Suspension test optimization method, apparatus, and computer device

Abstract

The application relates to a suspension test optimization method, device and computer equipment. The method comprises the following steps: obtaining vehicle suspension feedback information of a user vehicle and vehicle suspension structure detection information of the user vehicle, querying suspension abnormal information of the user vehicle in a suspension test database based on the vehicle suspension feedback information, identifying suspension structure abnormal information of the user vehicle based on the vehicle suspension structure detection information of the user vehicle, identifying a suspension abnormal structure area of the user vehicle based on the suspension structure abnormal information of the user vehicle, generating an initial suspension test scheme of the user vehicle based on the suspension abnormal information of the user vehicle, and performing test adjustment processing on the initial suspension test scheme based on the suspension structure abnormal information of the user vehicle and the suspension abnormal structure area of the user vehicle to obtain a target suspension test scheme of the user vehicle. The method can improve the detection efficiency of the vehicle suspension.

Description

Technical Field

[0001] This application relates to the field of intelligent suspension testing technology, and in particular to a suspension testing optimization method, apparatus, and computer equipment. Background Technology

[0002] Inspection of vehicle suspension systems can promptly detect and repair potential faults, such as broken springs and leaking shock absorbers. If these faults are not addressed in time, they can lead to abnormal vibrations, unstable handling, and even traffic accidents during driving. However, ensuring comprehensive inspection results in lengthy and complex testing methods for actual vehicle suspension systems, leading to significant resource costs for a complete system inspection. Therefore, optimizing the suspension testing process is a current research focus.

[0003] Traditional methods for optimizing vehicle suspension inspection involve subjective analysis of the vehicle's suspension condition and user feedback on suspension anomalies by human personnel. This analysis is then combined with appropriate testing equipment and instruments to comprehensively inspect the vehicle suspension. However, manual inspection is affected by work experience and the level of detail required for observation, resulting in a high error rate and a cumbersome and lengthy inspection process, thus leading to low efficiency in vehicle suspension inspection. Summary of the Invention

[0004] Therefore, it is necessary to provide a suspension testing optimization method, apparatus, computer equipment, computer-readable storage medium, and computer program product to address the aforementioned technical problems.

[0005] Firstly, this application provides a suspension testing optimization method, including:

[0006] Obtain vehicle suspension feedback information and vehicle suspension structure detection information of the user vehicle, and based on the vehicle suspension feedback information, query the suspension abnormal information of the user vehicle in the suspension test database.

[0007] Based on the vehicle suspension structure detection information of the user vehicle, identify the suspension structure abnormality information of the user vehicle, and based on the suspension structure abnormality information of the user vehicle, identify the suspension abnormal structure area of ​​the user vehicle.

[0008] Based on the suspension anomaly information of the user vehicle, an initial suspension test plan for the user vehicle is generated. Based on the suspension structure anomaly information and the suspension anomaly structure area of ​​the user vehicle, the initial suspension test plan is tested and adjusted to obtain the target suspension test plan for the user vehicle.

[0009] Optionally, the step of querying the suspension anomaly information of the user vehicle in the suspension test database based on the vehicle suspension feedback information includes:

[0010] Based on the vehicle suspension feedback information, a semantic recognition network is used to identify each suspension anomaly feature of the user's vehicle and the degree of suspension anomaly of each of the suspension anomaly features.

[0011] Based on the suspension anomaly information corresponding to each suspension anomaly feature of the user vehicle, query the initial suspension anomaly information of the user vehicle in the suspension anomaly database, and identify the anomaly degree range corresponding to each initial suspension anomaly information.

[0012] Based on the degree of suspension abnormality of each of the aforementioned suspension abnormality features and the range of abnormality corresponding to each of the aforementioned initial suspension abnormality information, the suspension abnormality information of the user vehicle is filtered from the aforementioned initial suspension abnormality information.

[0013] Optionally, identifying abnormal suspension structure information of the user vehicle based on the vehicle suspension structure detection information includes:

[0014] Based on the vehicle suspension structure detection information of the user vehicle, a three-dimensional structure model of the user vehicle suspension is generated, and the target three-dimensional structure model of the user vehicle suspension is queried in the database.

[0015] Based on the target vehicle suspension three-dimensional structural model, the structural difference range in the vehicle suspension three-dimensional structural model and the structural difference information corresponding to each structural difference range are identified, and the structural difference information corresponding to all structural difference ranges is used as the suspension structural anomaly information of the user vehicle.

[0016] Optionally, identifying the abnormal suspension structure region of the user vehicle based on the abnormal suspension structure information of the user vehicle includes:

[0017] The three-dimensional structural model of the user vehicle's suspension is divided into sub-three-dimensional structural models of each vehicle suspension structure. In each sub-three-dimensional structural model of the vehicle suspension structure, the unit structure range corresponding to each suspension unit of each vehicle suspension structure is identified by a three-dimensional structural recognition network.

[0018] For each structural difference range, based on the structural difference range and the unit structural range corresponding to each suspension unit of each vehicle suspension structure, the target unit structural range corresponding to the structural difference range is identified, and each target unit structural range corresponding to each structural difference range is taken as the suspension abnormal structure area of ​​the user vehicle.

[0019] Optionally, generating an initial suspension test plan for the user vehicle based on the suspension anomaly information of the user vehicle includes:

[0020] Identify the suspension anomaly type corresponding to the suspension anomaly information, and based on the suspension anomaly type corresponding to the suspension anomaly information, query the suspension test database for each vehicle test process corresponding to the suspension anomaly information, and the vehicle test target corresponding to each vehicle test process.

[0021] All vehicle testing procedures and the corresponding vehicle testing objectives are used as the initial suspension testing scheme for the user vehicle.

[0022] Optionally, the step of adjusting the initial suspension test plan based on the suspension structure anomaly information of the user vehicle and the abnormal suspension structure area of ​​the user vehicle to obtain the target suspension test plan for the user vehicle includes:

[0023] Identify the structural difference type corresponding to each of the aforementioned structural difference information, and based on the structural range of each target unit corresponding to each structural difference range and the structural difference type corresponding to the structural difference information of each structural difference range, identify the associated vehicle anomaly information caused by each structural difference range through the suspension structural anomaly identification network.

[0024] In the suspension test database, query the test process of each associated vehicle corresponding to the abnormal information of the associated vehicle, and the test target of each associated vehicle corresponding to the test process of each associated vehicle. Based on each vehicle test process and each associated vehicle test process, generate the target suspension test process of the user vehicle through process optimization processing strategy.

[0025] Based on the vehicle test objectives of each of the vehicle test processes and the associated vehicle test objectives corresponding to each of the associated vehicle test processes, a suspension test objective for the target suspension test process is generated, and the target suspension test process and the suspension test objective of the target suspension test process are used as the target suspension test scheme for the user vehicle.

[0026] Secondly, this application also provides a suspension testing optimization device, comprising:

[0027] The acquisition module is used to acquire vehicle suspension feedback information and vehicle suspension structure detection information of the user vehicle, and based on the vehicle suspension feedback information, query the suspension abnormal information of the user vehicle in the suspension test database.

[0028] The identification module is used to identify abnormal information of the suspension structure of the user vehicle based on the vehicle suspension structure detection information of the user vehicle, and to identify the abnormal suspension structure area of ​​the user vehicle based on the abnormal information of the suspension structure of the user vehicle.

[0029] The adjustment module is used to generate an initial suspension test plan for the user vehicle based on the suspension anomaly information of the user vehicle, and to perform test adjustment processing on the initial suspension test plan based on the suspension structure anomaly information of the user vehicle and the suspension anomaly structure area of ​​the user vehicle, so as to obtain the target suspension test plan for the user vehicle.

[0030] Optionally, the acquisition module is specifically used for:

[0031] Based on the vehicle suspension feedback information, a semantic recognition network is used to identify each suspension anomaly feature of the user's vehicle and the degree of suspension anomaly of each of the suspension anomaly features.

[0032] Based on the suspension anomaly information corresponding to each suspension anomaly feature of the user vehicle, query the initial suspension anomaly information of the user vehicle in the suspension anomaly database, and identify the anomaly degree range corresponding to each initial suspension anomaly information.

[0033] Based on the degree of suspension abnormality of each of the aforementioned suspension abnormality features and the range of abnormality corresponding to each of the aforementioned initial suspension abnormality information, the suspension abnormality information of the user vehicle is filtered from the aforementioned initial suspension abnormality information.

[0034] Optionally, the identification module is specifically used for:

[0035] Based on the vehicle suspension structure detection information of the user vehicle, a three-dimensional structure model of the user vehicle suspension is generated, and the target three-dimensional structure model of the user vehicle suspension is queried in the database.

[0036] Based on the target vehicle suspension three-dimensional structural model, the structural difference range in the vehicle suspension three-dimensional structural model and the structural difference information corresponding to each structural difference range are identified, and the structural difference information corresponding to all structural difference ranges is used as the suspension structural anomaly information of the user vehicle.

[0037] Optionally, the identification module is specifically used for:

[0038] The three-dimensional structural model of the user vehicle's suspension is divided into sub-three-dimensional structural models of each vehicle suspension structure. In each sub-three-dimensional structural model of the vehicle suspension structure, the unit structure range corresponding to each suspension unit of each vehicle suspension structure is identified by a three-dimensional structural recognition network.

[0039] For each structural difference range, based on the structural difference range and the unit structural range corresponding to each suspension unit of each vehicle suspension structure, the target unit structural range corresponding to the structural difference range is identified, and each target unit structural range corresponding to each structural difference range is taken as the suspension abnormal structure area of ​​the user vehicle.

[0040] Optionally, the adjustment module is specifically used for:

[0041] Identify the suspension anomaly type corresponding to the suspension anomaly information, and based on the suspension anomaly type corresponding to the suspension anomaly information, query the suspension test database for each vehicle test process corresponding to the suspension anomaly information, and the vehicle test target corresponding to each vehicle test process.

[0042] All vehicle testing procedures and the corresponding vehicle testing objectives are used as the initial suspension testing scheme for the user vehicle.

[0043] Optionally, the adjustment module is specifically used for:

[0044] Identify the structural difference type corresponding to each of the aforementioned structural difference information, and based on the structural range of each target unit corresponding to each structural difference range and the structural difference type corresponding to the structural difference information of each structural difference range, identify the associated vehicle anomaly information caused by each structural difference range through the suspension structural anomaly identification network.

[0045] In the suspension test database, query the test process of each associated vehicle corresponding to the abnormal information of the associated vehicle, and the test target of each associated vehicle corresponding to the test process of each associated vehicle. Based on each vehicle test process and each associated vehicle test process, generate the target suspension test process of the user vehicle through process optimization processing strategy.

[0046] Based on the vehicle test objectives of each of the vehicle test processes and the associated vehicle test objectives corresponding to each of the associated vehicle test processes, a suspension test objective for the target suspension test process is generated, and the target suspension test process and the suspension test objective of the target suspension test process are used as the target suspension test scheme for the user vehicle.

[0047] 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.

[0048] 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.

[0049] 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.

[0050] The aforementioned suspension testing optimization method, apparatus, and computer equipment acquire vehicle suspension feedback information and vehicle suspension structure detection information of the user vehicle. Based on the vehicle suspension feedback information, they query the suspension anomaly information of the user vehicle in the suspension testing database. Based on the vehicle suspension structure detection information, they identify the suspension structure anomaly information of the user vehicle and, based on the suspension structure anomaly information, identify the abnormal suspension structure region of the user vehicle. Based on the suspension anomaly information of the user vehicle, they generate an initial suspension test plan for the user vehicle and, based on the suspension structure anomaly information and the abnormal suspension structure region of the user vehicle, perform test adjustment processing on the initial suspension test plan to obtain the target suspension test plan for the user vehicle. This solution intelligently analyzes the anomaly information of the user vehicle's suspension from two perspectives: vehicle suspension feedback information and vehicle suspension structure detection information. This comprehensive identification of the anomaly information in the user vehicle's suspension avoids the limitations, biases, and errors of human judgment. Then, when generating the test plan, this approach first generates a suspension test plan based on the suspension anomaly information, and then adjusts the suspension test plan based on the identified suspension structural anomaly information. This ensures that the generated suspension test plan can effectively test the vehicle suspension anomaly information comprehensively, and that the test plan can comprehensively test both suspension structural anomalies and suspension anomaly information. Furthermore, it reduces the testing process, ensuring the comprehensiveness of the generated target suspension test plan and the simplicity of the testing process, thereby comprehensively improving the testing efficiency of the vehicle suspension. Attached Figure Description

[0051] 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.

[0052] Figure 1This is a flowchart illustrating a suspension testing optimization method in one embodiment;

[0053] Figure 2 This is a flowchart illustrating an example of suspension test optimization in one embodiment;

[0054] Figure 3 This is a structural block diagram of a suspension test optimization device in one embodiment;

[0055] Figure 4 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0056] 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.

[0057] The suspension testing optimization method provided in this application can be applied to vehicle suspension testing environments. 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 intelligently analyzes the abnormal information in the user vehicle's suspension from two perspectives: vehicle suspension feedback information and vehicle suspension structure testing information. This comprehensive analysis identifies the abnormal information in the user vehicle's suspension, avoiding the limitations, biases, and errors of human judgment. Then, when generating the test plan, this approach first generates a suspension test plan based on the suspension anomaly information, and then adjusts the suspension test plan based on the identified suspension structural anomaly information. This ensures that the generated suspension test plan can effectively test the vehicle suspension anomaly information comprehensively, and that the test plan can comprehensively test both suspension structural anomalies and suspension anomaly information. Furthermore, it reduces the testing process, ensuring the comprehensiveness of the generated target suspension test plan and the simplicity of the testing process, thereby comprehensively improving the testing efficiency of the vehicle suspension.

[0058] In one exemplary embodiment, such as Figure 1 As shown, a suspension testing optimization method is provided. Taking the application of this method to a terminal as an example, the method includes the following steps S101 to S103. Wherein:

[0059] Step S101: Obtain the vehicle suspension feedback information and the vehicle suspension structure detection information of the user vehicle, and based on the vehicle suspension feedback information, query the suspension abnormal information of the user vehicle in the suspension test database.

[0060] In this embodiment, the terminal responds to the user's vehicle information feedback operation, obtains the vehicle suspension problem information related to the vehicle suspension reported by the user, and uses all vehicle suspension problem information as the vehicle suspension feedback information for the user's vehicle. The information type of this vehicle suspension problem information can be audio or text. Then, the terminal uses a vehicle 3D structure scanner to perform multi-angle scanning and imaging of the user's vehicle suspension structure, obtaining structural inspection information of the vehicle suspension structure. Finally, based on the vehicle suspension feedback information, the terminal queries the suspension test database for suspension anomaly information for the user's vehicle. This suspension anomaly information refers to the anomaly information corresponding to the suspension anomaly problem existing in the user's vehicle, and this anomaly information is used to characterize the anomaly problem of the suspension structure. The specific query process will be explained in detail later.

[0061] Step S102: Based on the vehicle suspension structure detection information of the user vehicle, identify the suspension structure abnormal information of the user vehicle, and based on the suspension structure abnormal information of the user vehicle, identify the suspension abnormal structure area of ​​the user vehicle.

[0062] In this embodiment, the terminal identifies suspension structure anomalies based on the vehicle's suspension structure detection information, and then identifies anomalous suspension structure regions based on these anomalies. The suspension structure anomalies refer to the appearance anomalies of the vehicle's suspension structure, including but not limited to: structural fractures, deformations, loosening, displacement, and cracking. The anomalous suspension structure regions are the areas of the suspension structure containing these appearance anomalies. The specific identification process will be explained in detail later.

[0063] Step S103: Based on the suspension anomaly information of the user vehicle, generate an initial suspension test plan for the user vehicle, and based on the suspension structure anomaly information and the suspension anomaly structure area of ​​the user vehicle, perform test adjustment processing on the initial suspension test plan to obtain the target suspension test plan for the user vehicle.

[0064] In this embodiment, the terminal generates an initial suspension test plan for the user vehicle based on the suspension anomaly information. Then, based on the suspension structural anomaly information and the abnormal suspension structural areas of the user vehicle, the terminal performs test adjustment processing on the initial suspension test plan to obtain the target suspension test plan for the user vehicle. The initial suspension test plan comprises various vehicle test procedures for testing the vehicle's suspension, and the corresponding vehicle test objectives for each test procedure. The vehicle test procedures include, but are not limited to, various suspension function test types, suspension dynamic test types, suspension comfort and handling test types, and suspension noise test types. Each vehicle test objective is a test target that tests different functions, areas, and conditions of the vehicle.

[0065] Based on the above scheme, this approach intelligently analyzes the abnormal information in the user vehicle's suspension from two perspectives: vehicle suspension feedback information and vehicle suspension structure detection information. This comprehensive analysis identifies the abnormal information in the user vehicle's suspension, avoiding the limitations, biases, and errors of human judgment. When generating a test plan, this scheme first generates a suspension test plan based on the abnormal suspension information, and then adjusts the suspension test plan based on the identified structural abnormalities. This ensures that the generated suspension test plan can effectively and comprehensively test the vehicle's suspension abnormalities, and that the test plan can comprehensively test both structural and informational suspension abnormalities. Furthermore, it reduces the testing process, ensuring the comprehensiveness and streamlining of the generated target suspension test plan, thereby comprehensively improving the efficiency of vehicle suspension testing.

[0066] Optionally, based on vehicle suspension feedback information, the suspension anomaly information of the user vehicle is queried in the suspension test database, including: based on the vehicle suspension feedback information, using a semantic recognition network, identifying each suspension anomaly feature of the user vehicle and the degree of suspension anomaly of each suspension anomaly feature; based on the suspension anomaly information corresponding to each suspension anomaly feature of the user vehicle, querying each initial suspension anomaly information of the user vehicle in the suspension anomaly database, and identifying the anomaly degree range corresponding to each initial suspension anomaly information; based on the suspension anomaly degree of each suspension anomaly feature and the anomaly degree range corresponding to each initial suspension anomaly information, filtering the suspension anomaly information of the user vehicle from the initial suspension anomaly information.

[0067] In this embodiment, the terminal, based on vehicle suspension feedback information, uses a semantic recognition network to identify various suspension anomalies and their severity. This semantic recognition network is a large language model based on natural language processing technology. When the vehicle suspension feedback information is audio, the terminal first uses a text recognition network to identify the corresponding text content, and then uses the semantic recognition network to identify the various suspension anomalies and their severity. These suspension anomalies are the feature information corresponding to suspension problems. These anomalies include, but are not limited to, abnormal features such as vehicle bumps, long braking distances, excessive vehicle vibration, and excessive body roll in corners. The severity of each anomaly is categorized as the degree of vehicle bumps, braking distance range, vibration level, and body roll angle. Then, based on the suspension anomaly information corresponding to each suspension anomaly feature of the user vehicle, the terminal queries the suspension anomaly database for each initial suspension anomaly information of the user vehicle and identifies the anomaly severity range corresponding to each initial suspension anomaly information. Next, based on the suspension anomaly severity of each suspension anomaly feature and the anomaly severity range corresponding to each initial suspension anomaly information, the terminal filters the user vehicle's suspension anomaly information from the initial suspension anomaly information. The suspension anomaly database stores the correspondence between different suspension anomaly information and suspension anomaly features, with each suspension anomaly information corresponding to a suspension anomaly feature range. This suspension anomaly feature range includes the anomaly severity range of each suspension anomaly feature. The terminal identifies the suspension anomaly information corresponding to the user vehicle through the above correspondence.

[0068] Based on the above scheme, by recognizing the abnormal features and degree of each suspension abnormality through semantic feature recognition, the corresponding suspension abnormality information of the user's vehicle is then identified, thereby improving the accuracy of identifying suspension abnormality information.

[0069] Optionally, based on the vehicle suspension structure detection information of the user vehicle, identify the suspension structure anomaly information of the user vehicle, including: generating a three-dimensional structure model of the user vehicle's suspension based on the vehicle suspension structure detection information of the user vehicle, and querying the target vehicle suspension three-dimensional structure model in the database; based on the target vehicle suspension three-dimensional structure model, identifying the structural difference range in the vehicle suspension three-dimensional structure model, as well as the structural difference information corresponding to each structural difference range, and taking the structural difference information corresponding to all structural difference ranges as the suspension structure anomaly information of the user vehicle.

[0070] In this embodiment, the terminal generates a three-dimensional suspension structure model of the user vehicle based on the vehicle suspension structure detection information, and queries the database for the target vehicle suspension three-dimensional structure model. Specifically, the vehicle suspension structure detection information is the suspension three-dimensional structure data of the user vehicle. Then, based on the suspension three-dimensional structure data, the terminal constructs the suspension three-dimensional structure model of the user vehicle through a three-dimensional modeling program. The target vehicle suspension three-dimensional structure model stored in the database for each vehicle is the suspension three-dimensional structure model of each vehicle under normal conditions.

[0071] Next, based on the target vehicle's 3D suspension structural model, the terminal identifies the range of structural differences within the model and the corresponding structural difference information. This information is then used as the suspension structural anomaly information for the user's vehicle. Specifically, the terminal uses the target vehicle's 3D suspension template as a standard to identify the range of differences in the 3D structural model, defining it as the structural difference range. The structural difference information corresponding to each range is determined by a structural difference type recognition network built using a self-attention mechanism convolutional neural network. The identified structural difference type for each range is then used as the structural difference information for that range.

[0072] Based on the above scheme, by using the model difference information after modeling, the structural difference range of the user's vehicle and the structural difference information are identified, thereby improving the efficiency and accuracy of identifying the structural difference range.

[0073] Optionally, based on the suspension structure anomaly information of the user vehicle, the abnormal suspension structure region of the user vehicle is identified, including: splitting the three-dimensional structure model of the user vehicle's suspension into sub-three-dimensional structure models of each vehicle suspension structure, and in each sub-three-dimensional structure model of the vehicle suspension structure, using a three-dimensional structure recognition network to identify the unit structure range corresponding to each suspension unit of each vehicle suspension structure; for each structural difference range, based on the structural difference range and the unit structure range corresponding to each suspension unit of each vehicle suspension structure, identifying each target unit structure range corresponding to the structural difference range, and taking each target unit structure range corresponding to each structural difference range as the abnormal suspension structure region of the user vehicle.

[0074] In this embodiment, the terminal breaks down the three-dimensional structural model of the user's vehicle suspension into sub-three-dimensional structural models of each vehicle suspension structure. Within each sub-three-dimensional structural model, a three-dimensional structural recognition network identifies the unit structural range corresponding to each suspension unit of each vehicle suspension structure. Each suspension unit is a structural unit that makes up the entire vehicle suspension structure. The vehicle-mounted suspension structure includes, but is not limited to, shock absorbers, suspension springs, anti-roll bars, sub-beams, control arms, longitudinal rods, steering knuckle arms, rubber bushings, and connecting rods. Each structural unit of a suspension structure includes, for example, the shock absorber, lower spring pad, dust cover, spring, shock absorber pad, upper spring pad, spring seat, bearing, top mount, and nut. Each structural unit corresponds to a unit structural range within the sub-three-dimensional structural model of the vehicle suspension structure; this unit structural range is the structural model range of that structural unit within the sub-three-dimensional structural model.

[0075] For each structural difference range, the terminal identifies the target unit structural range corresponding to the structural difference range based on the structural difference range and the unit structural range corresponding to each suspension unit of each vehicle suspension structure, and takes the target unit structural range corresponding to each structural difference range as the abnormal suspension structural area of ​​the user vehicle.

[0076] Based on the above scheme, by breaking down the vehicle suspension into structural units of each vehicle suspension structure and then identifying the range of structural differences, the accuracy of the difference range identification is improved.

[0077] Optionally, based on the suspension anomaly information of the user vehicle, an initial suspension test plan for the user vehicle is generated, including: identifying the suspension anomaly type corresponding to the suspension anomaly information, and based on the suspension anomaly type corresponding to the suspension anomaly information, querying the suspension test database for the test process of each vehicle corresponding to the suspension anomaly information, and the vehicle test target corresponding to each vehicle test process; and using all vehicle test processes and the vehicle test targets corresponding to all vehicle test processes as the initial suspension test plan for the user vehicle.

[0078] In this embodiment, the terminal identifies the suspension anomaly type corresponding to the suspension anomaly information, and based on the suspension anomaly type, queries the suspension test database for the vehicle test procedures corresponding to the suspension anomaly information, as well as the vehicle test objectives corresponding to each vehicle test procedure. The suspension test database stores the vehicle test procedures corresponding to each suspension anomaly type, and each vehicle test procedure corresponds to a vehicle test objective. The vehicle test objective indicates the purpose of the vehicle test procedure; that is, through the vehicle test procedure, the test results of the vehicle test objective can be obtained.

[0079] Then, the terminal uses all vehicle testing procedures and the corresponding vehicle testing objectives as the initial suspension testing plan for the user vehicle.

[0080] Based on the above scheme, by identifying the suspension anomaly type corresponding to the suspension anomaly information and then filtering the vehicle testing process, the accuracy and efficiency of vehicle testing process identification are improved.

[0081] Optionally, based on the suspension structural anomaly information of the user vehicle and the abnormal suspension structural regions of the user vehicle, the initial suspension test plan is adjusted to obtain the target suspension test plan for the user vehicle. This includes: identifying the structural difference type corresponding to each structural difference information, and based on the structural range of each target unit structure corresponding to each structural difference range and the structural difference type corresponding to the structural difference information of each structural difference range, identifying the associated vehicle anomaly information caused by each structural difference range through a suspension structural anomaly identification network; querying the test processes of each associated vehicle corresponding to the associated vehicle anomaly information and the associated vehicle test targets corresponding to each associated vehicle test process in the suspension test database, and generating the target suspension test process for the user vehicle through process optimization processing strategies based on each vehicle test process and each associated vehicle test process; generating the suspension test target of the target suspension test process based on the vehicle test targets of each vehicle test process and the associated vehicle test targets corresponding to each associated vehicle test process, and using the target suspension test process and the suspension test target of the target suspension test process as the target suspension test plan for the user vehicle.

[0082] In this embodiment, the terminal identifies the structural difference type corresponding to each structural difference information, and based on the structural range of each target unit corresponding to each structural difference range, and the structural difference type corresponding to the structural difference information of each structural difference range, it identifies the associated vehicle anomaly information caused by each structural difference range through a suspension structural anomaly identification network. This suspension structural anomaly identification network is an artificial neural network based on reinforcement learning, which is trained extensively using the structural range of each sample unit, the sample structural difference type, and the sample associated vehicle anomaly information. The associated vehicle anomaly information includes the impact information of each structural difference information on vehicle suspension anomalies, or the vehicle suspension anomaly information generated by the structural difference information, etc.

[0083] Then, the terminal queries the suspension test database for the test processes and test targets corresponding to the abnormal information of each associated vehicle. Based on these test processes and related vehicle test processes, the terminal generates a target suspension test process for the user vehicle through a process optimization strategy. Next, based on the vehicle test targets of each test process and the related vehicle test targets, the terminal generates the suspension test targets for the target suspension test process and uses these target suspension test processes and their corresponding suspension test targets as the target suspension test plan for the user vehicle. The process optimization strategy involves identifying the vehicle test methods and test steps within each vehicle test process, and combining identical vehicle test methods and test steps to obtain the target suspension test process. Therefore, the target suspension test process may correspond to one or more suspension test targets.

[0084] Based on the above solution, by merging and optimizing the vehicle test process for abnormal vehicle information and related abnormal vehicle information, the suspension test target is also merged and optimized, thereby improving the optimization effect of the suspension test process and the optimization effect of the suspension test target.

[0085] This application also provides an example of suspension test optimization, such as... Figure 2 As shown, the specific processing procedure includes the following steps:

[0086] Step S201: Obtain vehicle suspension feedback information and vehicle suspension structure detection information of the user vehicle.

[0087] Step S202: Based on the vehicle suspension feedback information, the user's vehicle's various suspension abnormalities and the degree of suspension abnormality of each abnormality are identified through a semantic recognition network.

[0088] Step S203: Based on the suspension anomaly information corresponding to each suspension anomaly feature of the user vehicle, query the initial suspension anomaly information of the user vehicle in the suspension anomaly database, and identify the anomaly degree range corresponding to each initial suspension anomaly information.

[0089] Step S204: Based on the degree of suspension abnormality of each suspension abnormality feature and the range of abnormality corresponding to each initial suspension abnormality information, filter the suspension abnormality information of the user vehicle from each initial suspension abnormality information.

[0090] Step S205: Based on the vehicle suspension structure detection information of the user vehicle, generate a three-dimensional structure model of the user vehicle's suspension, and query the database for the target three-dimensional structure model of the user vehicle's suspension.

[0091] Step S206: Based on the three-dimensional structural model of the target vehicle suspension, identify the range of structural differences in the three-dimensional structural model of the vehicle suspension, as well as the structural difference information corresponding to each range of structural differences, and use the structural difference information corresponding to all ranges of structural differences as the suspension structural anomaly information of the user vehicle.

[0092] Step S207: The three-dimensional structural model of the user vehicle's suspension is divided into sub-three-dimensional structural models of each vehicle suspension structure. In each sub-three-dimensional structural model of the vehicle suspension structure, the unit structure range corresponding to each suspension unit of each vehicle suspension structure is identified through a three-dimensional structural recognition network.

[0093] Step S208: For each structural difference range, based on the structural difference range and the unit structural range corresponding to each suspension unit of each vehicle suspension structure, identify each target unit structural range corresponding to the structural difference range, and take each target unit structural range corresponding to each structural difference range as the suspension abnormal structure area of ​​the user vehicle.

[0094] Step S209: Identify the suspension anomaly type corresponding to the suspension anomaly information, and based on the suspension anomaly type corresponding to the suspension anomaly information, query the suspension test database for the test process of each vehicle corresponding to the suspension anomaly information, and the vehicle test target corresponding to each vehicle test process.

[0095] Step S210: Take all vehicle test procedures and the corresponding vehicle test objectives as the initial suspension test plan for the user vehicle.

[0096] Step S211: Identify the structural difference type corresponding to each structural difference information, and based on the structural range of each target unit corresponding to each structural difference range and the structural difference type corresponding to the structural difference information of each structural difference range, identify the associated vehicle anomaly information caused by each structural difference range through the suspension structural anomaly identification network.

[0097] Step S212: In the suspension test database, query the test processes of each associated vehicle corresponding to the abnormal information of the associated vehicle, and the test targets of each associated vehicle corresponding to the test processes of each associated vehicle. Based on each vehicle test process and each associated vehicle test process, generate the target suspension test process for the user vehicle through process optimization processing strategy.

[0098] Step S213: Based on the vehicle test objectives of each vehicle test process and the associated vehicle test objectives corresponding to each associated vehicle test process, generate the suspension test objectives of the target suspension test process, and use the target suspension test process and the suspension test objectives of the target suspension test process as the target suspension test scheme for the user vehicle.

[0099] 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.

[0100] Based on the same inventive concept, this application also provides a suspension test optimization device for implementing the suspension test optimization method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the suspension test optimization device provided below can be found in the limitations of the suspension test optimization method described above, and will not be repeated here.

[0101] In one exemplary embodiment, such as Figure 3 As shown, a suspension testing optimization device is provided, comprising: an acquisition module 310, an identification module 320, and an adjustment module 330, wherein:

[0102] The acquisition module 310 is used to acquire vehicle suspension feedback information of the user vehicle and vehicle suspension structure detection information of the user vehicle, and based on the vehicle suspension feedback information, query the suspension abnormal information of the user vehicle in the suspension test database.

[0103] The identification module 320 is used to identify the abnormal information of the suspension structure of the user vehicle based on the vehicle suspension structure detection information of the user vehicle, and to identify the abnormal suspension structure area of ​​the user vehicle based on the abnormal information of the suspension structure of the user vehicle.

[0104] The adjustment module 330 is used to generate an initial suspension test plan for the user vehicle based on the suspension anomaly information of the user vehicle, and to perform test adjustment processing on the initial suspension test plan based on the suspension structure anomaly information of the user vehicle and the suspension anomaly structure area of ​​the user vehicle, so as to obtain the target suspension test plan for the user vehicle.

[0105] Optionally, the acquisition module 310 is specifically used for:

[0106] Based on the vehicle suspension feedback information, a semantic recognition network is used to identify each suspension anomaly feature of the user's vehicle and the degree of suspension anomaly of each of the suspension anomaly features.

[0107] Based on the suspension anomaly information corresponding to each suspension anomaly feature of the user vehicle, query the initial suspension anomaly information of the user vehicle in the suspension anomaly database, and identify the anomaly degree range corresponding to each initial suspension anomaly information.

[0108] Based on the degree of suspension abnormality of each of the aforementioned suspension abnormality features and the range of abnormality corresponding to each of the aforementioned initial suspension abnormality information, the suspension abnormality information of the user vehicle is filtered from the aforementioned initial suspension abnormality information.

[0109] Optionally, the identification module 320 is specifically used for:

[0110] Based on the vehicle suspension structure detection information of the user vehicle, a three-dimensional structure model of the user vehicle suspension is generated, and the target three-dimensional structure model of the user vehicle suspension is queried in the database.

[0111] Based on the target vehicle suspension three-dimensional structural model, the structural difference range in the vehicle suspension three-dimensional structural model and the structural difference information corresponding to each structural difference range are identified, and the structural difference information corresponding to all structural difference ranges is used as the suspension structural anomaly information of the user vehicle.

[0112] Optionally, the identification module 320 is specifically used for:

[0113] The three-dimensional structural model of the user vehicle's suspension is divided into sub-three-dimensional structural models of each vehicle suspension structure. In each sub-three-dimensional structural model of the vehicle suspension structure, the unit structure range corresponding to each suspension unit of each vehicle suspension structure is identified by a three-dimensional structural recognition network.

[0114] For each structural difference range, based on the structural difference range and the unit structural range corresponding to each suspension unit of each vehicle suspension structure, the target unit structural range corresponding to the structural difference range is identified, and each target unit structural range corresponding to each structural difference range is taken as the suspension abnormal structure area of ​​the user vehicle.

[0115] Optionally, the adjustment module 330 is specifically used for:

[0116] Identify the suspension anomaly type corresponding to the suspension anomaly information, and based on the suspension anomaly type corresponding to the suspension anomaly information, query the suspension test database for each vehicle test process corresponding to the suspension anomaly information, and the vehicle test target corresponding to each vehicle test process.

[0117] All vehicle testing procedures and the corresponding vehicle testing objectives are used as the initial suspension testing scheme for the user vehicle.

[0118] Optionally, the adjustment module 330 is specifically used for:

[0119] Identify the structural difference type corresponding to each of the aforementioned structural difference information, and based on the structural range of each target unit corresponding to each structural difference range and the structural difference type corresponding to the structural difference information of each structural difference range, identify the associated vehicle anomaly information caused by each structural difference range through the suspension structural anomaly identification network.

[0120] In the suspension test database, query the test process of each associated vehicle corresponding to the abnormal information of the associated vehicle, and the test target of each associated vehicle corresponding to the test process of each associated vehicle. Based on each vehicle test process and each associated vehicle test process, generate the target suspension test process of the user vehicle through process optimization processing strategy.

[0121] Based on the vehicle test objectives of each of the vehicle test processes and the associated vehicle test objectives corresponding to each of the associated vehicle test processes, a suspension test objective for the target suspension test process is generated, and the target suspension test process and the suspension test objective of the target suspension test process are used as the target suspension test scheme for the user vehicle.

[0122] Each module in the aforementioned suspension testing optimization device 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, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0123] 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 executed by the processor, the computer program implements a suspension test optimization method. 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.

[0124] 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.

[0125] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps corresponding to the suspension test optimization method.

[0126] 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 corresponding to the suspension test optimization method.

[0127] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps corresponding to the suspension test optimization method.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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 testing optimization method, characterized in that, The method includes: Obtain vehicle suspension feedback information and vehicle suspension structure detection information of the user vehicle, and based on the vehicle suspension feedback information, query the suspension abnormal information of the user vehicle in the suspension test database. Based on the vehicle suspension structure detection information of the user vehicle, identify the suspension structure abnormality information of the user vehicle, and based on the suspension structure abnormality information of the user vehicle, identify the suspension abnormal structure area of ​​the user vehicle. Based on the suspension anomaly information of the user vehicle, an initial suspension test plan for the user vehicle is generated. Identify the structural difference type corresponding to each of the aforementioned structural difference information, and based on the structural range of each target unit corresponding to each structural difference range and the structural difference type corresponding to the structural difference information of each structural difference range, identify the associated vehicle anomaly information caused by each structural difference range through the suspension structural anomaly identification network. In the suspension test database, query the test process of each associated vehicle corresponding to the abnormal information of the associated vehicle, and the test target of each associated vehicle corresponding to the test process of the associated vehicle. Based on the vehicle test process and the associated vehicle test process, identify the vehicle test methods and vehicle test steps for each vehicle test process, and combine the same vehicle test methods and vehicle test steps to obtain the target suspension test process. Based on the vehicle test objectives of each of the vehicle test processes and the associated vehicle test objectives corresponding to each of the associated vehicle test processes, a suspension test objective for the target suspension test process is generated, and the target suspension test process and the suspension test objective of the target suspension test process are used as the target suspension test scheme for the user vehicle.

2. The method according to claim 1, characterized in that, The step of querying the suspension anomaly information of the user vehicle in the suspension test database based on the vehicle suspension feedback information includes: Based on the vehicle suspension feedback information, a semantic recognition network is used to identify each suspension anomaly feature of the user's vehicle and the degree of suspension anomaly of each of the suspension anomaly features. Based on the suspension anomaly information corresponding to each suspension anomaly feature of the user vehicle, query the initial suspension anomaly information of the user vehicle in the suspension anomaly database, and identify the anomaly degree range corresponding to each initial suspension anomaly information. Based on the degree of suspension abnormality of each of the aforementioned suspension abnormality features and the range of abnormality corresponding to each of the aforementioned initial suspension abnormality information, the suspension abnormality information of the user vehicle is filtered from the aforementioned initial suspension abnormality information.

3. The method according to claim 1, characterized in that, The step of identifying abnormal information in the suspension structure of the user vehicle based on the vehicle suspension structure detection information includes: Based on the vehicle suspension structure detection information of the user vehicle, a three-dimensional structure model of the user vehicle suspension is generated, and the target three-dimensional structure model of the user vehicle suspension is queried in the database. Based on the target vehicle suspension three-dimensional structural model, the structural difference range in the vehicle suspension three-dimensional structural model and the structural difference information corresponding to each structural difference range are identified, and the structural difference information corresponding to all structural difference ranges is used as the suspension structural anomaly information of the user vehicle.

4. The method according to claim 3, characterized in that, The step of identifying the abnormal suspension structure region of the user vehicle based on the abnormal suspension structure information of the user vehicle includes: The three-dimensional structural model of the user vehicle's suspension is divided into sub-three-dimensional structural models of each vehicle suspension structure. In each sub-three-dimensional structural model of the vehicle suspension structure, the unit structure range corresponding to each suspension unit of each vehicle suspension structure is identified by a three-dimensional structural recognition network. For each structural difference range, based on the structural difference range and the unit structural range corresponding to each suspension unit of each vehicle suspension structure, the target unit structural range corresponding to the structural difference range is identified, and each target unit structural range corresponding to each structural difference range is taken as the suspension abnormal structure area of ​​the user vehicle.

5. The method according to claim 1, characterized in that, The step of generating an initial suspension test plan for the user vehicle based on the suspension anomaly information of the user vehicle includes: Identify the suspension anomaly type corresponding to the suspension anomaly information, and based on the suspension anomaly type corresponding to the suspension anomaly information, query the suspension test database for each vehicle test process corresponding to the suspension anomaly information, and the vehicle test target corresponding to each vehicle test process. All vehicle testing procedures and the corresponding vehicle testing objectives are used as the initial suspension testing scheme for the user vehicle.

6. A suspension testing optimization device, characterized in that, The device includes: The acquisition module is used to acquire vehicle suspension feedback information and vehicle suspension structure detection information of the user vehicle, and based on the vehicle suspension feedback information, query the suspension abnormal information of the user vehicle in the suspension test database. The identification module is used to identify abnormal information of the suspension structure of the user vehicle based on the vehicle suspension structure detection information of the user vehicle, and to identify the abnormal suspension structure area of ​​the user vehicle based on the abnormal information of the suspension structure of the user vehicle. The adjustment module is used to generate an initial suspension test plan for the user vehicle based on the suspension anomaly information of the user vehicle; identify the structural difference type corresponding to each of the structural difference information, and based on the structural unit structural range corresponding to each structural difference range and the structural difference type corresponding to the structural difference information of each structural difference range, identify the associated vehicle anomaly information caused by each structural difference range through a suspension structural anomaly identification network; query the associated vehicle test process and the associated vehicle test target corresponding to each of the associated vehicle anomaly information in the suspension test database; identify the vehicle test method and vehicle test steps of each vehicle test process based on each vehicle test process and each of the associated vehicle test processes, and combine the same vehicle test method and vehicle test steps to obtain a target suspension test process; generate the suspension test target of the target suspension test process based on the vehicle test target of each vehicle test process and the associated vehicle test target corresponding to each of the associated vehicle test processes, and use the target suspension test process and the suspension test target of the target suspension test process as the target suspension test plan for the user vehicle.

7. The apparatus according to claim 6, characterized in that, The acquisition module is specifically used for: Based on the vehicle suspension feedback information, a semantic recognition network is used to identify each suspension anomaly feature of the user's vehicle and the degree of suspension anomaly of each of the suspension anomaly features. Based on the suspension anomaly information corresponding to each suspension anomaly feature of the user vehicle, query the initial suspension anomaly information of the user vehicle in the suspension anomaly database, and identify the anomaly degree range corresponding to each initial suspension anomaly information. Based on the degree of suspension abnormality of each of the aforementioned suspension abnormality features and the range of abnormality corresponding to each of the aforementioned initial suspension abnormality information, the suspension abnormality information of the user vehicle is filtered from the aforementioned initial suspension abnormality information.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

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

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

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