters for the component corresponding to the body-in-white structure using a preset component-level decision tree model based on the target component index includes: traversing the component-level decision tree model to obtain a component performance index that matches the target component index; and determining that the range of design parameters for the component corresponding to the matching component performance index is the range of design parameters for the component corresponding to the body-in-white structure. This embodiment of the present application describes the relationship between component performance indexes and the range of design parameters for components using a component-level decision tree model, and uses the component-level decision tree model to determine the range of design parameters for the component corresponding to the target component index. This can reduce the number of simulations and experiments during the optimization design process of the body-in-white structure, reduce development costs, and improve the design efficiency of the body-in-white structure.

[0055] Among them, the performance index of the component can be represented by a numerical range, and the value range of the design parameter can be a numerical range, a category range (for example, material categories such as steel, aluminum alloy, carbon fiber, etc.), and a logical range (for example, "yes" or "no"), which is not limited here. Traverse the component-level decision tree model, select the component performance index that is the same as the target component index as the matching component performance index, and use the value range of the design parameters on the same branch as the matching component performance index as the value range of the design parameters of the corresponding component of the body-in-white structure. In some embodiments, the number of matching component performance indicators may be 1 or more. When the number of matching component performance indicators is multiple, a preset screening rule can be used to select a preset number of component performance indicators from multiple matching component performance indicators. Among them, the screening rules can be set according to actual needs. For example, screening can be performed based on the discreteness of the branch. The preset number can be 1 or more, which is not limited here.

[0056] For example, taking crash performance as the target performance indicator for the body-in-white (BIW) structure, the corresponding component performance indicators for crash performance include the specific energy absorption (SEA) of the beam and the crush force efficiency (CFE) of the side structure. Specific energy absorption indicates the energy that a material or structure can absorb per unit mass during a collision. In BIW structures, the SEA values ​​of key energy-absorbing components, such as transverse and longitudinal beams, are typically evaluated. These components are the first to make contact and absorb significant amounts of energy during a frontal collision, mitigating the impact on the passenger compartment. Crush force efficiency indicates the ratio of the actual average crush force generated by the structure during a collision to the theoretical crush force. In BIW structures, CFE is primarily used to evaluate side structural components, such as the B-pillar and door anti-collision beam. These components play a critical role in supporting and absorbing energy during side collisions, and the stability and consistency of their crush force are crucial for protecting vehicle occupants. Based on this, design parameters for the beam's specific energy absorption can include the radius of curvature at the transition between the roof rear crossbeam and the upper side rail, the width of the roof rear crossbeam in the vehicle frame width direction, and the Y-axis width of the rocker beam (where the Y-axis refers to the direction perpendicular to the vehicle's length). Design parameters for the side structure's crushing force efficiency can include the thickness of the B-pillar and the rocker beam.

[0057] Taking the collision performance as an example, the target performance index of the body-in-white structure is combined with Figure 3 A schematic diagram illustrating a component-level decision tree model provided in an embodiment of the present application. Figure 3 As shown in Figure 2, the component performance indicators of the component-level decision tree model include the specific energy absorption of the beam and the crushing force efficiency of the side structure. Figure 3 The above two component performance indicators are integrated, and the working condition where the specific energy absorption of the beam is greater than or equal to 16KJ / kg, and the crushing force efficiency of the side structure is greater than or equal to 45% is marked as excellent collision performance of the body-in-white structure, marked with e. The working condition where the specific energy absorption of the beam is greater than or equal to 13.64KJ / kg and less than 16KJ / kg, and the crushing force efficiency of the side structure is greater than or equal to 35% and less than 45% is marked as average collision performance of the body-in-white structure, marked with g. The working condition where the specific energy absorption of the beam is less than 13.64KJ / kg, and the crushing force efficiency of the side structure is less than 35% is marked as poor collision performance of the body-in-white structure, marked with b. The design parameters of the components include the thickness of the B-pillar, the thickness of the rocker beam, the transition curvature radius of the joint between the rear crossbeam of the roof and the upper side beam, the size of the rear crossbeam of the roof in the width direction of the frame, and the Y-width of the rocker beam, which are marked with d, n, m, h, and t respectively. Figure 3As shown in the figure, there are 5 branches with excellent collision performance of the body-in-white structure, 7 branches with average collision performance of the body-in-white structure, and 5 branches with poor collision performance of the body-in-white structure. For example, when the target performance indicator of the body-in-white structure is excellent collision performance, by traversing the system-level decision tree model (not shown in the figure), it can be known that the target component indicators corresponding to the collision performance include the specific energy absorption of the beam and the crushing force efficiency of the side structure, and the specific energy absorption of the beam is greater than or equal to 16KJ / kg, and the crushing force efficiency of the side structure is greater than or equal to 45%. Afterwards, traverse as shown in the figure. Figure 3 The component-level decision tree model shown selects five branches with beam specific energy absorption greater than or equal to 16 kJ / kg and side structure crushing force efficiency greater than or equal to 45%. By screening the five branches, the value range of the design parameters of the component corresponding to one branch is selected.

[0058] In some embodiments, after the component-level decision tree model training is completed, the value ranges of multiple design parameters in the component-level decision tree model can be simulated and verified to obtain simulation verification results. The simulation verification results are used to indicate whether the performance indicators of the body-in-white structure corresponding to the value ranges of the design parameters meet the target performance indicators. The simulation verification results include the number of performance indicators of the body-in-white structure corresponding to the value ranges of the design parameters that meet the target performance indicators and the number of performance indicators of the body-in-white structure corresponding to the value ranges of the design parameters that do not meet the target performance indicators. Please continue to refer to Figure 3 , Figure 3 Here, b(30.0) is used to indicate that the number of performance indicators of the body-in-white structure corresponding to the value range of the design parameter that meet the target performance indicators (for example, poor collision performance) is 30, and g(11.0 / 33.0) is used to indicate that the number of performance indicators of the body-in-white structure corresponding to the value range of the design parameter that meet the target performance indicators (for example, average collision performance) is 11, and the number of performance indicators of the body-in-white structure corresponding to the value range of the design parameter that do not meet the target performance indicators is 3.

[0059] In some embodiments, after determining the value ranges of the design parameters of the components corresponding to the BIW structure, the method further includes: verifying the value ranges of the design parameters to obtain verification results. Verifying the value ranges of the design parameters may include performing simulation verification based on the value ranges of the design parameters to obtain simulation verification results; and performing tests based on the value ranges of the design parameters to obtain test results. When testing based on the value ranges of the design parameters, corresponding components may be produced based on the value ranges of the design parameters, the BIW structure may be assembled using the produced components, and the performance indicators of the BIW structure may be confirmed. If the performance indicators of the BIW structure meet preset requirements, the BIW structure may be produced using the value ranges of the design parameters. If the performance indicators of the BIW structure do not meet the preset requirements, the value ranges of the design parameters may be readjusted. The preset requirements may be determined based on the vehicle performance indicators input by the user.

[0060] The above-mentioned body-in-white structure design method provided in the embodiment of the present application describes the relationship between the design parameters of the corresponding components of the body-in-white structure and the performance indicators of the body-in-white through a system-level decision tree model and a component-level decision tree model, and uses the system-level decision tree model and the component-level decision tree model to guide the design of the body-in-white structure, which can reduce the number of simulations and experiments of the body-in-white structure in the optimization design process, reduce development costs, and improve the design efficiency of the body-in-white structure; in addition, the system-level decision tree model and the component-level decision tree model can be quickly rebuilt according to the different design requirements of the user without the need for huge computational complexity, and obtain decision tree models for different design requirements, further reducing development costs and improving the design efficiency of the body-in-white structure.

[0061] In at least one embodiment of the present application, the system-level decision tree model and the component-level decision tree model can predict the value range of the design parameters of the components corresponding to the body-in-white structure based on the known value range of the design parameters, and derive the vehicle performance indicators corresponding to the value range of the design parameters based on the known value range of the design parameters, thereby realizing the bidirectional functions of prediction and decision-making. Figure 4 This is a flow chart of a method for constructing a decision tree model provided in an embodiment of the present application. The method for constructing a decision tree model is applied to a computer device. Figure 4 As shown, the following steps are included:

[0062] S21, constructing an index-parameter framework corresponding to the body-in-white structure to be designed based on the design data corresponding to the designed body-in-white structure.

[0063] In at least one embodiment of the present application, design data can be obtained based on design examples of existing BIW structures. The design data may include overall vehicle performance indicators, BIW structure performance indicators, component performance indicators that primarily influence the performance indicators of each BIW structure, and adjustable design parameters and their corresponding value ranges. A metric-parameter framework is used to guide the generation of basic data for training decision tree models (e.g., system-level decision tree models and component-level decision tree models). The metric-parameter framework includes BIW structure performance indicators, component performance indicators, and design parameters. Based on the metric-parameter framework, the corresponding relationships between BIW structure performance indicators and component performance indicators, as well as the corresponding relationships between component performance indicators and design parameters, can be determined.

[0064] In some embodiments, the construction of an indicator-parameter framework corresponding to the body-in-white structure to be designed based on the design data corresponding to the designed body-in-white structure includes: parsing the design data to obtain the performance indicators, component performance indicators and design parameters of the body-in-white structure to be designed; using the performance indicators as the first node of the indicator-parameter framework; using the component performance indicators as the second node of the indicator-parameter framework; using the design parameters as the third node of the indicator-parameter framework; and constructing the indicator-parameter framework based on the first node, the second node and the third node. The second node is used to indicate the next-level node of the first node, and the third node is used to indicate the next-level node of the second node. The embodiment of the present application uses the performance indicators, component performance indicators and design parameters of the body-in-white structure to construct an indicator-parameter framework, and uses the indicator-parameter framework to guide the generation of basic data for training a decision tree model, thereby improving the accuracy of determining the basic data, and then improving the accuracy of training the decision tree model, thereby improving the design effect of the body-in-white structure.

[0065] Combine Figure 5 The following is a schematic diagram illustrating the structure of the indicator-parameter framework provided in the embodiment of the present application. Figure 5As shown, taking the NVH performance indicators as an example, the performance indicators of the body-in-white structure can include the first-order bending / torsional mode, static bending / torsional stiffness, and suspension system dynamic stiffness characteristics. The first-order bending / torsional mode, static bending / torsional stiffness, and suspension system dynamic stiffness characteristics are each considered as the first node. The above three body-in-white performance indicators are all related to the beam natural frequency, beam bending / torsional stiffness, test point stiffness, joint stiffness, joint mass, constraint point stiffness, and number of joints. The beam natural frequency, beam bending / torsional stiffness, test point stiffness, joint stiffness, joint mass, constraint point stiffness, and number of joints are considered as the second node. The beam natural frequency, beam bending / torsional stiffness, test point stiffness, joint stiffness, and joint mass are all related to the beam design parameters and joint design parameters. The beam design parameters and joint design parameters are considered as the third node of the beam natural frequency, beam bending / torsional stiffness, test point stiffness, joint stiffness, and joint mass. The constraint point design parameters are related to the constraint point stiffness. The constraint point design parameters are considered as the third node of the constraint point stiffness. Judgment parameters are related to the number of joints and serve as a third factor in determining the number of joints. Beam design parameters can include beam material parameters (e.g., aluminum alloy, high-strength steel, composite materials), and beam geometry parameters (e.g., cross-sectional shape, dimensions, length, and radius of curvature). Joint design parameters can include joint material parameters and joint geometry. Constraint point design parameters can include the coordinate locations and number of constraint points. Judgment parameters can include joint spacing, joint strength distribution uniformity, and joint formation efficiency.

[0066] S22, using the indicator-parameter framework to determine basic data.

[0067] In at least one embodiment of the present application, basic data is used to train a system-level decision tree model and a component-level decision tree model. The basic data may include experimental data and simulation data. The experimental data is obtained by collecting data on the body-in-white structure development of multiple models of relevant vehicles. The experimental data includes performance indicators of the body-in-white structure, component performance indicators, and value ranges of design parameters. The simulation data is obtained by performing simulation processing using the system-level simulation model and the component-level simulation model. The simulation data includes performance indicators of the body-in-white structure, component performance indicators, and value ranges of design parameters.

[0068] In some embodiments, the use of the indicator-parameter framework to determine basic data includes: determining vehicle type information corresponding to the body-in-white structure to be designed; determining initial finite element models of multiple designed body-in-white structures that match the vehicle type information; obtaining a finite element model of the body-in-white structure to be designed based on multiple initial finite element models; generating simulation data of the body-in-white structure to be designed using the indicator-parameter framework and the finite element model; obtaining test data of the body-in-white structure to be designed, and combining the simulation data with the test data to obtain the basic data.

[0069] Among them, the same vehicle model information may include multiple car styles. For example, the same vehicle model information may include ordinary styles, sports styles, luxury styles, etc. The body-in-white structures of different car styles may be different, which is not limited here. In some embodiments, a first correspondence between vehicle model information and car styles and a second correspondence between car styles and finite element models are pre-set. By querying the first correspondence, other car styles with the same vehicle model information as the body-in-white structure to be designed can be obtained. By querying the second correspondence, the finite element model corresponding to the car style can be obtained (for ease of description, this application refers to it as the "initial finite element model"). Among them, the finite element model is used to indicate that a continuous physical structure (for example, a designed body-in-white structure) is discretized into a finite number of units, and these units are given specific physical and mechanical properties, so that the behavior of complex structures can be simulated and analyzed on a computer device. The method for establishing the finite element model can refer to the relevant technology and will not be repeated here. Each car style has a corresponding initial finite element model.

[0070] In some embodiments, the finite element model of the body-in-white structure to be designed includes initial finite element models of multiple vehicle models with the same vehicle model information. These initial finite element models correspond to the same design parameters that affect the performance of the body-in-white structure. Thus, when determining the parameter ranges of the same design parameters, design results for the multiple initial finite element models can be obtained, thereby enabling simultaneous mining of multiple design results.

[0071] Combine Figure 6 The following is a schematic diagram illustrating the structure of the finite element model provided in the embodiment of the present application. Figure 6 As shown, there are four car models with the same vehicle model information as the BIW structure to be designed, denoted as the first, second, third, and fourth car models, respectively. The BIW structures of each car model are different. This application utilizes initial finite element models of multiple designed car models with the same vehicle model information to construct a finite element model of the BIW structure to be designed. This allows for the simultaneous mining of a large number of design results for multiple car models, thereby improving the design of the BIW structure.

[0072] In some embodiments, using the indicator-parameter framework and the finite element model to generate simulation data of the body-in-white structure to be designed may include: determining the simulation conditions of the finite element model based on the indicator-parameter framework; building a simulation platform based on the simulation conditions and the finite element model, and generating simulation data using the simulation platform.

[0073] Among them, the simulation conditions are used to represent the decomposition strategy of decomposing the performance indicators of the body-in-white structure into component performance indicators, and decomposing the component performance indicators into the value range of the design parameters. Using the indicator-parameter framework, the relationship between the performance indicators of the body-in-white structure and the performance indicators of the components, as well as the relationship between the performance indicators of the components and the design parameters can be determined. Based on the above relationship, the simulation conditions can be determined. For example, the simulation conditions may include decomposing the first-order bending / torsion mode into the natural frequency of the beam, the bending / torsion stiffness of the beam, the test point stiffness, the joint stiffness, the joint mass, the constraint point stiffness and the number of joints. The embodiment of the present application determines the simulation conditions of the finite element model by using the indicator-parameter framework, so that the simulation at the component level can truly reflect the performance of the component.

[0074] In some embodiments, a simulation platform can be built based on simulation conditions and finite element models. The simulation platform can include a system-level simulation platform and a component-level simulation platform. Figure 7 , Figure 7The figure below is a flow chart illustrating the simulation data determination and expansion process provided by an embodiment of the present application. During the simulation data determination phase, the simulation platform sequentially performs pre-processing, simulation calculation, post-processing, and output steps. Pre-processing involves generating multiple design solutions using the Design of Experiment (DOE) method based on preset design parameter value ranges. For example, design parameter value ranges for multiple components in the body-in-white structure are pre-set, such as the rocker beam thickness range, crossbeam thickness range, longitudinal beam thickness range, rocker beam material, crossbeam material, and longitudinal beam material. Using the DOE method, the value ranges of these multiple component design parameters are combined to generate multiple design solutions. DOE methods can include full-factorial design of experiments, fractional-factorial design of experiments, orthogonal design of experiments, and other methods. This embodiment of the present application uses the full-factorial design of experiments as an example to illustrate the DOE method. By utilizing the full-factorial design of experiments method, a comprehensive understanding of the value ranges of each design parameter and the impact of their interactions on the design results can be achieved. Simulation calculations involve determining the performance indicators and component indicators of the body-in-white structure using a finite element model based on the multiple design solutions. Post-processing can include selecting relevant data whose performance indicators of the BIW structure meet specified requirements. This data includes data such as the value ranges of design parameters, component performance indicators, and performance indicators of the BIW structure. These data are then output as simulation data. For example, a component-level simulation platform processes the preset value ranges of design parameters to obtain component performance indicators. Subsequently, a system-level simulation platform processes the component performance indicators to obtain performance indicators of the BIW structure. In this way, the simulation platform can obtain data such as the value ranges of design parameters, component performance indicators, and performance indicators of the BIW structure. This data is then used as simulation data for subsequent training and validation of system-level and component-level decision tree models.

[0075] In some embodiments, after outputting the simulation data, the method further includes: detecting whether the simulation data meets the data volume requirement; if the simulation data meets the data volume requirement, entering the simulation data expansion phase; if the simulation data does not meet the data volume requirement, continuing to generate simulation data. Here, "reach" may include greater than or equal to. The embodiments of the present application enhance the robustness and stability of the decision tree model by performing data volume detection on the simulation data and continuing to generate simulation data when the simulation data does not meet the data volume requirement. By generating a large amount of simulation data,

[0076] In at least one embodiment of the present application, in order to increase data diversity and avoid the problem of model overfitting, data expansion can be performed, for example, expanding the test data, expanding the simulation data, or expanding the basic data. The embodiment of the present application is described by taking the expansion of simulation data as an example, and the method also includes: using a preset data expansion model to expand the simulation data to obtain expanded simulation data; updating the basic data based on the expanded simulation data, and using the updated basic data to train the system-level decision tree model and the component-level decision tree model. Among them, the expanded simulation data and the test data are used as the updated basic data. The simulation data is used as the source of the expansion data and is split into an expanded training set and an expanded validation set. The expanded training set is used to train the data expansion model, and the expanded validation set is used to evaluate the performance of the trained data expansion model. For example, 70% to 80% of the data in the simulation data is used as the expanded training set, and the remaining data is used as the expanded validation set. Among them, the data expansion model can include a generative adversarial network model, a variational autoencoder model, a support vector machine regression model, a Gaussian mixture model, etc., without limitation.

[0077] Please continue reading Figure 7 In the expansion stage of simulation data, the simulation data is divided into an expanded training set and an expanded verification set; based on the expanded training set, multiple preset models are used to perform model training to obtain multiple trained data expansion models; a target data expansion model is selected from the multiple trained data expansion models based on model performance; the expanded verification set is used to verify whether the target data expansion model passes the verification; if the verification passes, the verified target data expansion model is used to perform data expansion to obtain expanded simulation data; if the verification fails, the steps of using multiple preset models to perform model training based on the expanded training set to obtain multiple trained data expansion models are repeated until the data expansion model passes the verification.

[0078] In some embodiments, model performance may include but is not limited to data expansion rate, model complexity, training time, memory usage and other performance, wherein the data expansion rate is used to evaluate the rate at which the data expansion model generates new data. The greater the data expansion rate, the better the model performance; the smaller the data expansion rate, the worse the model performance. Model complexity is used to evaluate the complexity of the model structure. Training time is used to represent the training iteration time of the model. Memory usage is used to represent the memory usage of the model during training and prediction. The embodiment of the present application selects a model with better model performance from multiple trained data expansion models as the target data expansion model based on model performance, which can improve the effect of data expansion.

[0079] In some embodiments, using the expanded validation set to verify whether the target data expansion model passes may include: using the target data expansion model to generate new data, and using a preset error function to determine the error value between the new data and the expanded validation set; if the error value is less than or equal to the error threshold, then it is determined that the verification is passed; if the error value is greater than the error threshold, then it is determined that the verification fails. Among them, the error function may include a mean square error function, a cross entropy loss function, etc., and the error threshold can be set according to actual needs and is not limited here. The embodiment of the present application can evaluate the quality of the generated data and the generalization ability of the model on the new data by comparing the new data generated by the data expansion model with the expanded validation set, thereby improving the accuracy of the data expansion model training.

[0080] In the body-in-white structure design method provided in an embodiment of the present application, a preset data expansion model is used to expand basic data to obtain expanded basic data, and the expanded basic data is used to train a system-level decision tree model and a component-level decision tree model, so as to increase the diversity of data, improve the accuracy of the system-level decision tree model and the component-level decision tree model, and thereby improve the effect of the body-in-white structure design.

[0081] S23, constructing the system-level decision tree model and the component-level decision tree model based on the indicator-parameter framework and the basic data.

[0082] In at least one embodiment of the present application, during training, a system-level decision tree model is first trained, and then the component-level decision tree model to be trained is determined based on the determined system-level decision tree model. Based on this, the basic data is first split according to an indicator-parameter framework to obtain first data suitable for training the system-level decision tree model; then, the basic data is split according to the indicator-parameter framework to obtain second data suitable for training the component-level decision tree model.

[0083] In some embodiments, constructing the system-level decision tree model and the component-level decision tree model based on the indicator-parameter framework and the basic data includes: utilizing the indicator-parameter framework to parse the basic data to obtain first data and second data; utilizing a first decision tree algorithm based on the first data to obtain a system-level decision tree model; and utilizing a second decision tree algorithm based on the second data to obtain a component-level decision tree model. The first data includes performance indicators and component performance indicators of a plurality of designed body-in-white structures, and the second data includes performance indicators and design parameters of a plurality of designed body-in-white components. The first and second decision tree algorithms may be the same or different, without limitation. The first and second decision tree algorithms may include an Iterative Dichotomiser 3 (ID3) algorithm, a C4.5 algorithm, a Classification and Regression Tree (CART) algorithm, and the like. Among them, the first decision tree algorithm and the second decision tree algorithm can be determined based on the data type and / or data scale. For example, if the data set contains a large number of numerical features, the CART algorithm can be selected; if the data set contains more categorical features, the ID3 algorithm or the C4.5 algorithm can be selected; if the data volume is small and the feature dimension is low, the ID3 algorithm or the C4.5 algorithm can be selected; if the data volume is large, the CART algorithm can be selected. The embodiment of the present application uses an indicator-parameter framework to decompose the basic data to obtain the first data used for system-level decision tree model training and the second data for component-level decision tree model training. The adapted data features are screened according to the model requirements, which can reduce noise interference and improve the training accuracy of the system-level decision tree model and the component-level decision tree model.

[0084] For example, taking NVH as the vehicle performance indicator, based on the indicator-parameter framework, the performance indicators of the body-in-white structure can include the first-order bending / torsional mode, static bending / torsional stiffness, and suspension system dynamic stiffness characteristics. When the performance indicators of the body-in-white structure are the first-order bending / torsional mode, static bending / torsional stiffness, and suspension system dynamic stiffness characteristics, the component performance indicators can include the beam natural frequency, beam bending / torsional stiffness, test point stiffness, joint stiffness, joint mass, constraint point stiffness, and number of joints. Based on this, the basic data is analyzed, and the first-order bending / torsion mode and its corresponding beam natural frequency, beam bending / torsion stiffness, test point stiffness, joint stiffness, joint mass, constraint point stiffness and number of joints; the static bending / torsion stiffness and its corresponding beam natural frequency, beam bending / torsion stiffness, test point stiffness, joint stiffness, joint mass, constraint point stiffness and number of joints; and the dynamic stiffness characteristics of the suspension system and its corresponding beam natural frequency, beam bending / torsion stiffness, test point stiffness, joint stiffness, joint mass, constraint point stiffness and number of joints are taken as the first data.

[0085] For example, taking the beam natural frequency as a component performance indicator, based on the indicator-parameter framework, the value ranges of the component design parameters can include the threshold beam thickness range, crossbeam thickness range, longitudinal beam thickness range, threshold beam material, crossbeam material, and longitudinal beam material. Based on this, the beam natural frequency and its corresponding threshold beam thickness range, crossbeam thickness range, longitudinal beam thickness range, threshold beam material, crossbeam material, and longitudinal beam material are used as the second data.

[0086] Combine Figure 8 A flow chart illustrating the decision tree model training process provided in the embodiment of the present application is shown below. Figure 8 As shown, the training process of the decision tree model includes the training process of the system-level decision tree model and the training process of the component-level decision tree model, and the training process of the system-level decision tree model comes first and the training process of the component-level decision tree model comes later.

[0087] During the training process of a system-level decision tree model, the performance indicators of the body-in-white structure are set as the leaf node contents of the system-level decision tree model as the model requirements of the system-level decision tree model; based on the determined model requirements, the first data is divided into a first training set and a first validation set; based on the first training set, training is performed using a first decision tree algorithm; the trained system-level decision tree model is pruned to obtain an initial system-level decision tree model; the initial system-level decision tree model is verified using the first validation set to verify whether the initial system-level decision tree model meets the accuracy requirements; if the verification result shows that the initial system-level decision tree model meets the accuracy requirements, the initial system-level decision tree model is used as the target system-level decision tree model, and the training process of the component-level decision tree model is executed; if the verification result shows that the initial system-level decision tree model does not meet the accuracy requirements, training is performed again using the first decision tree algorithm based on the first training set until the verification passes, thereby obtaining the target system-level decision tree model. The accuracy requirements can be set according to actual needs and are not limited here. Pruning is used to remove some branches in the model to avoid overfitting. For pruning the decision tree model, you can refer to related technologies, which will not be described in detail here.

[0088] During the training process of the component-level decision tree model, the component performance index is set as the leaf node content of the component-level decision tree model as the model requirement of the component-level decision tree model; based on the determined model requirements, the second data is divided into a second training set and a second validation set; based on the second training set, the second decision tree algorithm is used for training; the trained component-level decision tree model is pruned to obtain an initial component-level decision tree model; the initial component-level decision tree model is verified using the second validation set to verify whether the initial component-level decision tree model meets the accuracy requirements; if the verification result shows that the initial component-level decision tree model meets the accuracy requirements, the initial component-level decision tree model is used as the target component-level decision tree model; if the verification result shows that the initial component-level decision tree model does not meet the accuracy requirements, the second decision tree algorithm is used to train again based on the second training set until the verification passes to obtain the target component-level decision tree model. Among them, the accuracy requirements can be set according to actual needs and are not limited here.

[0089] In the body-in-white structure design method provided in the embodiment of the present application, the indicator-parameter framework corresponding to the body-in-white structure to be designed is used to guide the generation of basic data, which can improve the accuracy of the basic data; and the present application uses the indicator-parameter framework and the basic data to construct the system-level decision tree model and the component-level decision tree model, so that the system-level decision tree model can describe the relationship between the performance indicators of the body-in-white structure and the performance indicators of the components, and the component-level decision tree model can describe the correspondence between the performance indicators of the component and the value range of the design parameters. The subsequent use of the system-level decision tree model and the component-level decision tree model to guide the design of the body-in-white structure can reduce the number of simulations and experiments of the body-in-white structure during the optimization design process, reduce development costs, and improve the design efficiency of the body-in-white structure.

[0090] See also Figure 9 , Figure 9 is a schematic diagram of the structure of a body-in-white structure design device provided in an embodiment of the present application. In some embodiments, the body-in-white structure design device 20 may include multiple functional modules composed of computer program segments. The computer program of each program segment in the body-in-white structure design device 20 may be stored in the memory of the computer device 10 and executed by at least one processor to perform (see Figure 2 Description) Function of body-in-white structural design.

[0091] In this embodiment, the body-in-white structure design device 20 can be divided into multiple functional modules based on their functions. These modules may include an indicator determination module 201 and a parameter determination module 202. A module, as used herein, refers to a series of computer program segments that can be executed by at least one processor and perform a fixed function, stored in memory. The functions of each module in this embodiment will be described in detail in subsequent embodiments.

[0092] The index determination module 201 may be used to determine target performance indexes corresponding to the body-in-white structure to be designed.

[0093] The indicator determination module 201 is further configured to determine target component indicators corresponding to the body-in-white structure to be designed based on the target performance indicators and using a preset system-level decision tree model.

[0094] The parameter determination module 202 is used to determine the value range of the design parameters of the components corresponding to the body-in-white structure based on the target component indicators and using a preset component-level decision tree model. The value range of the design parameters is used to design the body-in-white structure so that the body-in-white structure achieves the target performance indicators.

[0095] In some embodiments, the index determination module 201 can also be used to obtain the whole vehicle performance index corresponding to the vehicle to be designed; analyze the whole vehicle performance index to obtain the target performance index of the body-in-white structure corresponding to the vehicle.

[0096] In some embodiments, the indicator determination module 201 can also be used to traverse the system-level decision tree model to obtain performance indicators that match the target performance indicators; and determine that the component performance indicators corresponding to the matching performance indicators are the target component indicators corresponding to the body-in-white structure to be designed.

[0097] In some embodiments, the parameter determination module 202 can also be used to traverse the component-level decision tree model to obtain component performance indicators that match the target component indicators; determine that the value range of the design parameters of the component corresponding to the matching component performance indicators is the value range of the design parameters of the component corresponding to the white body structure.

[0098] In some embodiments, the parameter determination module 202 can also be used to construct an indicator-parameter framework corresponding to the body-in-white structure to be designed based on the design data corresponding to the designed body-in-white structure; use the indicator-parameter framework to determine basic data; and construct the system-level decision tree model and the component-level decision tree model based on the indicator-parameter framework and the basic data.

[0099] In some embodiments, the parameter determination module 202 can also be used to parse the design data to obtain the performance indicators, component performance indicators and design parameters of the body-in-white structure to be designed; use the performance indicators as the first node of the indicator-parameter framework; use the component performance indicators as the second node of the indicator-parameter framework; use the design parameters as the third node of the indicator-parameter framework; and construct the indicator-parameter framework based on the first node, the second node and the third node.

[0100] In some embodiments, the parameter determination module 202 can also be used to determine the vehicle model information corresponding to the body-in-white structure to be designed; determine the initial finite element models of multiple designed body-in-white structures that match the vehicle model information; obtain the finite element model of the body-in-white structure to be designed based on the multiple initial finite element models; use the indicator-parameter framework to determine the simulation conditions of the finite element model, and obtain the basic data based on the simulation conditions and the finite element model.

[0101] In some embodiments, the parameter determination module 202 can also be used to utilize the indicator-parameter framework to parse the basic data to obtain first data and second data; based on the first data, utilize the first decision tree algorithm to obtain a system-level decision tree model; based on the second data, utilize the second decision tree algorithm to obtain a component-level decision tree model.

[0102] It can be understood that the body-in-white structure design device 20 and the body-in-white structure design method of the above-mentioned embodiment belong to the same inventive concept. The specific implementation method of each module of the body-in-white structure design device 20 corresponds to the various steps of the body-in-white structure design method in the above-mentioned embodiment, and this application will not go into details here.

[0103] The module division described above is a logical functional division, and other division methods may be used in actual implementation. In addition, the functional modules in the various embodiments of the present application can be integrated into the same processing unit, or each module can exist physically separately, or two or more modules can be integrated into the same unit. The above-mentioned integrated modules can be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0104] then Figure 1 Regarding the description of the computer device, the communication module 101 may include a wired communication module and / or a wireless communication module. The wired communication module may provide one or more wired communication solutions such as universal serial bus (USB) and controller area network (CAN). The wireless communication module may provide one or more wireless communication solutions such as wireless fidelity (Wi-Fi), Bluetooth (BT), mobile communication network, frequency modulation (FM), near field communication (NFC), infrared technology (IR), etc.

[0105] In some embodiments, memory 102 may include one or more random access memories (RAMs) and one or more non-volatile memories (NVMs). The RAM can be directly read and written by processor 103 and can be used to store executable programs (e.g., machine instructions) of other running programs, as well as user and application data. RAM may include static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), etc.

[0106] In some embodiments, non-volatile memory can also store executable programs and user and application data, etc., which can be pre-loaded into random access memory for direct reading and writing by processor 103. Non-volatile memory can include disk storage devices and flash memory.

[0107] In other embodiments, the computer device 10 further includes an external memory interface for connecting to an external memory to expand the storage capacity of the computer device 10 .

[0108] In some embodiments, the processor 103 may include one or more processing units. For example, the processor 103 may include an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a video codec, a digital signal processor (DSP), and / or a neural-network processing unit (NPU). Different processing units may be independent devices or integrated into one or more processors.

[0109] It should be understood that the structures illustrated in the embodiments of the present application do not constitute a specific limitation on the computer device 10. In other embodiments of the present application, the computer device 10 may include more or fewer components than shown, or may combine or separate certain components, or arrange the components differently. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0110] An embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored. The computer program includes program instructions. The method implemented when the program instructions are executed can refer to the methods in the above-mentioned embodiments of the present application.

[0111] The computer-readable storage medium may be the internal memory of the computer device described in the above embodiments, such as the hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash memory card, etc.

[0112] In some embodiments, the computer-readable storage medium may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function, etc.; the data storage area may store data created according to the use of the computer device, etc.

[0113] In the several embodiments provided in this 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 merely illustrative. For example, the module division is merely a logical function division, and other division methods may be used in actual implementation.

[0114] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules may be selected to achieve the purpose of the solution of this embodiment according to actual needs.

[0115] In addition, the functional modules 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 in the form of hardware plus software functional modules.

[0116] Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the present application is defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the claims are intended to be embraced therein. Any reference to a figure in a claim should not be construed as limiting the claim to which it relates.

[0117] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in this application may also be implemented by a single unit or device through software or hardware. Terms such as first and second are used to indicate names and do not imply any particular order.

[0118] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A method for designing a body-in-white structure, characterized in that: The method comprises: Determine the target performance indicators corresponding to the body-in-white structure to be designed; Based on the target performance indicators, target component indicators corresponding to the body-in-white structure to be designed are determined using a preset system-level decision tree model, wherein the system-level decision tree model uses the performance indicators of the body-in-white structure as leaf node content and the component performance indicators as decision node content; Based on the target component index, a preset component-level decision tree model is used to determine a value range of a design parameter of a component corresponding to the body-in-white structure. The value range of the component design parameter is used to design the body-in-white structure so that the body-in-white structure achieves the target performance index. The component-level decision tree model uses the component performance index as a leaf node content and the value range of the component design parameter as a decision node content. The method for constructing the system-level decision tree model and the component-level decision tree model includes: Based on the design data corresponding to the designed body-in-white structure, constructing an indicator-parameter framework corresponding to the body-in-white structure to be designed; Determining basic data using the indicator-parameter framework, the basic data including simulation data and test data of the body-in-white structure to be designed, the simulation data generated based on the indicator-parameter framework and a finite element model, the finite element model being derived based on initial finite element models of a plurality of previously designed body-in-white structures, the initial finite element models of the plurality of previously designed body-in-white structures being matched with vehicle model information corresponding to the body-in-white structure to be designed; The system-level decision tree model and the component-level decision tree model are constructed based on the indicator-parameter framework and the basic data.

2. The body-in-white structure design method according to claim 1, wherein: Determining the target performance indicators corresponding to the body-in-white structure to be designed includes: Obtain the vehicle performance indicators corresponding to the vehicle to be designed; The vehicle performance index is analyzed to obtain a target performance index corresponding to the vehicle body-in-white structure.

3. The body-in-white structure design method according to claim 1, wherein: The method of determining target component indicators corresponding to the body-in-white structure to be designed based on the target performance indicators and utilizing a preset system-level decision tree model includes: Traversing the system-level decision tree model to obtain a performance indicator that matches the target performance indicator; The component performance index corresponding to the matched performance index is determined to be a target component index corresponding to the body-in-white structure to be designed.

4. The body-in-white structure design method according to claim 1, wherein: The method of determining the value range of the design parameters of the components corresponding to the body-in-white structure based on the target component index and using a preset component-level decision tree model includes: Traversing the component-level decision tree model to obtain component performance indicators that match the target component indicators; It is determined that the value range of the design parameter of the component corresponding to the matching component performance index is the value range of the design parameter of the component corresponding to the white body structure.

5. The body-in-white structure design method according to claim 1, wherein: The step of constructing an index-parameter framework corresponding to the body-in-white structure to be designed based on the design data corresponding to the designed body-in-white structure includes: Analyzing the design data to obtain performance indicators, component performance indicators, and design parameters of the body-in-white structure to be designed; Using the performance indicator as the first node of the indicator-parameter framework; Using the component performance index as the second node of the index-parameter framework; Using the design parameter as the third node of the indicator-parameter framework; The indicator-parameter framework is constructed based on the first node, the second node and the third node.

6. The method for designing a body-in-white structure according to claim 1, wherein: The use of the indicator-parameter framework to determine basic data includes: Determining vehicle model information corresponding to the body-in-white structure to be designed; determining initial finite element models of a plurality of designed body-in-white structures matching the vehicle model information; Based on the multiple initial finite element models, a finite element model of the body-in-white structure to be designed is obtained; generating simulation data of the body-in-white structure to be designed using the indicator-parameter framework and the finite element model; Acquire test data of the body-in-white structure to be designed, and combine the simulation data with the test data to obtain the basic data.

7. The method for designing a body-in-white structure according to claim 1, wherein: The constructing of the system-level decision tree model and the component-level decision tree model based on the indicator-parameter framework and the basic data includes: Utilizing the indicator-parameter framework, the basic data is parsed to obtain first data and second data, wherein the first data includes a plurality of performance indicators and component performance indicators of the designed body-in-white structure, and the second data includes a plurality of component performance indicators and design parameters of the designed body-in-white structure; Based on the first data, using a first decision tree algorithm, obtaining the system-level decision tree model; Based on the second data, a second decision tree algorithm is used to obtain the component-level decision tree model.

8. A computer device, characterized in that: The computer device includes a processor and a memory, and the processor is configured to implement the body-in-white structure design method according to any one of claims 1 to 7 when executing a computer program stored in the memory.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor of a computer device, implements the body-in-white structure design method according to any one of claims 1 to 7.

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