Vehicle data optimization method, device, equipment and storage medium

Abstract

The present application relates to the technical field of vehicle design, and particularly relates to a whole vehicle data optimization method, device, equipment and storage medium, the present application obtains the variable factor and initial thickness information of the target vehicle under each vehicle working condition, then determines the corresponding initial weight information according to the variable factor and the initial thickness information, and further establishes the quality response surface, establishes the association of the variable factor, the initial thickness information and the weight information, then realizes data optimization through the lightweight processing of the initial thickness information, further obtains the best target thickness, and finally determines the target whole vehicle mass corresponding to the target thickness, avoids the technical problems of large data processing and calculation workload and low data processing efficiency in the lightweight design of the vehicle body in the prior art, reduces the data processing workload, and improves the data processing efficiency.

Description

Technical Field

[0001] This invention relates to the field of vehicle design technology, and in particular to a method, apparatus, device, and storage medium for optimizing vehicle data. Background Technology

[0002] In the process of vehicle development, in order to reduce vehicle production costs, we can reduce vehicle weight or simplify vehicle structure to reduce production costs without affecting vehicle performance. However, the optimization of body weight is generally done by data processing for individual performance optimizations, and the optimization space is limited. Achieving body lightweighting without affecting body performance involves a large amount of data processing and calculation work, and the data processing efficiency is low.

[0003] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main objective of this invention is to provide a method, apparatus, device, and storage medium for optimizing vehicle data, aiming to solve the technical problems of large computational workload and low data processing efficiency in the prior art for lightweight vehicle body design.

[0005] To achieve the above objectives, the present invention provides a method for optimizing vehicle data, the method comprising the following steps:

[0006] Obtain the variable factors of the target vehicle under various vehicle operating conditions and the initial thickness information corresponding to the variable factors;

[0007] The corresponding initial weight information is determined based on the variable factor and the initial thickness information;

[0008] The mass response surface of the target vehicle is established based on the variable factors, the initial thickness information, and the initial weight information;

[0009] The initial thickness information is lightweighted by using a preset lightweight model to obtain the target thickness information;

[0010] The target vehicle mass is determined based on the target thickness information using the quality response surface.

[0011] Optionally, the vehicle operating conditions include: structural stiffness operating conditions, modal operating conditions, NVH operating conditions, and safety collision operating conditions;

[0012] The step of lightweighting the initial thickness information using a preset lightweight model to obtain the target thickness information includes:

[0013] Obtain the constraint factors and overall vehicle target values ​​of the target vehicle under each vehicle operating condition;

[0014] Based on the target value of the whole vehicle and the constraint factor, the initial thickness information is optimized for the first time to obtain the first thickness information;

[0015] Update the torsional stiffness parameter and torsional modal parameter in the constraint factor according to the preset performance data image;

[0016] The first thickness information is optimized a second time based on the updated torsional stiffness parameters and the updated torsional mode parameters to obtain the second thickness information;

[0017] The second thickness information is optimized a third time using a preset lightweight model to obtain the target thickness information.

[0018] Optionally, the constraint factors include: structural stiffness condition constraint factors, modal condition constraint factors, NVH condition constraint factors, and safety collision constraint factors, wherein the structural stiffness condition constraint factors include: torsional stiffness and bending stiffness, and the modal condition constraint factors include: torsional mode and bending mode.

[0019] The first data optimization of the initial thickness information based on the target value of the whole vehicle and the constraint factor includes:

[0020] Extract the vehicle collision displacement from the target vehicle value;

[0021] The vehicle collision displacement covers the safety collision condition constraint factor;

[0022] The torsional stiffness and torsional modes cover the NVH condition constraint factors;

[0023] The initial thickness information is optimized for the first time based on the torsional stiffness, bending stiffness, torsional mode, bending mode, and vehicle collision displacement.

[0024] Optionally, the NVH condition constraint factor includes: vibration transmission information; updating the torsional stiffness parameter and torsional modal parameter in the constraint factor according to the preset performance data image includes:

[0025] The proportionality coefficients between the vibration transmission information, the torsional mode, and the torsional stiffness are determined based on the preset performance data image.

[0026] The torsional stiffness parameter and the torsional modal parameter are adjusted according to the proportional coefficient.

[0027] Optionally, the safety collision condition constraint factors include: intrusion amount and intrusion velocity; the NVH condition constraint factors also include: dynamic stiffness parameters and noise transmission information;

[0028] The third data optimization of the second thickness information using a preset lightweight model includes:

[0029] Based on the torsional stiffness, bending stiffness, torsional mode, bending mode, dynamic stiffness parameter, vibration transmission information, noise transmission information, intrusion amount, and intrusion velocity, the second thickness information is optimized for the third time using a preset lightweight model.

[0030] Optionally, obtaining the variable factors of the target vehicle under various vehicle operating conditions and the initial thickness information corresponding to the variable factors includes:

[0031] Determine the partial derivatives of the initial variable factors of the target vehicle under each vehicle operating condition with respect to the initial variable factors;

[0032] Sensitivity analysis is performed on the initial variable factors based on the partial derivatives to obtain the sensitivity analysis results;

[0033] The initial variable factors are screened based on the sensitivity analysis results to obtain the variable factors, and the initial thickness information corresponding to the variable factors is obtained.

[0034] Optionally, establishing the mass response surface of the target vehicle based on the variable factor, the initial thickness information, and the initial weight information includes:

[0035] Obtain the operating conditions of the target vehicle;

[0036] Determine the target response surface establishment strategy based on the described operating conditions;

[0037] The mass response surface of the target vehicle is established based on the variable factors, the initial thickness information, and the initial weight information using a target response surface establishment strategy.

[0038] Obtain the accuracy information of the quality response surface;

[0039] When the accuracy information is not less than a preset accuracy threshold, the step of performing lightweight processing on the initial thickness information through a preset lightweight model to obtain the target thickness information is executed.

[0040] Furthermore, to achieve the above objectives, the present invention also proposes a vehicle data optimization device, the vehicle data optimization device comprising:

[0041] The information acquisition module is used to acquire the variable factors of the target vehicle under various vehicle operating conditions and the initial thickness information corresponding to the variable factors.

[0042] The weight determination module is used to determine the corresponding initial weight information based on the variable factor and the initial thickness information;

[0043] The response surface construction module is used to construct the mass response surface of the target vehicle based on the variable factors, the initial thickness information, and the initial weight information.

[0044] The lightweight processing module is used to lightweight process the initial thickness information using a preset lightweight model to obtain the target thickness information.

[0045] The quality query module is used to determine the target vehicle mass based on the target thickness information and the quality response surface.

[0046] Furthermore, to achieve the above objectives, the present invention also proposes a vehicle data optimization device, which includes: a memory, a processor, and a vehicle data optimization program stored in the memory and executable on the processor. The vehicle data optimization program is configured to implement the steps of the vehicle data optimization method described above.

[0047] Furthermore, to achieve the above objectives, the present invention also proposes a storage medium storing a vehicle data optimization program, wherein when the vehicle data optimization program is executed by a processor, it implements the steps of the vehicle data optimization method described above.

[0048] This invention discloses a vehicle data optimization method, which includes: acquiring variable factors of a target vehicle under various vehicle operating conditions and the initial thickness information corresponding to the variable factors; determining the corresponding initial weight information based on the variable factors and the initial thickness information; establishing a mass response surface of the target vehicle based on the variable factors, the initial thickness information, and the initial weight information; performing lightweight processing on the initial thickness information using a preset lightweight model to obtain target thickness information; and determining the target vehicle mass based on the target thickness information using the mass response surface. Compared with existing technologies, this invention obtains variable factors and initial thickness information of the target vehicle under various vehicle operating conditions, determines the corresponding initial weight information based on the variable factors and the initial thickness information, establishes a mass response surface, establishes a correlation between the variable factors, initial thickness information, and weight information, and then optimizes the data through lightweight processing of the initial thickness information to obtain the optimal target thickness. Finally, it determines the target vehicle mass corresponding to the target thickness. This avoids the technical problems of large data processing workload and low data processing efficiency in the prior art for vehicle body lightweight design, reduces the data processing workload, and improves data processing efficiency. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of the structure of the vehicle data optimization device in the hardware operating environment involved in the embodiments of the present invention;

[0050] Figure 2 This is a flowchart illustrating the first embodiment of the vehicle data optimization method of the present invention;

[0051] Figure 3 This is a flowchart illustrating the second embodiment of the vehicle data optimization method of the present invention;

[0052] Figure 4 This is a schematic diagram of the first data optimization performance parameters of an embodiment of the vehicle data optimization method of the present invention;

[0053] Figure 5 This is a schematic diagram showing the relationship between torsional stiffness, modal characteristics, and vibration transmission conditions in an embodiment of the vehicle data optimization method of the present invention.

[0054] Figure 6 This is a schematic diagram of the second data optimization performance parameters of an embodiment of the vehicle data optimization method of the present invention;

[0055] Figure 7 This is a schematic diagram of the third data optimization performance parameters of an embodiment of the vehicle data optimization method of the present invention;

[0056] Figure 8 This is a structural block diagram of the first embodiment of the vehicle data optimization device of the present invention.

[0057] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0058] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0059] Reference Figure 1 , Figure 1 This is a schematic diagram of the vehicle data optimization device structure in the hardware operating environment involved in the embodiments of the present invention.

[0060] like Figure 1As shown, the vehicle data optimization device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wireless-Fidelity (Wi-Fi) interface). The memory 1005 may be high-speed random access memory (RAM) or stable non-volatile memory (NVM), such as a disk storage device. The memory 1005 may also optionally be a storage device independent of the aforementioned processor 1001.

[0061] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the vehicle data optimization equipment and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0062] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and a vehicle data optimization program.

[0063] exist Figure 1 In the vehicle data optimization device shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the vehicle data optimization device of the present invention can be set in the vehicle data optimization device. The vehicle data optimization device calls the vehicle data optimization program stored in the memory 1005 through the processor 1001 and executes the vehicle data optimization method provided in the embodiment of the present invention.

[0064] This invention provides a method for optimizing vehicle data, referring to... Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of a vehicle data optimization method according to the present invention.

[0065] In this embodiment, the vehicle data optimization method includes the following steps:

[0066] Step S10: Obtain the variable factors of the target vehicle under each vehicle operating condition and the initial thickness information corresponding to the variable factors.

[0067] It should be noted that the execution subject in the method of this embodiment can be a device with data acquisition, data transmission and data processing functions, such as a test device, a control computer and a mobile terminal. This embodiment does not make specific limitations on this. In this embodiment and the following embodiments, a test device will be used as an example for explanation.

[0068] In this embodiment, since the variable factors set may be the same under different single operating conditions, the performance optimization results may be opposite when the thickness value of the variable factor is changed. Furthermore, the vehicle body quality optimization based on the performance under a single operating condition may not yield the optimal quality parameters under the current performance state. If the performance is optimized under multiple operating conditions at the same time, the workload of data processing will be too large, which will consume a lot of human or computing resources.

[0069] It is worth noting that variable factors refer to structural accessories that affect vehicle body quality under at least one of the following conditions: modal, structural stiffness, NVH, and safety collision. The setting of variable factors can vary depending on the vehicle's optimized performance. For example, in the NVH condition, variable factors can be accessories such as left and right suspensions, the frame, and spring seats; in the safety collision condition, variable factors can be structural accessories in areas such as the B-pillar, front doors, and rear doors; and in the structural stiffness condition, variable factors can be set as structural accessories in areas such as door sills or longitudinal beams. This embodiment does not impose specific limitations on these aspects.

[0070] In practical implementation, since the impact of some structural accessories on the overall vehicle operating conditions varies, there are some accessories that have a relatively small impact on vehicle performance. When setting variable factors, all structural accessories that affect the vehicle are used as variable factors. In order to reduce the amount of invalid data to process and improve data processing efficiency, this embodiment can screen variable factors and remove variable factors that have a relatively small impact on vehicle performance to improve data processing efficiency.

[0071] Furthermore, in order to eliminate variable factors that have a relatively small impact on vehicle performance, step S10 includes:

[0072] Determine the partial derivatives of the initial variable factors of the target vehicle under each vehicle operating condition with respect to the initial variable factors;

[0073] Sensitivity analysis is performed on the initial variable factors based on the partial derivatives to obtain the sensitivity analysis results;

[0074] The initial variable factors are screened based on the sensitivity analysis results to obtain the variable factors, and the initial thickness information corresponding to the variable factors is obtained.

[0075] It should be noted that the initial variable factors refer to the structural attachments that affect the performance of the vehicle under a single operating condition; the partial derivatives are used to quantify the degree of influence of each variable factor on the performance of the vehicle under various operating conditions.

[0076] In practice, based on the design sensitivity analysis and the partial derivatives of the design response with respect to the optimization variables, the magnitude of the influence of each variable factor on the performance of the vehicle under various operating conditions can be determined.

[0077] Step S20: Determine the corresponding initial weight information based on the variable factor and the initial thickness information.

[0078] It is understandable that by calculating the variable factors and the initial thickness information corresponding to the variable factors, a set of accurate performance parameters and the initial weight of the vehicle body can be obtained. This process can involve uploading the variable factors and initial thickness information to a preset cloud platform for calculation. During the calculation process, the material properties of various accessories of the vehicle body can also be referenced to obtain the initial weight information of the vehicle.

[0079] Step S30: Establish the mass response surface of the target vehicle based on the variable factors, the initial thickness information, and the initial weight information.

[0080] It should be understood that the mass response surface refers to the response surface relating variable factors, initial thickness information, and initial weight information. When thickness information is used as a variable, mass, modal characteristics, and structural stiffness have strong linear characteristics and can be fitted using a polynomial method. However, for NVH and safety collision conditions, due to the strong nonlinear relationship, the kriging method or neural network method can be used to establish the mass response surface. This embodiment does not impose specific restrictions on this.

[0081] Furthermore, to improve the reliability of the quality response surface, establishing the quality response surface of the target vehicle based on the variable factors, the initial thickness information, and the initial weight information includes:

[0082] Obtain the operating conditions of the target vehicle;

[0083] Determine the target response surface establishment strategy based on the described operating conditions;

[0084] The mass response surface of the target vehicle is established based on the variable factors, the initial thickness information, and the initial weight information using a target response surface establishment strategy.

[0085] Obtain the accuracy information of the quality response surface;

[0086] When the accuracy information is not less than a preset accuracy threshold, the step of performing lightweight processing on the initial thickness information through a preset lightweight model to obtain the target thickness information is executed.

[0087] It should be noted that the accuracy information of the quality response surface is used to quantify the degree of fit between the estimated or predicted value of the trend line and the actual data. The higher the degree of fit, the greater the accuracy information. The preset accuracy threshold can be set to a value no greater than 1. The preset accuracy threshold can be set to 0.8. This embodiment does not impose specific restrictions on this.

[0088] In the specific implementation, after establishing the quality response surface for each working condition, it is necessary to calculate the accuracy information of the quality response surface for each working condition. When the accuracy information of the quality response surface for each working condition is not less than 0.8, each quality response surface is output, and the initial thickness information is lightened by a preset lightweight model to obtain the target thickness information. If the accuracy information of the quality response surface for each working condition is less than 0.8, the scheme of the quality response surface needs to be adjusted. The adjustment scheme may be to re-select variable factors, change the approximate model establishment method, and increase the number of samples, etc. This embodiment does not impose specific restrictions on this.

[0089] Step S40: Lightweight the initial thickness information using a preset lightweight model to obtain the target thickness information.

[0090] It is worth noting that the preset lightweight model is used to optimize the initial thickness information of variable factors and perform lightweight processing to obtain the lightest vehicle weight without affecting vehicle performance.

[0091] It is understandable that traditional lightweighting processes are generally constrained by target values ​​defined by existing operating conditions, which makes it difficult to obtain optimization results or results that are not ideal. In this embodiment, however, lightweighting will be performed on the target vehicle's data through three-wheel data optimization.

[0092] Step S50: Determine the target vehicle mass based on the target thickness information using the mass response surface.

[0093] It should be understood that after determining each target variable factor and the corresponding target thickness information, the corresponding accessory name can be determined based on the quality response surface. Then, the mass of the accessory can be determined by the accessory name, the accessory thickness, and the material properties of the accessory, thereby determining the mass value of the whole vehicle.

[0094] This embodiment discloses a vehicle data optimization method, which includes: acquiring variable factors of a target vehicle under various vehicle operating conditions and the initial thickness information corresponding to the variable factors; determining the corresponding initial weight information based on the variable factors and the initial thickness information; establishing a mass response surface of the target vehicle based on the variable factors, the initial thickness information, and the initial weight information; performing lightweight processing on the initial thickness information using a preset lightweight model to obtain target thickness information; and determining the target vehicle mass based on the target thickness information using the mass response surface. This embodiment obtains variable factors and initial thickness information of the target vehicle under various vehicle operating conditions, determines the corresponding initial weight information based on the variable factors and the initial thickness information, establishes a mass response surface, establishes the correlation between variable factors, initial thickness information, and weight information, and then optimizes the data by performing lightweight processing on the initial thickness information to obtain the optimal target thickness. Finally, it determines the target vehicle mass corresponding to the target thickness. This avoids the technical problems of large data processing workload and low data processing efficiency in the prior art for vehicle body lightweight design, reduces the data processing workload, and improves data processing efficiency.

[0095] refer to Figure 3 , Figure 3 This is a flowchart illustrating a second embodiment of a vehicle data optimization method according to the present invention.

[0096] Based on the first embodiment described above, in this embodiment, step S40 includes:

[0097] Step S401: Obtain the constraint factor and target value of the target vehicle under each vehicle operating condition.

[0098] It should be noted that the constraint factors under each vehicle operating condition include: structural stiffness constraint factors, modal constraint factors, NVH constraint factors, and safety collision constraint factors; among them, structural stiffness constraint factors include torsional stiffness and bending stiffness; modal constraint factors include torsional mode and bending mode; NVH constraint factors include dynamic stiffness parameters, vibration transmission information, and noise transmission information; and safety collision constraint factors include intrusion amount and intrusion speed.

[0099] It is worth noting that the target values ​​for the whole vehicle can be the vehicle collision displacement, torsional mode, and torsional stiffness, etc., and this embodiment does not impose specific restrictions on them.

[0100] Step S402: Perform the first data optimization on the initial thickness information based on the target value of the whole vehicle and the constraint factor to obtain the first thickness information.

[0101] It should be noted that before performing the first data optimization on the initial thickness information, the target value of a single working condition can be replaced by the target value of the whole vehicle. For example, the frontal collision displacement can be replaced by the whole vehicle collision displacement, and the modal and torsional stiffness can be used to approximate the NVH related performance. In this way, the amount of processing can be reduced and the data processing efficiency can be improved in the first data optimization process for working conditions with multiple target values.

[0102] Further, step S402 includes:

[0103] Extract the vehicle collision displacement from the target vehicle value;

[0104] The vehicle collision displacement covers the safety collision condition constraint factor;

[0105] The torsional stiffness and torsional modes cover the NVH condition constraint factors;

[0106] The initial thickness information is optimized for the first time based on the torsional stiffness, bending stiffness, torsional mode, bending mode, and vehicle collision displacement.

[0107] Understandably, in the initial data optimization process, the constraint factors for the vehicle's operating conditions are limited to five: torsional stiffness, bending stiffness, torsional mode, bending mode, and vehicle collision displacement. The initial data optimization is performed based on these five constraint factors, thereby reducing the number of performance parameters examined, increasing the optimization space, and shortening the optimization time. Furthermore, since the requirements for each performance parameter are inconsistent, the parameter requirements for each performance parameter can also be referenced during data optimization. Figure 4 , Figure 4 These are some of the performance-related parameter requirements during the first data optimization process.

[0108] Step S403: Update the torsional stiffness parameter and torsional mode parameter in the constraint factor according to the preset performance data image.

[0109] It should be understood that during the second data optimization process, the performance of the safety collision has been improved to the initial level, but the performance of the vibration transmission condition is still poor. It is necessary to continue to optimize the performance of the vibration transmission condition. If all the performance of the vibration transmission condition is constrained within the acceptable range, it will be difficult to find the ideal optimization result, or the weight reduction effect will be very poor. Therefore, it is necessary to obtain the relationship between the torsional stiffness, modes and vibration transmission condition in the first data optimization process.

[0110] Further, step S403 includes:

[0111] The proportionality coefficients between the vibration transmission information, the torsional mode, and the torsional stiffness are determined based on the preset performance data image.

[0112] The torsional stiffness parameter and the torsional modal parameter are adjusted according to the proportional coefficient.

[0113] In the specific implementation, refer to Figure 5 , Figure 5 This is a schematic diagram showing the relationship between torsional stiffness, modal characteristics, and vibration transmission performance. Figure 5 It can be seen that the various performance parameters of the vibration transmission condition are directly proportional to the torsional mode and stiffness. Therefore, by determining the proportionality coefficients between the vibration transmission information and the torsional mode and torsional stiffness, the torsional stiffness and torsional mode can be adjusted to improve the overall vehicle vibration transmission performance. For example, the torsional stiffness constraint can be increased from 13000 Nm / deg to 14000 Nm / deg, and the torsional mode constraint from 31.94 Hz to 32.4 Hz. This embodiment does not impose specific limitations on these parameters. After adjusting the torsional stiffness and torsional mode, the performance parameters for the second data optimization are referenced. Figure 6 .

[0114] Step S404: Perform a second data optimization on the first thickness information based on the updated torsional stiffness parameters and the updated torsional mode parameters to obtain the second thickness information.

[0115] Step S405: Perform a third data optimization on the second thickness information using a preset lightweight model to obtain the target thickness information.

[0116] It is understandable that the preset lightweight model can be a data processing model based on the mitigation optimization algorithm, used to perform a third data optimization on the thickness information. In the third data optimization process, the weight will be reduced based on all the performance tests under each working condition, and the target value of the whole vehicle will no longer replace the constraint factor of a single working condition.

[0117] Further, step S405 includes:

[0118] Based on the torsional stiffness, bending stiffness, torsional mode, bending mode, dynamic stiffness parameter, vibration transmission information, noise transmission information, intrusion amount, and intrusion velocity, the second thickness information is optimized for the third time using a preset lightweight model.

[0119] In the specific implementation, refer to Figure 7 The parameter requirements for performance evaluation under various operating conditions in the third data optimization.

[0120] This embodiment discloses the following steps: obtaining the constraint factors and target values ​​of the target vehicle under various vehicle operating conditions; performing a first data optimization on the initial thickness information based on the target value and the constraint factors to obtain first thickness information; updating the torsional stiffness parameters and torsional modal parameters in the constraint factors based on a preset performance data image; performing a second data optimization on the first thickness information based on the updated torsional stiffness parameters and updated torsional modal parameters to obtain second thickness information; and performing a third data optimization on the second thickness information using a preset lightweighting model to obtain target thickness information. This embodiment achieves maximum vehicle lightweighting while ensuring performance under various operating conditions through three data optimizations, and avoids the potential conflict between performance optimization data under a single operating condition.

[0121] Furthermore, this embodiment of the invention also proposes a storage medium storing a vehicle data optimization program, which, when executed by a processor, implements the steps of the vehicle data optimization method described above.

[0122] Since this storage medium adopts all the technical solutions of all the above embodiments, it has at least all the beneficial effects brought about by the technical solutions of the above embodiments, which will not be repeated here.

[0123] Reference Figure 8 , Figure 8 This is a structural block diagram of the first embodiment of the vehicle data optimization device of the present invention.

[0124] like Figure 8 As shown, the vehicle data optimization device proposed in this embodiment of the invention includes:

[0125] The information acquisition module 100 is used to acquire the variable factors of the target vehicle under various vehicle operating conditions and the initial thickness information corresponding to the variable factors.

[0126] The weight determination module 200 is used to determine the corresponding initial weight information based on the variable factor and the initial thickness information.

[0127] The response surface establishment module 300 is used to establish the mass response surface of the target vehicle based on the variable factors, the initial thickness information, and the initial weight information.

[0128] The lightweight processing module 400 is used to lightweight process the initial thickness information through a preset lightweight model to obtain the target thickness information.

[0129] The quality query module 500 is used to determine the target vehicle mass based on the target thickness information through the quality response surface.

[0130] This embodiment discloses a vehicle data optimization method, which includes: acquiring variable factors of a target vehicle under various vehicle operating conditions and the initial thickness information corresponding to the variable factors; determining the corresponding initial weight information based on the variable factors and the initial thickness information; establishing a mass response surface of the target vehicle based on the variable factors, the initial thickness information, and the initial weight information; performing lightweight processing on the initial thickness information using a preset lightweight model to obtain target thickness information; and determining the target vehicle mass based on the target thickness information using the mass response surface. This embodiment obtains variable factors and initial thickness information of the target vehicle under various vehicle operating conditions, determines the corresponding initial weight information based on the variable factors and the initial thickness information, establishes a mass response surface, establishes the correlation between variable factors, initial thickness information, and weight information, and then optimizes the data by performing lightweight processing on the initial thickness information to obtain the optimal target thickness. Finally, it determines the target vehicle mass corresponding to the target thickness. This avoids the technical problems of large data processing workload and low data processing efficiency in the prior art for vehicle body lightweight design, reduces the data processing workload, and improves data processing efficiency.

[0131] In one embodiment, the lightweight processing module 400 is further configured to: acquire the constraint factors and overall vehicle target values ​​of the target vehicle under various vehicle operating conditions; perform a first data optimization on the initial thickness information based on the overall vehicle target value and the constraint factors to obtain first thickness information; update the torsional stiffness parameters and torsional modal parameters in the constraint factors based on a preset performance data image; perform a second data optimization on the first thickness information based on the updated torsional stiffness parameters and updated torsional modal parameters to obtain second thickness information; and perform a third data optimization on the second thickness information using a preset lightweight model to obtain target thickness information.

[0132] In one embodiment, the lightweight processing module 400 is further configured to extract the vehicle collision displacement from the vehicle target value; cover the safety collision condition constraint factor according to the vehicle collision displacement; cover the NVH condition constraint factor according to the torsional stiffness and torsional mode; and perform a first data optimization on the initial thickness information according to the torsional stiffness, the bending stiffness, the torsional mode, the bending mode, and the vehicle collision displacement.

[0133] In one embodiment, the lightweight processing module 400 is further configured to determine a proportionality coefficient between the vibration transmission information, the torsional mode, and the torsional stiffness based on the preset performance data image; and adjust the torsional stiffness parameter and the torsional mode parameter according to the proportionality coefficient.

[0134] In one embodiment, the lightweight processing module 400 is further configured to perform a third data optimization on the second thickness information based on the torsional stiffness, the bending stiffness, the torsional mode, the bending mode, the dynamic stiffness parameter, the vibration transmission information, the noise transmission information, the intrusion amount, and the intrusion velocity through a preset lightweight model.

[0135] In one embodiment, the information acquisition module 100 is further configured to determine the partial derivatives of the initial variable factors of the target vehicle under various vehicle operating conditions and the initial variable factors; perform sensitivity analysis on the initial variable factors based on the partial derivatives to obtain sensitivity analysis results; filter the initial variable factors based on the sensitivity analysis results to obtain variable factors, and obtain the initial thickness information corresponding to the variable factors.

[0136] In one embodiment, the response surface establishment module 300 is further configured to: acquire the operating conditions of the target vehicle; determine a target response surface establishment strategy based on the operating conditions; establish the mass response surface of the target vehicle based on the variable factors, the initial thickness information, and the initial weight information through the target response surface establishment strategy; acquire the accuracy information of the mass response surface; and, when the accuracy information is not less than a preset accuracy threshold, perform a step of lightweighting the initial thickness information using a preset lightweighting model to obtain the target thickness information.

[0137] It should be understood that the above are merely illustrative examples and do not constitute any limitation on the technical solutions of the present invention. In specific applications, those skilled in the art can make settings as needed, and the present invention does not impose any restrictions on this.

[0138] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of this invention. In practical applications, those skilled in the art can select some or all of the workflow to achieve the purpose of this embodiment according to actual needs, and no restrictions are imposed here.

[0139] In addition, for technical details not described in detail in this embodiment, please refer to the vehicle data optimization method provided in any embodiment of the present invention, which will not be repeated here.

[0140] Furthermore, it should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0141] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0142] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory (ROM) / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0143] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A vehicle data optimization method, characterized by, The whole vehicle data optimization method comprises: Obtaining variable factors of a target vehicle under each vehicle working condition and initial thickness information corresponding to the variable factors; Determining corresponding initial weight information according to the variable factors and the initial thickness information; Establishing a mass response surface of the target vehicle according to the variable factors, the initial thickness information and the initial weight information; Lightweight processing the initial thickness information through a preset lightweight model to obtain target thickness information; Determining a target whole vehicle mass through the mass response surface according to the target thickness information; The lightweight processing of the initial thickness information through the preset lightweight model to obtain the target thickness information comprises: Obtaining constraint factors and whole vehicle target values of the target vehicle under each vehicle working condition, the vehicle working conditions comprising a structural rigidity working condition, a modal working condition, an NVH working condition and a safety collision working condition, the constraint factors comprising a structural rigidity working condition constraint factor, a modal working condition constraint factor, an NVH working condition constraint factor and a safety collision constraint factor, wherein the structural rigidity working condition constraint factor comprises torsional rigidity and bending rigidity, and the modal working condition constraint factor comprises torsional mode and bending mode; Extracting whole vehicle collision displacement in the whole vehicle target values, covering the safety collision working condition constraint factor according to the whole vehicle collision displacement, covering the NVH working condition constraint factor according to the torsional rigidity and the torsional mode, and performing first data optimization on the initial thickness information according to the torsional rigidity, the bending rigidity, the torsional mode, the bending mode and the whole vehicle collision displacement to obtain first thickness information; Updating torsional rigidity parameters and torsional mode parameters in the constraint factors according to a preset performance data image; Performing second data optimization on the first thickness information according to the updated torsional rigidity parameters and the updated torsional mode parameters to obtain second thickness information; Performing third data optimization on the second thickness information through a preset lightweight model to obtain target thickness information.

2. The vehicle data optimization method of claim 1, wherein, The NVH working condition constraint factor comprises vibration transmission information; The updating of the torsional rigidity parameters and the torsional mode parameters in the constraint factors according to the preset performance data image comprises: Determining a proportionality coefficient among the vibration transmission information, the torsional mode and the torsional rigidity according to the preset performance data image; Adjusting the torsional rigidity parameters and the torsional mode parameters according to the proportionality coefficient.

3. The vehicle data optimization method of claim 2, wherein, The safety collision working condition constraint factor comprises intrusion amount and intrusion speed, and the NVH working condition constraint factor further comprises dynamic rigidity parameters and noise transmission information; The third data optimization on the second thickness information through the preset lightweight model comprises: Performing third data optimization on the second thickness information through a preset lightweight model based on the torsional rigidity, the bending rigidity, the torsional mode, the bending mode, the dynamic rigidity parameters, the vibration transmission information, the noise transmission information, the intrusion amount and the intrusion speed.

4. The vehicle data optimization method of claim 1, wherein, The obtaining of the variable factors of the target vehicle under each vehicle working condition and the initial thickness information corresponding to the variable factors comprises: determining initial variable factors of the target vehicle in each vehicle working condition and partial derivatives of the initial variable factors; performing sensitivity analysis on the initial variable factors according to the partial derivatives to obtain a sensitivity analysis result; screening the initial variable factors according to the sensitivity analysis result to obtain variable factors and obtaining initial thickness information corresponding to the variable factors.

5. The vehicle data optimization method of any one of claims 1 to 4, wherein, establishing the mass response surface of the target vehicle according to the variable factors, the initial thickness information and the initial weight information, including: obtaining a running working condition of the target vehicle; determining a target response surface establishment strategy according to the running working condition; establishing the mass response surface of the target vehicle by the target response surface establishment strategy according to the variable factors, the initial thickness information and the initial weight information; obtaining accuracy information of the mass response surface; when the accuracy information is not less than a preset accuracy threshold, performing a step of lightweight processing of the initial thickness information by a preset lightweight model to obtain target thickness information.

6. A vehicle data optimization device characterized by comprising: The vehicle data optimization device includes: an information acquisition module configured to acquire variable factors of a target vehicle in each vehicle working condition and initial thickness information corresponding to the variable factors; a weight determination module configured to determine corresponding initial weight information according to the variable factors and the initial thickness information; a response surface establishment module configured to establish a mass response surface of the target vehicle according to the variable factors, the initial thickness information and the initial weight information; a lightweight processing module configured to perform lightweight processing of the initial thickness information by a preset lightweight model to obtain target thickness information; a mass query module configured to determine a target vehicle mass by the mass response surface according to the target thickness information; The lightweight processing module is further configured to acquire constraint factors and vehicle target values of the target vehicle in the each vehicle working condition, the vehicle working condition including a structural rigidity working condition, a modal working condition, an NVH working condition and a safety collision working condition, and the constraint factors including a structural rigidity working condition constraint factor, a modal working condition constraint factor, an NVH working condition constraint factor and a safety collision constraint factor, wherein the structural rigidity working condition constraint factor includes torsional rigidity and bending rigidity, and the modal working condition constraint factor includes torsional mode and bending mode. extracting vehicle collision displacement in the vehicle target values, covering the safety collision working condition constraint factor according to the vehicle collision displacement, covering the NVH working condition constraint factor according to the torsional rigidity and the torsional mode, and performing first data optimization on the initial thickness information according to the torsional rigidity, the bending rigidity, the torsional mode, the bending mode and the vehicle collision displacement to obtain first thickness information; updating torsional rigidity parameters and torsional mode parameters in the constraint factors according to a preset performance data image; performing second data optimization on the first thickness information according to the updated torsional rigidity parameters and the updated torsional mode parameters to obtain second thickness information; performing third data optimization on the second thickness information by a preset lightweight model to obtain target thickness information.

7. A vehicle data optimization device characterized by comprising: The vehicle data optimization device comprises a memory, a processor, and a vehicle data optimization program stored in the memory and executable on the processor, and the vehicle data optimization program is configured to implement the vehicle data optimization method according to any one of claims 1 to 5.

8. A storage medium, characterized by The storage medium stores a vehicle data optimization program, and the vehicle data optimization program is executed by the processor to implement the vehicle data optimization method according to any one of claims 1 to 5.

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