Suspension strength verification working condition generation method and device, equipment and storage medium

Abstract

The invention provides a suspension strength verification working condition generation method and device, equipment and a storage medium, and the method comprises the steps: building a suspension parameter relation model corresponding to a target vehicle model based on a suspension simulation model corresponding to the target vehicle model; determining a target acceleration and a target torque according to the suspension parameter relation model and a pre-obtained suspension three-component test value; inputting the target acceleration and the target torque into a suspension simulation model to obtain a suspension three-component simulation value; and under the condition that the suspension three-component test value is matched with the suspension three-component simulation value, generating a target working condition according to the target acceleration and the target torque. According to the method, the target acceleration and the target torque which correspond to the suspension three-component force test value and are loaded to the power assembly mass center are reversely derived through the suspension parameter relation model, the target working condition is obtained, and the working condition which is matched with the vehicle type and used for carrying out suspension strength verification can be obtained; and the problem of insufficient verification or excessive verification caused by using a general working condition for verification is solved.

Description

Technical Field

[0001] The present application relates to the field of automobile technology, and in particular to a method, device, equipment and storage medium for generating a suspension strength verification condition. Background Art

[0002] As the connecting structure between the powertrain and the vehicle body or frame, the main function of the suspension is to support the powertrain, reduce the impact of the vibration of the powertrain on the vehicle, and limit the vibration of the powertrain. During the driving process of the vehicle, it will be in various working conditions. The suspension needs to be able to adapt to these different working conditions to ensure that the powertrain can work stably in various conditions and avoid excessive stress on the vehicle body structure.

[0003] In order to verify the strength of the suspension, the existing technology mainly obtains the acceleration and torque corresponding to the 28 common working conditions in the industry as test data, loads them to the center of mass of the powertrain, and obtains the stress of the suspension to verify the strength of the suspension under these 28 working conditions.

[0004] However, considering the different positioning of various vehicle models, there are great differences in the applicable working conditions. If the strength of the suspension is verified through the universal 28 working conditions, there will be problems of insufficient verification or over-verification. Summary of the invention

[0005] The embodiments of the present application provide a method, device, equipment and storage medium for generating suspension strength verification conditions to solve the problem of insufficient verification or over-verification in the prior art when using the general 28 conditions as the suspension strength verification conditions.

[0006] In a first aspect, an embodiment of the present application provides a method for generating a suspension strength verification condition, the method comprising:

[0007] Based on the suspension simulation model corresponding to the target vehicle model, a suspension parameter relationship model corresponding to the target vehicle model is created, wherein the suspension parameter relationship model includes the corresponding relationship between the powertrain center of mass acceleration and torque corresponding to the target vehicle model and the three-part suspension force;

[0008] Determine, according to the suspension parameter relationship model and the pre-acquired suspension three-force test value, a target acceleration and a target torque corresponding to the suspension three-force test value;

[0009] Inputting the target acceleration and the target torque into the suspension simulation model to obtain a suspension three-force simulation value;

[0010] When the suspension three-force test value matches the suspension three-force simulation value, a target operating condition is generated according to the target acceleration and the target torque.

[0011] In a second aspect, an embodiment of the present application further provides a device for generating a suspension strength verification condition, the device comprising:

[0012] A creation module, used to create a suspension parameter relationship model corresponding to the target vehicle model based on the suspension simulation model corresponding to the target vehicle model, wherein the suspension parameter relationship model includes the corresponding relationship between the powertrain center of mass acceleration and torque corresponding to the target vehicle model and the three-part suspension force;

[0013] A first determination module, configured to determine a target acceleration and a target torque corresponding to the suspension three-force test value according to the suspension parameter relationship model and the pre-acquired suspension three-force test value;

[0014] A second determination module is used to input the target acceleration and the target torque into the suspension simulation model to obtain a suspension three-force simulation value;

[0015] The working condition generating module is used to generate a target working condition according to the target acceleration and the target torque when the suspension three-force test value matches the suspension three-force simulation value.

[0016] In a third aspect, an embodiment of the present application further provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the above-mentioned method for generating suspension strength verification conditions when executed by the processor.

[0017] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method for generating the suspension strength verification condition is implemented.

[0018] The embodiments of the present application include at least the following technical effects:

[0019] The technical solution of the embodiment of the present application is to obtain the corresponding relationship between the acceleration and torque of the center of mass of the powertrain corresponding to the target vehicle model and the three-force suspension force by creating a suspension parameter relationship model corresponding to the target vehicle model, and to infer the target acceleration and target torque corresponding to the three-force suspension test value according to the suspension parameter relationship model and the pre-acquired three-force suspension test value, and then obtain the three-force suspension simulation value by inputting the target acceleration and target torque into the suspension simulation model, and generate the target working condition according to the target acceleration and target torque when the three-force suspension test value and the three-force suspension simulation value match. The present application realizes inferring the target acceleration and target torque loaded to the center of mass of the powertrain corresponding to the three-force suspension test value through the suspension parameter relationship model, and obtaining the target working condition based on the target acceleration and target torque, so that for different vehicle models, according to the three-force suspension test values ​​corresponding to the vehicle models in different applicable scenarios, the target acceleration and target torque corresponding to each applicable scenario can be obtained, so as to obtain the working condition matching the vehicle model for the suspension strength verification, which can solve the problem of insufficient verification or excessive verification caused by using general working conditions for verification. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below.

[0021] Figure 1 It is one of the flow charts of the method for generating the suspension strength verification condition provided in the embodiment of the present application;

[0022] Figure 2 This is the second flow chart of the method for generating the suspension strength verification working condition provided in the embodiment of the present application;

[0023] Figure 3 It is a structural schematic diagram of a device for generating suspension strength verification conditions provided in an embodiment of the present application;

[0024] Figure 4 A block diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0026] It should be understood that the references to "one embodiment" or "an embodiment" throughout the specification mean that the specific features, structures, or characteristics associated with the embodiment are included in at least one embodiment of the present application. Therefore, the references to "in one embodiment" or "in an embodiment" appearing throughout the specification do not necessarily refer to the same embodiment. In addition, these specific features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0027] In the various embodiments of the present application, it should be understood that the size of the serial numbers of the following processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0028] In the prior art, in order to verify the strength of the suspension, the acceleration and torque corresponding to the 28 common working conditions in the industry are mainly obtained as test data, loaded to the center of mass of the powertrain, and the stress of the suspension is obtained to verify the strength of the suspension under these 28 working conditions.

[0029] However, considering the different positioning of various vehicle models, there are great differences in the applicable working conditions. If the strength of the suspension is verified through the universal 28 working conditions, there will be problems of insufficient verification or over-verification.

[0030] Therefore, the inventor proposes a solution for generating a suspension strength verification condition. The technical solution of the present application is described below through specific embodiments.

[0031] The present application embodiment provides a method for generating a suspension strength verification condition, such as Figure 1 As shown, the method includes:

[0032] Step 101: Based on the suspension simulation model corresponding to the target vehicle model, a suspension parameter relationship model corresponding to the target vehicle model is created, wherein the suspension parameter relationship model includes the corresponding relationship between the powertrain center of mass acceleration and torque corresponding to the target vehicle model and the three-part suspension force.

[0033] Specifically, when the suspension strength verification is required for a target vehicle model, a suspension strength verification condition matching the target vehicle model needs to be generated. The number and type of suspension strength verification conditions corresponding to different vehicle models are different.

[0034] In the embodiment of the present application, for different models, considering their different positioning, the design of the suspension will also be very different. For example, for luxury models, the suspension will be designed to be softer to better filter the vibration from the engine and the road. For economical and practical models, the design of the suspension will try to use simple structures and low-cost materials under the premise of meeting basic performance requirements. For sports models, the suspension is usually designed to be relatively hard to provide better powertrain support and reduce the shaking of the powertrain during intense driving. Therefore, for different models, it is necessary to first build a matching suspension simulation model based on the characteristics that the suspension installed by the model needs to have. Specifically, the input variables of the suspension simulation model are the acceleration and torque added to the center of mass of the powertrain, and the output variables are the three-part force of the suspension. The three-part force of the suspension here includes the force generated by the powertrain in the longitudinal direction (i.e., the X direction) after the acceleration and torque are loaded, the force generated by the suspension in the lateral direction (i.e., the Y direction), and the force generated by the suspension in the vertical direction (i.e., the Z direction).

[0035] The embodiment of the present application can create a suspension parameter relationship model corresponding to the target vehicle model based on the suspension simulation model corresponding to the target vehicle model, and the suspension parameter relationship model includes the corresponding relationship between the powertrain center of mass acceleration, torque and three-part suspension force corresponding to the target vehicle model.

[0036] Specifically, when creating a suspension parameter relationship model, multiple groups of acceleration and torque can be obtained by sampling, and then each group of acceleration and torque can be input into the suspension simulation model to obtain the corresponding suspension three-force, and then multiple groups of data can be obtained, each of which includes acceleration, torque and suspension three-force. Then, the machine learning algorithm is used to process these multiple groups of data to obtain a suspension parameter relationship model that can characterize the corresponding relationship between the powertrain center of mass acceleration, torque and suspension three-force.

[0037] Step 102 : Determine a target acceleration and a target torque corresponding to the suspension three-force test value according to the suspension parameter relationship model and the pre-acquired suspension three-force test value.

[0038] Specifically, when generating the suspension strength verification working conditions corresponding to the target vehicle model, it is necessary to obtain the corresponding suspension three-force test values ​​of the target vehicle model under various preset working conditions in advance through experiments. The preset working conditions here can be determined based on the driving scene determined by the positioning of the target vehicle model, and the types and numbers of preset working conditions corresponding to different vehicle models are different.

[0039] After obtaining the suspension parameter relationship model corresponding to the target vehicle model, the target acceleration and target torque corresponding to the suspension three-force test value are obtained according to the suspension parameter relationship model and the pre-acquired suspension three-force test value, that is, the acceleration and torque corresponding to the suspension three-force test value are inferred through the suspension parameter relationship model.

[0040] Step 103: input the target acceleration and target torque into the suspension simulation model to obtain the suspension three-force simulation value.

[0041] After obtaining the target acceleration and target torque, the target acceleration and target torque can be input as input variables into the suspension simulation model corresponding to the target vehicle model to obtain the suspension three-part force output by the suspension simulation model, and the suspension three-part force is determined as the suspension three-part force simulation value.

[0042] Step 104 : When the suspension three-force test value and the suspension three-force simulation value match, generate a target operating condition according to the target acceleration and the target torque.

[0043] Among them, after obtaining the suspension three-force simulation value, in order to verify whether the suspension three-force simulation value conforms to the actual working condition, the following steps need to be performed:

[0044] Obtaining a deviation value between the suspension three-force simulation value and the suspension three-force test value;

[0045] When the deviation value is less than a preset threshold, determining that the suspension three-force test value matches the suspension three-force simulation value;

[0046] When the deviation value is greater than or equal to the preset threshold, it is determined that the suspension three-force test value and the suspension three-force simulation value do not match.

[0047] The process of judging whether the suspension three-force simulation value and the suspension three-force test value match is specifically to obtain the deviation value of the suspension three-force test value and the suspension three-force simulation value, and then compare the deviation value with the preset threshold value to determine whether the suspension three-force simulation value and the suspension three-force test value match. Wherein, when the deviation value is less than the preset threshold value, it is determined that the suspension three-force test value and the suspension three-force simulation value match, indicating that the reverse thrust result, i.e., the target acceleration and target torque, are close to the actual working condition. When the deviation value is greater than or equal to the preset threshold value, it is determined that the suspension three-force test value and the suspension three-force simulation value do not match, indicating that the reverse thrust result, i.e., the target acceleration and target torque, are far from the actual working condition. Wherein, the deviation value can be a deviation degree, the deviation degree can be a ratio of the deviation value to the suspension three-force test value, and the preset threshold value can be a value pre-calibrated by the vehicle manufacturer. In the embodiment of the present application, the preset threshold value is not specifically limited. For example, the preset threshold value can be 10%, 15% or 5%, etc.

[0048] Among them, when the suspension three-force test value and the suspension three-force simulation value match, it shows that the target acceleration and target torque inferred by the suspension parameter relationship model are consistent with the actual working condition, with high reliability, and that the suspension parameter relationship model has good adaptability. At this time, a target working condition can be generated based on the target acceleration and target torque, and the target working condition can be used as a suspension strength verification working condition.

[0049] In addition, when the three-force test value of the suspension and the three-force simulation value of the suspension do not match, it indicates that the target acceleration and target torque inferred by the suspension parameter relationship model do not match the actual working conditions, and the reliability is low. It also indicates that the adaptability of the suspension parameter relationship model is poor. At this time, the working conditions cannot be generated based on the target acceleration and target torque, and the suspension parameter relationship model needs to be recreated.

[0050] Specifically, for each target vehicle model, a set of suspension strength verification conditions corresponding to the target vehicle model may be set. Then, after generating the target conditions, the following steps are performed:

[0051] The target working condition is added to the suspension strength verification working condition set corresponding to the target vehicle model.

[0052] Specifically, when the target working condition corresponding to the suspension three-force test value is obtained, the target working condition is added to the suspension strength verification condition set. Since the suspension three-force test value is determined based on the driving scene determined by the positioning of the target vehicle model, by obtaining the target working condition corresponding to each suspension three-force test value corresponding to the target vehicle model and adding it to the suspension strength verification condition set corresponding to the target vehicle model, each vehicle model can have a matching suspension strength verification condition set, and by continuously adding new target working conditions, the suspension strength verification condition set can be made richer and more comprehensive. Since different working conditions represent different stress conditions that the powertrain may face under various actual use conditions, incorporating the target working condition into the suspension strength verification condition set can ensure that the performance of the suspension in more different scenarios is verified, reducing the risk of potential problems caused by incomplete verification being discovered in actual use.

[0053] In an embodiment of the present application, a suspension parameter relationship model corresponding to a target vehicle model is created to obtain a corresponding relationship between the acceleration and torque of the center of mass of the powertrain corresponding to the target vehicle model and the three-force suspension test value. According to the suspension parameter relationship model and the pre-acquired three-force suspension test value, the target acceleration and target torque corresponding to the three-force suspension test value are inferred. Then, the three-force suspension simulation value is obtained by inputting the target acceleration and target torque into the suspension simulation model. When the three-force suspension test value and the three-force suspension simulation value match, the target working condition is generated according to the target acceleration and target torque. The present application realizes inferring the target acceleration and target torque loaded to the center of mass of the powertrain corresponding to the three-force suspension test value through the suspension parameter relationship model, and obtaining the target working condition based on the target acceleration and target torque. Therefore, for different vehicle models, according to the three-force suspension test values ​​corresponding to the vehicle models in different applicable scenarios, the target acceleration and target torque corresponding to each applicable scenario can be obtained, thereby obtaining the working condition matching the vehicle model for the suspension strength verification, which can solve the problem of insufficient verification or excessive verification caused by using general working conditions for verification.

[0054] The following describes how to create a suspension parameter relationship model based on the suspension simulation model in step 101. As an optional embodiment, creating a suspension parameter relationship model corresponding to a target vehicle model based on the suspension simulation model corresponding to the target vehicle model includes:

[0055] Based on the suspension simulation model, sampling is performed within a preset acceleration and torque range to obtain a sample data set;

[0056] The sample data set is processed using a machine learning algorithm to obtain the suspension parameter relationship model.

[0057] In this embodiment, the input variables of the suspension simulation model are the acceleration and torque loaded to the center of mass of the powertrain, and the output variable is the three-component suspension force.

[0058] For the target vehicle model, the range boundaries of the acceleration and torque loaded to the center of mass of the powertrain can be determined based on the driving data of the target vehicle model in various applicable driving scenarios, and then the preset acceleration and torque range can be determined.

[0059] Specifically, when creating a suspension parameter relationship model, you can first sample within the preset acceleration and torque range corresponding to the target vehicle model based on the suspension simulation model to obtain a sample data set. Among them, Latin hypercube sampling can be used when sampling to obtain multiple DOE (Design of Experiments) sample points, that is, a sample data set. Latin hypercube sampling is a stratified sampling method that can divide the value range of the input variable into several intervals of equal probability, and then randomly select a sample point in each interval. This ensures that the sample points are evenly distributed within the value range of the input variable, thereby improving the efficiency and accuracy of the experimental design.

[0060] The sample data set is processed using a machine learning algorithm to obtain the suspension parameter relationship model, including:

[0061] Processing the sample data set to obtain an input data table and an output data table;

[0062] The machine learning algorithm is used to perform model learning on the input data table and the output data table to obtain the suspension parameter relationship model.

[0063] In this embodiment, a processing tool (such as a pre-processing tool for automatically generating powertrain suspension DOE working conditions and a post-processing tool for automatically extracting the three-dimensional force of the suspension) can be developed in advance, and then the sample data set is processed by the processing tool to obtain an input data table and an output data table. The input data table includes the acceleration and torque of the center of mass of the powertrain, and the output data table includes the three-dimensional force of the suspension.

[0064] After obtaining the input data table and the output data table, a machine learning algorithm is used to perform model learning on the input data table and the output data table to obtain a suspension parameter relationship model. Specifically, the machine learning algorithm may be a radial basis function (RBF) algorithm, and the type of the suspension parameter relationship model may be a response surface methodology (RSM) model. Then, when creating the suspension parameter relationship model, the radial basis function algorithm may be used to seek the relationship between the powertrain center of mass acceleration and torque and the three-part suspension force and establish a response surface model.

[0065] The above implementation scheme of the present application obtains a sample data set by sampling within a preset acceleration and torque range based on a suspension simulation model, and processes the sample data set using a machine learning algorithm to obtain a suspension parameter relationship model, thereby realizing the conversion of the suspension simulation model to the suspension parameter relationship model, and facilitating the subsequent reverse deduction of the suspension three-force test value based on the suspension parameter relationship model to obtain the target acceleration and target torque, thereby realizing the correspondence between the simulation condition and the test condition, and reducing the risk of insufficient verification or over-verification during suspension strength verification.

[0066] The following describes how to infer the target acceleration and target torque corresponding to the suspension three-force test value through the suspension parameter relationship model. As an optional embodiment, according to the suspension parameter relationship model and the pre-acquired suspension three-force test value, the target acceleration and target torque corresponding to the suspension three-force test value are determined, including:

[0067] Determine the target optimization algorithm;

[0068] The target acceleration and target torque matching the suspension three-force test value in the suspension parameter relationship model are determined by running the target optimization algorithm.

[0069] When inferring the target acceleration and target torque, what is actually needed is to find a set of acceleration and torque values ​​so that when they are substituted into the suspension parameter relationship model, a suspension three-force that is as close as possible to the suspension three-force test value can be calculated.

[0070] First, you need to determine the target optimization algorithm. Common optimization algorithms include genetic algorithms, particle swarm optimization algorithms, simulated annealing algorithms, etc. These algorithms can be used to find the optimal solution.

[0071] Then, the target acceleration and target torque matching the three-force test value of the suspension are determined by running the target optimization algorithm.

[0072] Wherein, determining the target acceleration and target torque matching the suspension three-force test value in the suspension parameter relationship model by running the target optimization algorithm includes:

[0073] Determining an objective function and an objective function value corresponding to the suspension parameter relationship model, wherein the objective function is used to characterize the difference between a suspension three-force calculation value obtained by the suspension parameter relationship model and a suspension three-force test value;

[0074] The target optimization algorithm is run, and when the function value corresponding to the target function reaches the target function value, the acceleration corresponding to the function value is determined as the target acceleration, and the torque corresponding to the function value is determined as the target torque.

[0075] Specifically, when running the target optimization algorithm to determine the target acceleration and target torque, it is also necessary to determine the target function and target function value corresponding to the suspension parameter relationship model. The target function is used to characterize the difference between the suspension three-force calculation value obtained by the suspension parameter relationship model and the suspension three-force test value. The smaller the function value corresponding to the target function, the higher the accuracy of the suspension parameter relationship model. The target function value is the optimal value of the target function.

[0076] When running the target optimization algorithm, a set of initial solutions, i.e., a set of acceleration and torque, will be randomly generated, and the function value corresponding to the initial solution will be calculated. In the subsequent iterative optimization process, for each generation of solutions, the function value of the objective function is calculated, i.e., the difference between the calculated value of the suspension three-force and the test value of the suspension three-force. Then, according to the update rule of the target optimization algorithm, the next generation of solutions is generated, and the process is repeated until the function value of the objective function reaches the target function value. When the target optimization algorithm stops, the output is the optimal solution, i.e., the target acceleration and target torque.

[0077] The above implementation scheme of the present application determines the target acceleration and target torque that match the suspension three-force test value in the suspension parameter relationship model by running the target optimization algorithm, thereby providing a reliable basis for the subsequent generation of target working conditions and facilitating better evaluation of the reliability of the suspension.

[0078] The overall implementation process of the embodiment of the present application is introduced below. Figure 2 As shown, including:

[0079] Step 201 : Based on the suspension simulation model, sampling is performed within a preset acceleration and torque range to obtain a sample data set.

[0080] Step 202: Process the sample data set to obtain an input data table and an output data table.

[0081] Step 203: Use a machine learning algorithm to perform model learning on the input data table and the output data table to obtain a suspension parameter relationship model.

[0082] Step 204: Determine the target optimization algorithm.

[0083] Step 205 : Determine the target acceleration and target torque matching the suspension three-force test value in the suspension parameter relationship model by running the target optimization algorithm.

[0084] Step 206: Input the target acceleration and target torque into the suspension simulation model to obtain the suspension three-force simulation value.

[0085] Step 207, obtaining a deviation value between the suspension three-force simulation value and the suspension three-force test value;

[0086] Step 208 , determine whether the deviation value is less than a preset threshold value, if so, execute step 209 , otherwise execute step 201 .

[0087] Step 209: Generate a target operating condition according to the target acceleration and the target torque.

[0088] Step 210: Add the target working condition to a set of suspension strength verification working conditions corresponding to the target vehicle model.

[0089] In the above-mentioned embodiment of the present application, by creating a suspension parameter relationship model corresponding to the target vehicle model, the corresponding relationship between the acceleration and torque of the center of mass of the powertrain corresponding to the target vehicle model and the three-force suspension test value is obtained. According to the suspension parameter relationship model and the pre-acquired three-force suspension test value, the target acceleration and target torque corresponding to the three-force suspension test value are inversely deduced, and then the three-force suspension simulation value is obtained by inputting the target acceleration and target torque into the suspension simulation model, and the target working condition is generated according to the target acceleration and target torque when the three-force suspension test value and the three-force suspension simulation value match. The present application realizes the inverse deduction of the target acceleration and target torque loaded to the center of mass of the powertrain corresponding to the three-force suspension test value through the suspension parameter relationship model, and obtains the target working condition based on the target acceleration and target torque, so that for different vehicle models, according to the three-force suspension test values ​​corresponding to the vehicle models in different applicable scenarios, the target acceleration and target torque corresponding to each applicable scenario can be obtained, so as to obtain the working condition matching the vehicle model for suspension strength verification, which can solve the problem of insufficient verification or excessive verification caused by using general working conditions for verification.

[0090] The above introduces the method for generating the suspension strength verification working condition provided by the embodiment of the present application. The following will introduce the device for generating the suspension strength verification working condition provided by the embodiment of the present application in conjunction with the accompanying drawings.

[0091] like Figure 3 As shown, an embodiment of the present invention further provides a device for generating a suspension strength verification condition, the device comprising:

[0092] A creation module 301 is used to create a suspension parameter relationship model corresponding to the target vehicle model based on the suspension simulation model corresponding to the target vehicle model, wherein the suspension parameter relationship model includes the corresponding relationship between the powertrain center of mass acceleration and torque corresponding to the target vehicle model and the three-part suspension force;

[0093] A first determination module 302 is used to determine a target acceleration and a target torque corresponding to the suspension three-force test value according to the suspension parameter relationship model and the pre-acquired suspension three-force test value;

[0094] A second determination module 303 is used to input the target acceleration and the target torque into the suspension simulation model to obtain a suspension three-force simulation value;

[0095] The working condition generating module 304 is used to generate a target working condition according to the target acceleration and the target torque when the suspension three-force test value matches the suspension three-force simulation value.

[0096] Optionally, create a module including:

[0097] A sampling submodule, used for sampling within a preset acceleration and torque range based on the suspension simulation model to obtain a sample data set;

[0098] The processing submodule is used to process the sample data set using a machine learning algorithm to obtain the suspension parameter relationship model.

[0099] Optionally, the processing submodule includes:

[0100] A first processing unit, used for processing the sample data set to obtain an input data table and an output data table;

[0101] The second processing unit is used to use the machine learning algorithm to perform model learning on the input data table and the output data table to obtain the suspension parameter relationship model.

[0102] Optionally, the first determining module includes:

[0103] A first determination submodule is used to determine a target optimization algorithm;

[0104] The second determination submodule is used to determine the target acceleration and target torque matching the suspension three-force test value in the suspension parameter relationship model by running the target optimization algorithm.

[0105] Optionally, the second determining submodule includes:

[0106] A first determination unit, configured to determine an objective function and an objective function value corresponding to the suspension parameter relationship model, wherein the objective function is used to characterize a difference between a suspension three-force calculation value obtained by the suspension parameter relationship model and a suspension three-force test value;

[0107] The second determination unit is used to run the target optimization algorithm, and when the function value corresponding to the target function reaches the target function value, determine the acceleration corresponding to the function value as the target acceleration, and determine the torque corresponding to the function value as the target torque.

[0108] Optionally, after obtaining the suspension three-force simulation value, the device further includes:

[0109] An acquisition module, used for acquiring a deviation value between the suspension three-force simulation value and the suspension three-force test value;

[0110] A third determination module, configured to determine that the suspension three-force test value matches the suspension three-force simulation value when the deviation value is less than a preset threshold;

[0111] The fourth determination module is used to determine that the suspension three-force test value and the suspension three-force simulation value do not match when the deviation value is greater than or equal to the preset threshold.

[0112] Optionally, after generating the target operating condition, the device includes:

[0113] An adding module is used to add the target working condition to the suspension strength verification working condition set corresponding to the target vehicle model.

[0114] The device for generating the suspension strength verification working condition provided by the present application creates a suspension parameter relationship model corresponding to the target vehicle model, obtains the corresponding relationship between the acceleration and torque of the center of mass of the powertrain corresponding to the target vehicle model and the three-force suspension test value, and inversely deduces the target acceleration and target torque corresponding to the three-force suspension test value according to the suspension parameter relationship model and the pre-acquired three-force suspension test value, and then obtains the three-force suspension simulation value by inputting the target acceleration and target torque into the suspension simulation model, and generates the target working condition according to the target acceleration and target torque when the three-force suspension test value and the three-force suspension simulation value match. The present application realizes inversely deduce the target acceleration and target torque loaded to the center of mass of the powertrain corresponding to the three-force suspension test value through the suspension parameter relationship model, and obtains the target working condition based on the target acceleration and target torque, so that for different vehicle models, according to the three-force suspension test values ​​corresponding to the vehicle models in different applicable scenarios, the target acceleration and target torque corresponding to each applicable scenario can be obtained, so as to obtain the working condition for performing suspension strength verification that matches the vehicle model, and can solve the problem of insufficient verification or excessive verification caused by using general working conditions for verification.

[0115] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0116] An embodiment of the present application also provides an electronic device, including: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the various processes of the above-mentioned method for generating suspension strength verification conditions are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0117] For example, Figure 4FIG. 1 shows a schematic diagram of the physical structure of an electronic device. Figure 4 As shown, the electronic device may include: a processor (processor) 410, a communication interface (Communications Interface) 420, a memory (memory) 430 and a communication bus 440, wherein the processor 410, the communication interface 420, and the memory 430 complete mutual communication through the communication bus 440. The processor 410 can call the logic instructions in the memory 430, and the processor 410 is used to perform the following steps: based on the suspension simulation model corresponding to the target vehicle model, create a suspension parameter relationship model corresponding to the target vehicle model, wherein the suspension parameter relationship model includes the corresponding relationship between the acceleration and torque of the center of mass of the powertrain corresponding to the target vehicle model and the suspension three-force test value; determine the target acceleration and target torque corresponding to the suspension three-force test value according to the suspension parameter relationship model and the pre-acquired suspension three-force test value; input the target acceleration and the target torque into the suspension simulation model to obtain the suspension three-force simulation value; when the suspension three-force test value and the suspension three-force simulation value match, generate the target working condition according to the target acceleration and the target torque. The processor 410 may also execute other solutions in the embodiments of the present application, which will not be further elaborated here.

[0118] In addition, the logic instructions in the above-mentioned memory 430 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application.

[0119] The embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, each process of the above-mentioned method for generating the suspension strength verification working condition is implemented, and the same technical effect can be achieved. To avoid repetition, it is not repeated here. The computer-readable storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0120] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0121] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0122] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

[0123] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0124] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0125] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0126] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0127] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0128] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard drives, ROM, RAM, magnetic disks, or optical disks.

[0129] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for generating a suspension strength verification condition, characterized in that: The method comprises: Based on the suspension simulation model corresponding to the target vehicle model, a suspension parameter relationship model corresponding to the target vehicle model is created, wherein the suspension parameter relationship model includes the corresponding relationship between the powertrain center of mass acceleration and torque corresponding to the target vehicle model and the three-part suspension force; Determine a target acceleration and a target torque corresponding to the suspension three-force test value according to the suspension parameter relationship model and the pre-acquired suspension three-force test value; Inputting the target acceleration and the target torque into the suspension simulation model to obtain a suspension three-force simulation value; When the suspension three-force test value matches the suspension three-force simulation value, a target operating condition is generated according to the target acceleration and the target torque.

2. The method for generating suspension strength verification conditions according to claim 1, characterized in that: Based on the suspension simulation model corresponding to the target vehicle model, a suspension parameter relationship model corresponding to the target vehicle model is created, including: Based on the suspension simulation model, sampling is performed within a preset acceleration and torque range to obtain a sample data set; The sample data set is processed using a machine learning algorithm to obtain the suspension parameter relationship model.

3. The method for generating suspension strength verification conditions according to claim 2, characterized in that: The sample data set is processed using a machine learning algorithm to obtain the suspension parameter relationship model, including: Processing the sample data set to obtain an input data table and an output data table; The machine learning algorithm is used to perform model learning on the input data table and the output data table to obtain the suspension parameter relationship model.

4. The method for generating suspension strength verification conditions according to claim 1, characterized in that: Determining a target acceleration and a target torque corresponding to the suspension three-force test value according to the suspension parameter relationship model and the pre-acquired suspension three-force test value includes: Determine the target optimization algorithm; The target acceleration and target torque matching the suspension three-force test value in the suspension parameter relationship model are determined by running the target optimization algorithm.

5. The method for generating suspension strength verification conditions according to claim 1, characterized in that: Determining a target acceleration and a target torque matching the suspension three-force test value in the suspension parameter relationship model by running the target optimization algorithm includes: Determining an objective function and an objective function value corresponding to the suspension parameter relationship model, wherein the objective function is used to characterize the difference between a suspension three-force calculation value obtained by the suspension parameter relationship model and a suspension three-force test value; The target optimization algorithm is run, and when the function value corresponding to the target function reaches the target function value, the acceleration corresponding to the function value is determined as the target acceleration, and the torque corresponding to the function value is determined as the target torque.

6. The method for generating suspension strength verification conditions according to claim 1, characterized in that: After obtaining the suspension three-force simulation value, the method further includes: Obtaining a deviation value between the suspension three-force simulation value and the suspension three-force test value; When the deviation value is less than a preset threshold, determining that the suspension three-force test value matches the suspension three-force simulation value; When the deviation value is greater than or equal to the preset threshold, it is determined that the suspension three-force test value and the suspension three-force simulation value do not match.

7. The method for generating suspension strength verification conditions according to claim 1, characterized in that: After generating the target operating conditions, the method includes: The target working condition is added to the suspension strength verification working condition set corresponding to the target vehicle model.

8. A device for generating suspension strength verification conditions, characterized in that: include: A creation module, used to create a suspension parameter relationship model corresponding to the target vehicle model based on the suspension simulation model corresponding to the target vehicle model, wherein the suspension parameter relationship model includes the corresponding relationship between the powertrain center of mass acceleration and torque corresponding to the target vehicle model and the three-part suspension force; A first determination module, configured to determine a target acceleration and a target torque corresponding to the suspension three-force test value according to the suspension parameter relationship model and the pre-acquired suspension three-force test value; A second determination module is used to input the target acceleration and the target torque into the suspension simulation model to obtain a suspension three-force simulation value; The working condition generating module is used to generate a target working condition according to the target acceleration and the target torque when the suspension three-force test value matches the suspension three-force simulation value.

9. An electronic device, characterized in that: The method comprises a processor, a memory and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the method for generating the suspension strength verification condition as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for generating a suspension strength verification condition according to any one of claims 1 to 7 is implemented.

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