Suspension testing methods, devices, and computer equipment based on simulation technology

Abstract

The application relates to a suspension testing method, device and computer equipment based on simulation technology. The method comprises the following steps: obtaining suspension structure data, production process information and raw material information of each suspension structure of a produced vehicle suspension, identifying the production process and process parameter information of each suspension structure, generating a production digital model of each suspension structure, and predicting the range of each structure parameter of the suspension structure based on the raw material information of each suspension structure; based on the structure parameter range and the suspension structure data of each suspension structure, generating each sample structure model of each suspension structure, thereby generating each sample vehicle suspension model of the vehicle suspension; collecting simulation test process and environment requirement information corresponding to each suspension test scheme, so as to perform simulation test processing through each sample vehicle suspension model of the vehicle suspension, and obtain a suspension test result of the vehicle suspension. The method can improve the testing efficiency and accuracy of the produced vehicle suspension.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and in particular to a suspension testing method, apparatus, and computer equipment based on simulation technology. Background Technology

[0002] During vehicle suspension production, comprehensive testing of various suspension performance parameters is required to ensure the safety, stability, and comfort of the manufactured vehicle suspension. However, compared to traditional user-based vehicle suspension testing methods, manufacturing vehicle suspension testing requires a large amount of test data and a high testing frequency, often causing substantial damage to the suspension. Performing performance tests on individual vehicle suspensions consumes a significant amount of suspension material, leading to resource waste, high testing costs, and inefficient test results. Therefore, optimizing suspension resources and improving testing efficiency during manufacturing vehicle suspension testing is a current research focus.

[0003] Traditional methods for testing vehicle suspensions involve sampling the suspension components and then subjecting these samples to various tests. However, in actual testing, when the suspension suffers substantial damage, other performance tests are significantly affected. Testing only a subset of vehicle suspensions yields poor accuracy, while testing a large number of suspension components is time-consuming and costly, resulting in low efficiency for testing suspensions in production vehicles. Summary of the Invention

[0004] Therefore, it is necessary to provide a suspension testing method, apparatus, computer equipment, computer-readable storage medium, and computer program product based on simulation technology to address the above-mentioned technical problems.

[0005] Firstly, this application provides a suspension testing method based on simulation technology, including:

[0006] The system acquires suspension structure data, production process information, and raw material information of each suspension structure of the vehicle suspension, and identifies the production process and process parameter information of each suspension structure based on the production process information of each suspension structure.

[0007] Based on the production process of each suspension structure and the process parameter information of each suspension structure, a production digital model of each suspension structure is generated, and based on the raw material information of each suspension structure, the range of each structural parameter of the suspension structure is predicted through the production digital model.

[0008] Based on the structural parameter range of each suspension structure and the suspension structure data of each suspension structure, a sample structural model of each suspension structure is generated, and based on the sample structural models of each suspension structure, a sample vehicle suspension model of the vehicle suspension is generated.

[0009] The simulation test process corresponding to each suspension test scheme and the environmental requirement information corresponding to each suspension test scheme are collected. Based on the simulation test process corresponding to each suspension test scheme and the environmental requirement information corresponding to each suspension test scheme, simulation test processing is performed through each sample vehicle suspension model of the vehicle suspension to obtain the suspension test results of the vehicle suspension.

[0010] Optionally, the step of identifying the production process of each suspension structure and the process parameter information of each suspension structure based on the production process information of each suspension structure includes:

[0011] For each suspension structure, based on the production process information of the suspension structure, the stage production target, stage production method, and production parameter information of each production stage of the suspension structure are identified.

[0012] Based on the production targets and production methods of each production stage, the sub-production processes of each production stage are queried in the digital twin database, and the production control parameters of each production stage are identified based on the production parameter information of each production stage.

[0013] The sub-production processes of each production stage are used as the production process of the suspension structure, and the production control parameters of each production stage are used as the process parameter information of the suspension structure.

[0014] Optionally, generating a production digital model for each suspension structure based on the production process of each suspension structure and the process parameter information of each suspension structure includes:

[0015] The production structure model of each suspension structure is collected, and the production process of each suspension structure is parameterized to obtain the process design parameters of each suspension structure.

[0016] Based on the process parameter information and process design parameters of each suspension structure, a production digital model of each suspension structure is constructed through the production structure model.

[0017] Optionally, the step of predicting the range of structural parameters of each suspension structure based on the raw material information of each suspension structure using the production digital model includes:

[0018] For each suspension structure, based on the raw material information of the suspension structure, the material parameter range of each raw material type of the suspension structure is identified;

[0019] Based on the material parameter ranges of each of the aforementioned raw material types, the parameter ranges of each structural parameter type of the suspension structure are predicted using the production digital model, and the parameter range of each structural parameter type is used as the structural parameter range of the suspension structure.

[0020] Optionally, the step of generating sample structural models for each suspension structure based on the structural parameter range of each suspension structure and the data of each suspension structure, and generating sample vehicle suspension models for the vehicle suspension based on the sample structural models of each suspension structure, includes:

[0021] For each suspension structure, based on the range of structural parameters of the suspension structure, the values ​​of each structural parameter of the suspension structure are identified, and based on the values ​​of each structural parameter of each suspension structure and the suspension structure data of each suspension structure, sample structural models of each suspension structure are constructed respectively.

[0022] Based on the sample structural models of each suspension structure, a single variable method is used to generate structural combination strategies for each suspension structure. Based on the sample structural models of each suspension structure corresponding to each structural combination strategy, a sample vehicle suspension model of the vehicle suspension is constructed.

[0023] Optionally, based on the simulation test process corresponding to each suspension test scheme and the environmental requirement information corresponding to each suspension test scheme, the simulation test process is performed using each sample vehicle suspension model of the vehicle suspension to obtain the suspension test results of the vehicle suspension, including:

[0024] Based on the simulation test process, the simulation operation parameters of the vehicle suspension are identified, and based on each environmental requirement information, the environmental factor values ​​of each environmental factor type are identified.

[0025] Based on the simulation operation parameters of the vehicle suspension and the environmental factor values ​​of each environmental factor type, each simulation test strategy is generated. Based on each simulation test strategy, the suspension test data of each sample vehicle suspension model corresponding to each simulation test strategy of the vehicle suspension is obtained through simulation using each sample vehicle suspension model.

[0026] By employing a suspension test evaluation strategy, the suspension test data is processed to obtain test evaluation results for each suspension test data. Based on the test evaluation results of each sample vehicle suspension model corresponding to each simulation test strategy, the suspension test results of the vehicle suspension are identified.

[0027] Secondly, this application also provides a suspension testing device based on simulation technology, comprising:

[0028] The acquisition module is used to acquire suspension structure data of each suspension structure of the vehicle suspension, production process information of each suspension structure of the vehicle suspension, and raw material information of each suspension structure of the vehicle suspension, and based on the production process information of each suspension structure, to identify the production process of each suspension structure and the process parameter information of each suspension structure.

[0029] The prediction module is used to generate a production digital model for each suspension structure based on the production process of each suspension structure and the process parameter information of each suspension structure, and to predict the range of each structural parameter of the suspension structure based on the raw material information of each suspension structure through the production digital model.

[0030] The generation module is used to generate sample structural models for each suspension structure based on the structural parameter range and suspension structure data of each suspension structure, and to generate sample vehicle suspension models for the vehicle suspension based on the sample structural models of each suspension structure.

[0031] The simulation module is used to collect the simulation test process corresponding to each suspension test scheme and the environmental requirement information corresponding to each suspension test scheme. Based on the simulation test process and environmental requirement information corresponding to each suspension test scheme, the module performs simulation test processing through each sample vehicle suspension model of the vehicle suspension to obtain the suspension test results of the vehicle suspension.

[0032] Optionally, the acquisition module is specifically used for:

[0033] For each suspension structure, based on the production process information of the suspension structure, the stage production target, stage production method, and production parameter information of each production stage of the suspension structure are identified.

[0034] Based on the production targets and production methods of each production stage, the sub-production processes of each production stage are queried in the digital twin database, and the production control parameters of each production stage are identified based on the production parameter information of each production stage.

[0035] The sub-production processes of each production stage are used as the production process of the suspension structure, and the production control parameters of each production stage are used as the process parameter information of the suspension structure.

[0036] Optionally, the prediction module is specifically used for:

[0037] The production structure model of each suspension structure is collected, and the production process of each suspension structure is parameterized to obtain the process design parameters of each suspension structure.

[0038] Based on the process parameter information and process design parameters of each suspension structure, a production digital model of each suspension structure is constructed through the production structure model.

[0039] Optionally, the prediction module is specifically used for:

[0040] For each suspension structure, based on the raw material information of the suspension structure, the material parameter range of each raw material type of the suspension structure is identified;

[0041] Based on the material parameter ranges of each of the aforementioned raw material types, the parameter ranges of each structural parameter type of the suspension structure are predicted using the production digital model, and the parameter range of each structural parameter type is used as the structural parameter range of the suspension structure.

[0042] Optionally, the generation module is specifically used for:

[0043] For each suspension structure, based on the range of structural parameters of the suspension structure, the values ​​of each structural parameter of the suspension structure are identified, and based on the values ​​of each structural parameter of each suspension structure and the suspension structure data of each suspension structure, sample structural models of each suspension structure are constructed respectively.

[0044] Based on the sample structural models of each suspension structure, a single variable method is used to generate structural combination strategies for each suspension structure. Based on the sample structural models of each suspension structure corresponding to each structural combination strategy, a sample vehicle suspension model of the vehicle suspension is constructed.

[0045] Optionally, the simulation module is specifically used for:

[0046] Based on the simulation test process, the simulation operation parameters of the vehicle suspension are identified, and based on each environmental requirement information, the environmental factor values ​​of each environmental factor type are identified.

[0047] Based on the simulation operation parameters of the vehicle suspension and the environmental factor values ​​of each environmental factor type, each simulation test strategy is generated. Based on each simulation test strategy, the suspension test data of each sample vehicle suspension model corresponding to each simulation test strategy of the vehicle suspension is obtained through simulation using each sample vehicle suspension model.

[0048] By employing a suspension test evaluation strategy, the suspension test data is processed to obtain test evaluation results for each suspension test data. Based on the test evaluation results of each sample vehicle suspension model corresponding to each simulation test strategy, the suspension test results of the vehicle suspension are identified.

[0049] Thirdly, this application provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method described in any one of the first aspects.

[0050] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the method described in any one of the first aspects.

[0051] Fifthly, this application provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps of the method described in any one of the first aspects.

[0052] The aforementioned suspension testing method, apparatus, and computer equipment based on simulation technology acquire suspension structure data, production process information, and raw material information for each suspension structure of the manufactured vehicle suspension. Based on the production process information, they identify the production process and process parameter information for each suspension structure. Based on the production process and process parameter information, they generate a production digital model for each suspension structure. Finally, based on the raw material information, they use the production digital model to predict the... This method describes the range of structural parameters for each suspension structure; based on the range of structural parameters and the suspension structure data for each suspension structure, it generates sample structural models for each suspension structure, and based on these sample structural models, it generates sample vehicle suspension models for the vehicle suspension; it collects the simulation test process and environmental requirements information corresponding to each suspension test scheme, and based on these information, it performs simulation test processing using the sample vehicle suspension models to obtain the suspension test results. This method also constructs a production digital model for each suspension structure based on its production process and raw material information, and then uses digital twin technology to simulate the production process of each suspension structure, thereby obtaining the range of structural parameters for each suspension structure and constructing sample structural models for each suspension structure. The above-described solution avoids the cost and structural losses associated with manual inspection of the actual structural parameters of the suspension structure, as well as the losses incurred during manual testing. Furthermore, the range of structural parameters for each suspension structure obtained through digital twins improves the comprehensiveness of identifying the range of structural parameters for suspension structures produced using current production methods. This approach also avoids the problem of partial identification of the range of structural parameters caused by structural production deviations or sampling limitations during actual sampling. Then, this solution constructs suspension structure models based on the identified range of structural parameters for each suspension structure, and subsequently constructs sample vehicle suspension models for each vehicle suspension. This generates multiple sample vehicle suspension models for different vehicle suspensions, avoiding the problems of small sample data volume and partial sample data, thereby comprehensively improving the comprehensiveness of vehicle suspension testing during actual testing. Then, in actual testing, this solution constructs multiple simulation testing strategies by combining the testing plan and environmental requirements, thereby conducting simulation tests on each sample vehicle suspension model. This avoids the problem that when the vehicle suspension is substantially damaged during physical testing, other performance tests of the vehicle suspension will be significantly affected, thus improving the accuracy of vehicle suspension testing.In summary, this solution improves both the comprehensiveness of the sample vehicle suspension models for each suspension structure and the quantity of sample vehicle suspension models required. Furthermore, by combining simulation testing methods with environmental requirements, this solution conducts simulation tests on each sample vehicle suspension model, improving the accuracy and comprehensiveness of the tests, thereby comprehensively enhancing the testing efficiency and accuracy of production vehicle suspensions. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 This is a flowchart illustrating a suspension testing method based on simulation technology in one embodiment;

[0055] Figure 2 This is a flowchart illustrating a suspension test example based on simulation technology in one embodiment;

[0056] Figure 3 This is a structural block diagram of a suspension testing device based on simulation technology in one embodiment;

[0057] Figure 4 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0059] The suspension testing method based on simulation technology provided in this application embodiment can be applied to a simulation-based suspension testing environment. This method can be applied to a terminal, a server, or a system including both a terminal and a server, and is implemented through interaction between the terminal and the server. The terminal can be, but is not limited to, various personal computers, laptops, mid-range computers, etc. The terminal constructs a production digital model of each suspension structure based on the production process and raw material information during production. Then, using digital twin technology, it simulates the production process of each suspension structure to obtain the structural parameter range of each suspension structure, thereby constructing sample structural models for each suspension structure. This approach avoids the cost and structural losses associated with manual inspection of the actual structural parameters of the suspension structure, and the structural losses associated with manual testing. Furthermore, the structural parameter ranges of each suspension structure obtained through digital twins improve the comprehensiveness of identifying the structural parameter ranges of suspension structures produced using current production methods. Moreover, this method avoids the problem of one-sided identification of the structural parameter ranges of suspension structures caused by structural production deviations during actual production or sampling limitations during actual sampling. Then, this solution constructs suspension structure models based on the identified range of structural parameters for each suspension structure, and then constructs sample vehicle suspension models for each vehicle suspension. This generates multiple sample vehicle suspension models for different vehicle suspensions, avoiding the problems of small sample data volume and partial sample data, thereby comprehensively improving the comprehensiveness of vehicle suspension testing during actual testing. Furthermore, during actual testing, this solution constructs multiple simulation testing strategies by combining the testing plan and environmental requirements, and conducts simulation tests on each sample vehicle suspension model. This avoids the problem that when the vehicle suspension is substantially damaged during physical testing, other performance tests of the vehicle suspension will be significantly affected, thus improving the accuracy of vehicle suspension testing. In summary, this solution improves both the comprehensiveness and quantity of sample vehicle suspension models constructed for each suspension structure. Moreover, by combining simulation testing plans with environmental requirements, this solution conducts simulation tests on each sample vehicle suspension model, improving the accuracy and comprehensiveness of testing, thereby comprehensively improving the testing efficiency and accuracy of production vehicle suspensions.

[0060] In one exemplary embodiment, such as Figure 1 As shown, a suspension testing method based on simulation technology is provided. Taking the application of this method to a terminal as an example, the method includes the following steps S101 to S104.

[0061] in:

[0062] Step S101: Obtain the suspension structure data of each suspension structure of the vehicle suspension, the production process information of each suspension structure of the vehicle suspension, and the raw material information of each suspension structure of the vehicle suspension. Based on the production process information of each suspension structure, identify the production process of each suspension structure and the process parameter information of each suspension structure.

[0063] In this embodiment, the terminal uses a 3D scanning device to perform 3D structural scanning processing on each suspension structure of the vehicle suspension, obtaining suspension structure data for each suspension structure. This suspension structure includes, but is not limited to, elastic elements, shock absorbers, guiding mechanisms, and other auxiliary components. Then, the terminal acquires the production process information for each suspension structure. This production process information corresponds to the production process of each sub-production line within the vehicle suspension production line. The production process includes the stage production targets, production methods, and production parameters for each production stage of each suspension structure. These production parameters are adjustable parameters for each production stage during production on the sub-production line, such as the casting temperature range, welding times, heating time, and heating temperature—adjustable parameters that can be manually or mechanically controlled. Next, the terminal acquires the raw material information for each suspension structure before production. This raw material information includes the material parameter ranges for each type of raw material used in the suspension structure, such as the carbon content range of steel and the ore quality of iron ore. Finally, based on the production process information for each suspension structure, the terminal identifies the production process and process parameter information for each suspension structure. The specific identification process will be explained in detail later.

[0064] Step S102: Based on the production process of each suspension structure and the process parameter information of each suspension structure, generate a production digital model for each suspension structure, and based on the raw material information of each suspension structure, predict the range of each structural parameter of the suspension structure through the production digital model.

[0065] In this embodiment, the terminal generates a production digital model for each suspension structure based on the production process and process parameter information of each suspension structure. Then, based on the raw material information of each suspension structure, the terminal predicts the range of structural parameters of each suspension structure using the production digital model. The specific generation process will be explained in detail later.

[0066] Step S103: Based on the structural parameter range of each suspension structure and the suspension structure data of each suspension structure, generate sample structural models for each suspension structure, and based on the sample structural models of each suspension structure, generate sample vehicle suspension models for each vehicle suspension.

[0067] In this embodiment, the terminal generates sample structural models for each suspension structure based on the structural parameter range and suspension structure data of each suspension structure, and then generates sample vehicle suspension models for the vehicle suspension based on these sample structural models. The sample structural models are generated by using a single-variable strategy to permutate, combine, and splice the sample structural models of each suspension structure to obtain the sample vehicle suspension models for the vehicle suspension.

[0068] Step S104: Collect the simulation test process and environmental requirements information corresponding to each suspension test scheme. Based on the simulation test process and environmental requirements information corresponding to each suspension test scheme, perform simulation test processing through each sample vehicle suspension model to obtain the suspension test results of the vehicle suspension.

[0069] In this embodiment, the terminal collects the simulation test process and environmental requirement information corresponding to each suspension test scheme. Based on the simulation test process and environmental requirement information for each suspension test scheme, simulation test processing is performed using sample vehicle suspension models to obtain the suspension test results. The simulation test process is a test procedure for simulating various performance aspects of the vehicle suspension. These performance aspects include, but are not limited to, vehicle height type, wheel alignment parameter type, wheel balance type, shock absorber performance type, spring performance type, structural wear type, suspension system rigidity type, suspension system durability type, ride comfort type, vibration damping effectiveness type, handling stability type, and vehicle posture control type. The environmental requirement information includes the environmental data range for each environmental type, which includes, but is not limited to, temperature type, humidity type, wind speed type, and road condition type.

[0070] Based on the above scheme, a production digital model of each suspension structure is constructed by analyzing the production process and raw material information during production. Then, digital twin technology is used to simulate the production process of each suspension structure, thereby obtaining the structural parameter range of each suspension structure and constructing sample structural models for each suspension structure. This scheme avoids the cost and structural losses associated with manual inspection of the actual structural parameters of the suspension structure, and the structural parameter ranges obtained through digital twins improve the comprehensiveness of identifying the structural parameter ranges of suspension structures produced using current production methods. Furthermore, this method avoids the problem of partial identification of the structural parameter ranges of suspension structures caused by structural production deviations or sampling limitations during actual sampling. Then, this scheme constructs suspension structure models based on the identified structural parameter ranges of each suspension structure, and then constructs sample vehicle suspension models for each vehicle suspension, thereby generating multiple sample vehicle suspension models for different vehicle suspensions. This avoids the problems of small sample data volume and partial sample data, thus comprehensively improving the comprehensiveness of vehicle suspension testing during actual testing. Then, in actual testing, this solution constructs multiple simulation testing strategies by combining the testing plan and environmental requirements. These strategies are then used to conduct simulation tests on each sample vehicle suspension model, avoiding the problem that physical testing, where actual damage to the vehicle suspension during testing would significantly impact other suspension performance tests. This improves the accuracy of vehicle suspension testing. In summary, this solution enhances both the comprehensiveness and quantity of sample vehicle suspension models for each suspension structure. Furthermore, by combining simulation testing plans with environmental requirements, this solution improves the accuracy and comprehensiveness of testing for each sample vehicle suspension model, thereby comprehensively improving the testing efficiency and accuracy of production vehicle suspensions.

[0071] Optionally, based on the production process information of each suspension structure, the production process and process parameter information of each suspension structure are identified, including: for each suspension structure, based on the production process information of the suspension structure, identifying the stage production target, stage production method, and production parameter information of each production stage; based on the stage production target and stage production method of each production stage, querying the sub-production process of each production stage in the digital twin database, and identifying the production control parameters of each production stage based on the production parameter information of each production stage; using the sub-production process of each production stage as the production process of the suspension structure, and using the production control parameters of each production stage as the process parameter information of the suspension structure.

[0072] In this embodiment, the terminal, for each suspension structure, identifies the stage production targets, stage production methods, and production parameter information for each production stage based on the suspension structure's production process information. Each production stage of different suspension structures can correspond to one or more stage production methods. Each stage production method characterizes a different production process for the suspension structure; for example, it could be a stage generation method corresponding to a pouring method or a cutting and grinding method for constructing the main body of the suspension structure.

[0073] Then, based on the production targets and methods of each production stage, the terminal queries the sub-production processes of each production stage in the digital twin database and identifies the production control parameters for each stage based on the production parameter information. The digital twin database stores the different generation methods and generation targets for different production stages, corresponding to the sub-production processes. Specifically, each sub-production process for each suspension structure corresponds to one production stage, one generation method, and one generation target. The production parameter information includes various control parameters used to regulate production.

[0074] Then, the terminal uses the sub-production processes of each production stage as the production process of the suspension structure, and uses the production control parameters of each production stage as the process parameter information of the suspension structure.

[0075] Based on the above scheme, by identifying the sub-production processes of each production stage of each suspension structure and the process parameter information of each suspension structure from the perspective of digital twin, the efficiency and accuracy of identifying the processes and parameters related to the digital twin of the suspension structure are improved.

[0076] Optionally, based on the production process of each suspension structure and the process parameter information of each suspension structure, a production digital model of each suspension structure is generated, including: collecting the production structure model of each suspension structure and parametric processing of the production process of each suspension structure to obtain the process design parameters of each suspension structure; and constructing the production digital model of each suspension structure based on the process parameter information and the process design parameters of each suspension structure through the production structure model.

[0077] In this embodiment, the terminal collects the production structure model of each suspension structure and performs parameterization processing on the production process of each suspension structure to obtain the process design parameters of each suspension structure. Specifically, the terminal presets a digital twin parameterization program and uses this program to parameterize the production process of the suspension structures to obtain the process design parameters of each suspension structure.

[0078] Next, based on the process parameter information and process design parameters of each suspension structure, the terminal constructs a production digital model for each suspension structure through a production structure model. This constructed production digital model is a digital twin model used to simulate the production process of the suspension structure.

[0079] Based on the above scheme, after parameterizing the production process of each suspension structure, the production process of the suspension structure is parameterized through a digital twin parameterization program to obtain the process design parameters of each suspension structure, thereby constructing a digital twin model of the suspension structure production process, which improves the construction efficiency and accuracy of the digital twin model.

[0080] Optionally, based on the raw material information of each suspension structure, a digital model is produced to predict the range of each structural parameter of the suspension structure, including: for each suspension structure, based on the raw material information of the suspension structure, identifying the material parameter range of each raw material type of the suspension structure; based on the material parameter range of each raw material type, a digital model is produced to predict the parameter range of each structural parameter type of the suspension structure, and the parameter range of each structural parameter type is used as the range of each structural parameter of the suspension structure.

[0081] In this embodiment, for each suspension structure, the terminal identifies the material parameter ranges for each raw material type based on the raw material information of the suspension structure. Then, based on the material parameter ranges for each raw material type, the terminal simulates the production process of the suspension structure using a production digital model to obtain the parameter ranges for each structural parameter type of the suspension structure. Finally, the terminal uses the parameter range of each structural parameter type as the structural parameter range for the entire suspension structure.

[0082] Based on the above scheme, the production process of the suspension structure is simulated by using the material parameter ranges of each type of raw material, thereby obtaining the parameter ranges of each structural parameter type of the suspension structure and improving the accuracy of the digital twin of the suspension structure production process.

[0083] Optionally, based on the structural parameter range of each suspension structure and the data of each suspension structure, sample structural models of each suspension structure are generated, and based on the sample structural models of each suspension structure, sample vehicle suspension models of the vehicle suspension are generated. This includes: for each suspension structure, identifying the values ​​of each structural parameter of the suspension structure based on the structural parameter range of the suspension structure, and constructing sample structural models of each suspension structure based on the values ​​of each structural parameter of each suspension structure and the data of each suspension structure; based on the sample structural models of each suspension structure, generating structural combination strategies for each suspension structure using a single variable method, and constructing sample vehicle suspension models of the vehicle suspension based on the sample structural models of each suspension structure corresponding to each structural combination strategy.

[0084] In this embodiment, for each suspension structure, the terminal identifies the values ​​of each structural parameter of the suspension structure based on the range of structural parameters of the suspension structure, and constructs sample structural models for each suspension structure based on the values ​​of each structural parameter and the suspension structure data of each suspension structure. The constructed sample structural models for each suspension structure are sample structural models corresponding to the structural parameter values ​​of different suspension structures.

[0085] Then, based on the sample structural models of each suspension structure, the terminal generates the structural combination strategies of each suspension structure through the single variable method, and constructs the sample vehicle suspension models of each suspension structure based on the sample structural models of each suspension structure corresponding to each structural combination strategy.

[0086] Based on the above scheme, after constructing sample structural models of each suspension structure by using the structural parameter values ​​of each suspension structure, the sample vehicle suspension models of the vehicle suspension are generated by a single variable, which improves the generation efficiency and comprehensiveness of the sample vehicle suspension models of the vehicle suspension.

[0087] Optionally, based on the simulation test process corresponding to each suspension test scheme and the environmental requirement information corresponding to each suspension test scheme, simulation test processing is performed using each sample vehicle suspension model to obtain the suspension test results of the vehicle suspension. This includes: identifying each simulation operation parameter of the vehicle suspension based on each simulation test process, and identifying each environmental factor value for each environmental factor type based on each environmental requirement information; generating each simulation test strategy based on each simulation operation parameter of the vehicle suspension and each environmental factor value for each environmental factor type, and simulating the suspension test data of each sample vehicle suspension model corresponding to each simulation test strategy based on each simulation test strategy; performing data evaluation processing on the suspension test data through a suspension test evaluation strategy to obtain the test evaluation results of each suspension test data, and identifying the suspension test results of the vehicle suspension based on the test evaluation results of each sample vehicle suspension model corresponding to each simulation test strategy.

[0088] In this embodiment, the terminal identifies various simulation operation parameters of the vehicle suspension based on each simulation test process, and identifies the values ​​of various environmental factors for each environmental factor type based on each environmental requirement information. Then, based on the various simulation operation parameters of the vehicle suspension and the values ​​of various environmental factors for each environmental factor type, the terminal generates environmental factor data for different environmental factor types or simulation test strategies for different simulation operation parameters using a single variable strategy. Next, based on each simulation test strategy, the terminal simulates and obtains suspension test data for each sample vehicle suspension model corresponding to each simulation test strategy using each sample vehicle suspension model. Each simulation test strategy includes different simulation operation parameters and environmental factor values, generated separately using a single variable strategy.

[0089] Then, the terminal performs data evaluation processing on the suspension test data through the suspension test evaluation strategy to obtain the test evaluation results for each suspension test data. Specifically, the suspension test evaluation strategy includes sub-suspension test evaluation strategies corresponding to different suspension test schemes, and each sub-suspension test evaluation strategy includes the evaluation values ​​of each test target of the suspension test scheme and the test data range corresponding to each evaluation value. Each suspension test data includes the test data of each test target of the suspension test scheme corresponding to that suspension test data.

[0090] Finally, the terminal identifies the vehicle suspension test results based on the test evaluation results of each sample vehicle suspension model corresponding to each simulation test strategy. Specifically, the terminal uses the test data of each test target corresponding to environmental data of different environment types for each suspension test scheme as the suspension test result of that vehicle suspension. Among them, the test data of each test target corresponding to environmental data of the same environment type for the same suspension test scheme is the average value of the test evaluation results of each suspension test data obtained after simulation testing of environmental data of the same environment type for the same suspension test scheme through each sample vehicle suspension model.

[0091] Based on the above scheme, by using the suspension test evaluation strategies corresponding to different suspension test schemes, the test data of each suspension is processed for evaluation, thereby identifying the evaluation values ​​of each test target corresponding to environmental data of each suspension test scheme in different environmental types, thus improving the comprehensiveness and accuracy of suspension test evaluation.

[0092] This application also provides a suspension test example based on simulation technology, such as Figure 2 As shown, the specific processing procedure includes the following steps:

[0093] Step S201: Obtain the suspension structure data of each suspension structure of the produced vehicle suspension, the production process information of each suspension structure of the vehicle suspension, and the raw material information of each suspension structure of the vehicle suspension.

[0094] Step S202: For each suspension structure, based on the production process information of the suspension structure, identify the stage production target, stage production method, and production parameter information of each production stage of the suspension structure.

[0095] Step S203: Based on the stage production targets and stage production methods of each production stage, query the sub-production processes of each production stage in the digital twin database, and identify the production control parameters of each production stage based on the production parameter information of each production stage.

[0096] Step S204: The sub-production processes of each production stage are taken as the production process of the suspension structure, and the production control parameters of each production stage are taken as the process parameter information of the suspension structure.

[0097] Step S205: Collect the production structure model of each suspension structure and parametrically process the production process of each suspension structure to obtain the process design parameters of each suspension structure.

[0098] Step S206: Based on the process parameter information and process design parameters of each suspension structure, a production digital model of each suspension structure is constructed through the production structure model.

[0099] Step S207: For each suspension structure, based on the raw material information of the suspension structure, identify the material parameter range of each raw material type of the suspension structure.

[0100] Step S208: Based on the material parameter range of each raw material type, the parameter range of each structural parameter type of the suspension structure is predicted by producing a digital model, and the parameter range of each structural parameter type is used as the structural parameter range of the suspension structure.

[0101] Step S209: For each suspension structure, based on the range of structural parameters of the suspension structure, identify the values ​​of each structural parameter of the suspension structure, and construct sample structural models for each suspension structure based on the values ​​of each structural parameter of each suspension structure and the suspension structure data of each suspension structure.

[0102] Step S210: Based on the sample structural models of each suspension structure, generate the structural combination strategies of each suspension structure using the single variable method, and construct the sample vehicle suspension models of each suspension structure based on the sample structural models of each suspension structure corresponding to each structural combination strategy.

[0103] Step S211: Collect the simulation test process and environmental requirements information corresponding to each suspension test scheme.

[0104] Step S212: Based on each simulation test process, identify each simulation operation parameter of the vehicle suspension, and based on each environmental requirement information, identify each environmental factor value for each type of environmental factor.

[0105] Step S213: Based on the simulation operation parameters of the vehicle suspension and the environmental factor values ​​of each environmental factor type, generate each simulation test strategy, and based on each simulation test strategy, simulate and obtain the suspension test data of each sample vehicle suspension model corresponding to each simulation test strategy of the vehicle suspension through each sample vehicle suspension model.

[0106] Step S214: Through the suspension test evaluation strategy, the suspension test data is processed to obtain the test evaluation results of each suspension test data. Based on the test evaluation results of each sample vehicle suspension model corresponding to each simulation test strategy, the suspension test results of the vehicle suspension are identified.

[0107] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0108] Based on the same inventive concept, this application also provides a simulation-based suspension testing device for implementing the simulation-based suspension testing method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations of one or more simulation-based suspension testing device embodiments provided below can be found in the limitations of the simulation-based suspension testing method described above, and will not be repeated here.

[0109] In one exemplary embodiment, such as Figure 3 As shown, a suspension testing device based on simulation technology is provided, including: an acquisition module 310, a prediction module 320, a generation module 330, and a simulation module 340, wherein:

[0110] The acquisition module 310 is used to acquire suspension structure data of each suspension structure of the vehicle suspension produced, production process information of each suspension structure of the vehicle suspension, and raw material information of each suspension structure of the vehicle suspension, and based on the production process information of each suspension structure, to identify the production process of each suspension structure and the process parameter information of each suspension structure.

[0111] The prediction module 320 is used to generate a production digital model for each suspension structure based on the production process of each suspension structure and the process parameter information of each suspension structure, and to predict the range of each structural parameter of the suspension structure based on the raw material information of each suspension structure through the production digital model.

[0112] The generation module 330 is used to generate sample structural models of each suspension structure based on the structural parameter range of each suspension structure and the suspension structure data of each suspension structure, and to generate sample vehicle suspension models of the vehicle suspension based on the sample structural models of each suspension structure.

[0113] The simulation module 340 is used to collect the simulation test process corresponding to each suspension test scheme and the environmental requirement information corresponding to each suspension test scheme. Based on the simulation test process and environmental requirement information corresponding to each suspension test scheme, the module performs simulation test processing through each sample vehicle suspension model of the vehicle suspension to obtain the suspension test results of the vehicle suspension.

[0114] Optionally, the acquisition module 310 is specifically used for:

[0115] For each suspension structure, based on the production process information of the suspension structure, the stage production target, stage production method, and production parameter information of each production stage of the suspension structure are identified.

[0116] Based on the production targets and production methods of each production stage, the sub-production processes of each production stage are queried in the digital twin database, and the production control parameters of each production stage are identified based on the production parameter information of each production stage.

[0117] The sub-production processes of each production stage are used as the production process of the suspension structure, and the production control parameters of each production stage are used as the process parameter information of the suspension structure.

[0118] Optionally, the prediction module 320 is specifically used for:

[0119] The production structure model of each suspension structure is collected, and the production process of each suspension structure is parameterized to obtain the process design parameters of each suspension structure.

[0120] Based on the process parameter information and process design parameters of each suspension structure, a production digital model of each suspension structure is constructed through the production structure model.

[0121] Optionally, the prediction module 320 is specifically used for:

[0122] For each suspension structure, based on the raw material information of the suspension structure, the material parameter range of each raw material type of the suspension structure is identified;

[0123] Based on the material parameter ranges of each of the aforementioned raw material types, the parameter ranges of each structural parameter type of the suspension structure are predicted using the production digital model, and the parameter range of each structural parameter type is used as the structural parameter range of the suspension structure.

[0124] Optionally, the generation module 330 is specifically used for:

[0125] For each suspension structure, based on the range of structural parameters of the suspension structure, the values ​​of each structural parameter of the suspension structure are identified, and based on the values ​​of each structural parameter of each suspension structure and the suspension structure data of each suspension structure, sample structural models of each suspension structure are constructed respectively.

[0126] Based on the sample structural models of each suspension structure, a single variable method is used to generate structural combination strategies for each suspension structure. Based on the sample structural models of each suspension structure corresponding to each structural combination strategy, a sample vehicle suspension model of the vehicle suspension is constructed.

[0127] Optionally, the simulation module 340 is specifically used for:

[0128] Based on the simulation test process, the simulation operation parameters of the vehicle suspension are identified, and based on each environmental requirement information, the environmental factor values ​​of each environmental factor type are identified.

[0129] Based on the simulation operation parameters of the vehicle suspension and the environmental factor values ​​of each environmental factor type, each simulation test strategy is generated. Based on each simulation test strategy, the suspension test data of each sample vehicle suspension model corresponding to each simulation test strategy of the vehicle suspension is obtained through simulation using each sample vehicle suspension model.

[0130] By employing a suspension test evaluation strategy, the suspension test data is processed to obtain test evaluation results for each suspension test data. Based on the test evaluation results of each sample vehicle suspension model corresponding to each simulation test strategy, the suspension test results of the vehicle suspension are identified.

[0131] The various modules in the aforementioned suspension testing device based on simulation technology can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0132] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 4As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a suspension testing method based on simulation technology. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0133] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0134] In one exemplary embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of a suspension testing method based on simulation technology.

[0135] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of a suspension testing method based on simulation technology.

[0136] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of a suspension testing method based on simulation technology.

[0137] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0138] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0139] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0140] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A suspension test method based on simulation technology, characterized by, The method comprises: acquiring suspension structure data of each suspension structure of a produced vehicle suspension, production process information of each suspension structure of the vehicle suspension, and raw material information of each suspension structure of the vehicle suspension, and identifying the production process of each suspension structure and process parameter information of each suspension structure based on the production process information of each suspension structure; generating a production digital model of each suspension structure based on the production process of each suspension structure and the process parameter information of each suspension structure, and predicting a range of structure parameters of each suspension structure based on the raw material information of each suspension structure through the production digital model; generating each sample structure model of each suspension structure based on the range of structure parameters of each suspension structure and the suspension structure data of each suspension structure, and generating each sample vehicle suspension model of the vehicle suspension based on each sample structure model of each suspension structure; collecting simulation test processes corresponding to each suspension test scheme and environment requirement information corresponding to each suspension test scheme; identifying each simulation running parameter of the vehicle suspension based on each simulation test process, and identifying each environment factor value of each environment factor type based on each environment requirement information; generating each simulation test strategy based on each simulation running parameter of the vehicle suspension and each environment factor value of each environment factor type, and simulating each sample vehicle suspension model corresponding to each simulation test strategy of the vehicle suspension based on each simulation test strategy to obtain suspension test data of each sample vehicle suspension model corresponding to each simulation test strategy of the vehicle suspension; the suspension test data comprises test data of each test target of the suspension test scheme corresponding to the suspension test data; performing data evaluation processing on the suspension test data through a suspension test evaluation strategy to obtain test evaluation results of each suspension test data, and identifying a suspension test result of the vehicle suspension based on the test evaluation results of each sample vehicle suspension model corresponding to each simulation test strategy; the test data of each test target corresponding to the environment data of the same environment type of the same suspension test scheme is the average value of the test evaluation results of each suspension test data obtained by simulating and testing each sample vehicle suspension model.

2. The method of claim 1, wherein, The method comprises: for each suspension structure, identifying stage production targets, stage production modes, and production parameter information of each production stage of the suspension structure based on the production process information of the suspension structure; querying sub-production processes of each production stage in a digital twin database based on the stage production targets and the stage production modes of each production stage, and identifying production control parameters of each production stage based on the production parameter information of each production stage; The sub-production processes of the production stages are taken as the production process of the suspension structure, and the production control parameters of the production stages are taken as the process parameter information of the suspension structure.

3. The method of claim 1, wherein, The production digital model of each suspension structure is generated based on the production process of each suspension structure and the process parameter information of each suspension structure, including: The production structure model of each suspension structure is collected, and the production process of each suspension structure is parameterized to obtain the process design parameters of each suspension structure; The production digital model of each suspension structure is constructed based on the process parameter information of each suspension structure and the process design parameters of each suspension structure through the production structure model.

4. The method of claim 1, wherein, The range of each structure parameter of the suspension structure is predicted based on the raw material information of each suspension structure through the production digital model, including: For each suspension structure, the range of material parameters of each raw material type of the suspension structure is identified based on the raw material information of the suspension structure; The range of each structure parameter type of the suspension structure is predicted based on the range of material parameters of each raw material type through the production digital model, and the range of each structure parameter type is taken as the range of each structure parameter of the suspension structure.

5. The method of claim 1, wherein, The sample structure model of each suspension structure is generated based on the range of each structure parameter of each suspension structure and the suspension structure data of each suspension structure, and the sample vehicle suspension model of the vehicle suspension is generated based on the sample structure model of each suspension structure, including: For each suspension structure, the value of each structure parameter of the suspension structure is identified based on the range of each structure parameter of the suspension structure, and the sample structure model of each suspension structure is constructed based on the value of each structure parameter of each suspension structure and the suspension structure data of each suspension structure; The structure combination strategy of each suspension structure is generated based on the sample structure model of each suspension structure through the single variable method strategy, and the sample vehicle suspension model of the vehicle suspension is constructed based on the sample structure model of each suspension structure corresponding to each structure combination strategy.

6. A suspension testing device based on simulation technology, characterized by The device comprises: An acquisition module is configured to acquire the suspension structure data of each suspension structure of a produced vehicle suspension, the production process information of each suspension structure of the vehicle suspension, and the raw material information of each suspension structure of the vehicle suspension, and identify the production process of each suspension structure and the process parameter information of each suspension structure based on the production process information of each suspension structure; A prediction module is configured to generate the production digital model of each suspension structure based on the production process of each suspension structure and the process parameter information of each suspension structure, and predict the range of each structure parameter of the suspension structure based on the raw material information of each suspension structure through the production digital model. The generating module is configured to generate a sample structure model of each suspension structure based on a structure parameter range of each suspension structure and suspension structure data of each suspension structure, and generate a sample vehicle suspension model of the vehicle suspension based on the sample structure model of each suspension structure. The simulation module is configured to collect simulation test processes corresponding to each suspension test scheme and environment requirement information corresponding to each suspension test scheme; identify simulation running parameters of the vehicle suspension based on the simulation test processes, and identify environment factor values of each environment factor type based on each environment requirement information; generate a simulation test strategy based on the simulation running parameters of the vehicle suspension and the environment factor values of each environment factor type, and simulate, based on the simulation test strategy, to obtain suspension test data of each sample vehicle suspension model corresponding to each simulation test strategy of the vehicle suspension based on the sample vehicle suspension model; the suspension test data includes test data of each test target of the suspension test scheme corresponding to the suspension test data; perform data evaluation processing on the suspension test data based on a suspension test evaluation strategy to obtain test evaluation results of each suspension test data, and identify a suspension test result of the vehicle suspension based on the test evaluation results of each sample vehicle suspension model corresponding to each simulation test strategy; the average of the test evaluation results of each suspension test data corresponding to the same environment data of the same environment type of the same suspension test scheme is obtained by simulating and testing each sample vehicle suspension model based on the same environment data of the same environment type of the same suspension test scheme.

7. The apparatus of claim 6, wherein, The acquisition module is specifically configured to: For each suspension structure, identify stage production targets of each production stage of the suspension structure, stage production modes of each production stage, and production parameter information of each production stage based on production process information of the suspension structure; query sub-production processes of each production stage in the digital twin database based on the stage production targets of each production stage and the stage production modes of each production stage, and identify production control parameters of each production stage based on the production parameter information of each production stage; use the sub-production processes of each production stage as the production process of the suspension structure, and use the production control parameters of each production stage as the process parameter information of the suspension structure. 8.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-7. The processor executes the computer program to implement the steps of the method of any one of claims 1 to 5.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 5.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 5.

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