Suspension test optimization method and device and computer equipment

Abstract

The invention relates to a suspension test optimization method and device and computer equipment. The method comprises the steps that vehicle suspension feedback information of a user vehicle and vehicle suspension structure detection information of the user vehicle are acquired, and suspension abnormal information of the user vehicle is inquired 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, and identifying a suspension abnormal structure area of the user vehicle based on the suspension structure abnormal information of the user vehicle; and 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. By adopting the method, the detection efficiency of the vehicle suspension can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent suspension testing, and in particular to a suspension testing optimization method, device and computer equipment. Background Art

[0002] The detection of vehicle suspension systems can promptly discover and repair potential faults, such as spring breakage and shock absorber oil leakage. If these faults are not handled in time, they may cause abnormal shaking, unstable control and other problems during driving, and even cause traffic accidents. However, in order to ensure the comprehensiveness of the detection, the detection time of the actual vehicle suspension system is long and the detection method is complex, which makes the resource cost of comprehensive detection of the vehicle suspension system high. Therefore, how to improve the process optimization effect of suspension testing is the current research focus.

[0003] The traditional optimization method for vehicle suspension detection is to manually conduct subjective analysis of the vehicle suspension conditions and the user's suspension abnormality feedback information, and then combine the corresponding detection equipment and corresponding detection instruments to comprehensively detect the vehicle suspension. However, manual detection is affected by work experience and the degree of observation, resulting in a high error rate in the inspection of vehicle suspension and a cumbersome and lengthy detection process, which leads to low detection efficiency of vehicle suspension. Summary of the invention

[0004] Based on this, it is necessary to provide a suspension test optimization method, device, computer equipment, computer readable storage medium and computer program product to address the above technical problems.

[0005] In a first aspect, the present application provides a suspension test optimization method, comprising:

[0006] Acquire vehicle suspension feedback information of a user vehicle and vehicle suspension structure detection information of the user vehicle, and query suspension abnormality information of the user vehicle in a suspension test database based on the vehicle suspension feedback information;

[0007] Based on the vehicle suspension structure detection information of the user vehicle, identifying the suspension structure abnormality information of the user vehicle, and based on the suspension structure abnormality information of the user vehicle, identifying the suspension abnormality structure area of ​​the user vehicle;

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

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

[0010] Based on the vehicle suspension feedback information, identifying each suspension abnormality feature of the user vehicle and the degree of suspension abnormality of each suspension abnormality feature through a semantic recognition network;

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

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

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

[0014] Based on the vehicle suspension structure detection information of the user vehicle, a vehicle suspension three-dimensional structure model of the user vehicle is generated, and a target vehicle suspension three-dimensional structure model of the user vehicle is searched in a 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 of the structural difference ranges are identified, and the structural difference information corresponding to all the structural difference ranges is used as the suspension structure abnormality information of the user vehicle.

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

[0017] The vehicle suspension three-dimensional structure model of the user vehicle is split into three-dimensional sub-structure models of each vehicle suspension structure, and in each three-dimensional sub-structure model of the vehicle suspension structure, a unit structure range corresponding to each suspension unit of each vehicle suspension structure is respectively identified by a three-dimensional structure 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, each 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 used as the abnormal suspension structure area of ​​the user vehicle.

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

[0020] Identify the suspension abnormality type corresponding to the suspension abnormality information, and based on the suspension abnormality type corresponding to the suspension abnormality information, query each vehicle test process corresponding to the suspension abnormality information and the vehicle test target corresponding to each vehicle test process in a suspension test database;

[0021] All vehicle test processes and vehicle test targets corresponding to all vehicle test processes are used as the initial suspension test plan for the user vehicle.

[0022] Optionally, the performing test adjustment processing on the initial suspension test scheme based on the suspension structure abnormality 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 includes:

[0023] Identifying the structural difference type corresponding to each of the structural difference information, and based on each target 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, identifying the associated vehicle abnormality information caused by each structural difference range through a suspension structure abnormality identification network;

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

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

[0026] In a second aspect, the present application also provides a suspension test optimization device, comprising:

[0027] An acquisition module, used to acquire vehicle suspension feedback information of a user's vehicle and vehicle suspension structure detection information of the user's vehicle, and query suspension abnormality information of the user's vehicle in a suspension test database based on the vehicle suspension feedback information;

[0028] an identification module, configured to identify suspension structure abnormality information of the user vehicle based on vehicle suspension structure detection information of the user vehicle, and to identify a suspension abnormality structure area of ​​the user vehicle based on the suspension structure abnormality information of the user vehicle;

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

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

[0031] Based on the vehicle suspension feedback information, identifying each suspension abnormality feature of the user vehicle and the degree of suspension abnormality of each suspension abnormality feature through a semantic recognition network;

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

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

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

[0035] Based on the vehicle suspension structure detection information of the user vehicle, a vehicle suspension three-dimensional structure model of the user vehicle is generated, and a target vehicle suspension three-dimensional structure model of the user vehicle is searched in a 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 of the structural difference ranges are identified, and the structural difference information corresponding to all the structural difference ranges is used as the suspension structure abnormality information of the user vehicle.

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

[0038] The vehicle suspension three-dimensional structure model of the user vehicle is split into three-dimensional sub-structure models of each vehicle suspension structure, and in each three-dimensional sub-structure model of the vehicle suspension structure, a unit structure range corresponding to each suspension unit of each vehicle suspension structure is respectively identified by a three-dimensional structure 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, each 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 used as the abnormal suspension structure area of ​​the user vehicle.

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

[0041] Identify the suspension abnormality type corresponding to the suspension abnormality information, and based on the suspension abnormality type corresponding to the suspension abnormality information, query each vehicle test process corresponding to the suspension abnormality information and the vehicle test target corresponding to each vehicle test process in a suspension test database;

[0042] All vehicle test processes and vehicle test targets corresponding to all vehicle test processes are used as the initial suspension test plan for the user vehicle.

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

[0044] Identifying the structural difference type corresponding to each of the structural difference information, and based on each target 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, identifying the associated vehicle abnormality information caused by each structural difference range through a suspension structure abnormality identification network;

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

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

[0047] In a third aspect, the present application provides a computer device, wherein the computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of any one of the methods in the first aspect are implemented.

[0048] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.

[0049] In a fifth aspect, the present application provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.

[0050] The above suspension test optimization method, device and computer equipment obtain the vehicle suspension feedback information of the user vehicle and the vehicle suspension structure detection information of the user vehicle, and query the suspension abnormality information of the user vehicle in the suspension test database based on the vehicle suspension feedback information; identify the suspension structure abnormality information of the user vehicle based on the vehicle suspension structure detection information of the user vehicle, and identify the suspension abnormality structure area of ​​the user vehicle based on the suspension structure abnormality information of the user vehicle; generate the initial suspension test plan of the user vehicle based on the suspension abnormality information of the user vehicle, and perform test adjustment processing on the initial suspension test plan based on the suspension structure abnormality information of the user vehicle and the suspension abnormality structure area of ​​the user vehicle to obtain the target suspension test plan of the user vehicle. This solution intelligently analyzes the abnormal information of the suspension of the user vehicle from two perspectives: the vehicle suspension feedback information of the user vehicle and the vehicle suspension structure detection information, so as to comprehensively identify the abnormal information of the suspension of the user vehicle, thereby avoiding the limitations, bias and error of human judgment. Then, when generating the test plan, this plan first generates a suspension test plan based on the suspension abnormality information, and then adjusts the suspension test plan based on the identified suspension structure abnormality information, thereby ensuring that the generated suspension test plan can effectively conduct a comprehensive test on the vehicle suspension abnormality information, and the tested plan can comprehensively test the suspension structure abnormality and suspension abnormality information, and can also reduce the test process, ensuring the comprehensiveness of the test of the generated target suspension test plan, and the streamlining of the test process, thereby comprehensively improving the detection efficiency of the vehicle suspension. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0052] Figure 1A schematic diagram of a suspension test optimization method according to an embodiment;

[0053] Figure 2 A schematic diagram of a flow chart of a suspension test optimization example in one embodiment;

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

[0055] Figure 4 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0057] The suspension test optimization method provided in the embodiment of the present application can be applied to the application environment of vehicle suspension detection. The method can be applied to a terminal, a server, or a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. The terminal can be, but is not limited to, various personal computers, laptops, mid-size computers, etc. The terminal intelligently analyzes the abnormal information of the suspension of the user's vehicle from two perspectives: the feedback information of the vehicle suspension of the user's vehicle and the detection information of the vehicle suspension structure, thereby comprehensively identifying the abnormal information of the suspension of the user's vehicle and avoiding the limitations, biases, and errors of human judgment. Then, when generating the test plan, this plan first generates a suspension test plan based on the suspension abnormality information, and then adjusts the suspension test plan based on the identified suspension structure abnormality information, thereby ensuring that the generated suspension test plan can effectively conduct a comprehensive test on the vehicle suspension abnormality information, and the tested plan can comprehensively test the suspension structure abnormality and suspension abnormality information, and can also reduce the test process, ensuring the comprehensiveness of the test of the generated target suspension test plan, and the streamlining of the test process, thereby comprehensively improving the detection efficiency of the vehicle suspension.

[0058] In an exemplary embodiment, Figure 1 As shown, a suspension test optimization method is provided, which is described by taking the method applied to a terminal as an example, and includes the following steps S101 to S103. Among them:

[0059] Step S101, obtaining vehicle suspension feedback information of the user's vehicle and vehicle suspension structure detection information of the user's vehicle, and querying suspension abnormality information of the user's vehicle in a suspension test database based on the vehicle suspension feedback information.

[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 the vehicle suspension problem information as the vehicle suspension feedback information of the user's vehicle. Among them, the information type of the vehicle suspension problem information can be an audio information type or a text information type. Then, the terminal uses a vehicle three-dimensional structure scanner to perform multi-angle scanning and shooting processing on the vehicle suspension structure of the user's vehicle to obtain the structural detection information of the vehicle suspension structure. Finally, based on the vehicle suspension feedback information, the terminal queries the suspension abnormality information of the user's vehicle in the suspension test database. Among them, the suspension abnormality information is the abnormal problem information corresponding to the suspension abnormality problem existing in the user's vehicle. The abnormal problem information is used to characterize the abnormal problem of the suspension structure. The specific query process will be described in detail later.

[0061] Step S102, based on the vehicle suspension structure detection information of the user's vehicle, identifying the suspension structure abnormality information of the user's vehicle, and based on the suspension structure abnormality information of the user's vehicle, identifying the suspension abnormal structure area of ​​the user's vehicle.

[0062] In this embodiment, the terminal identifies the abnormal suspension structure information of the user's vehicle based on the vehicle suspension structure detection information of the user's vehicle, and identifies the abnormal suspension structure area of ​​the user's vehicle based on the abnormal suspension structure information of the user's vehicle. The abnormal suspension structure information is the appearance abnormality information of the vehicle suspension structure, wherein the appearance abnormality information includes but is not limited to: structural abnormalities such as structural fracture, structural deformation, structural looseness, structural displacement, and structural cracking. The abnormal suspension structure area of ​​the user's vehicle is the suspension structure area where the above-mentioned appearance abnormality information exists, and the specific identification process will be described in detail later.

[0063] Step S103, based on the suspension abnormality information of the user vehicle, an initial suspension test plan for the user vehicle is generated, and based on the suspension structure abnormality information of the user vehicle and the suspension abnormality structure area of ​​the user vehicle, the initial suspension test plan is tested and adjusted to obtain a target suspension test plan for the user vehicle.

[0064] In this embodiment, the terminal generates an initial suspension test plan for the user's vehicle based on the suspension abnormality information of the user's vehicle, and performs test adjustment processing on the initial suspension test plan based on the suspension structure abnormality information of the user's vehicle and the suspension abnormality structure area of ​​the user's vehicle to obtain a target suspension test plan for the user's vehicle. The initial suspension test plan is each vehicle test process for testing the vehicle suspension, and the vehicle test target corresponding to each vehicle test process, wherein the vehicle test process includes but is not limited to each suspension function test type, suspension dynamic test type, suspension comfort and handling test type, and suspension noise test type. Each vehicle test target is a test target for testing different functions, areas, situations, and other information of the vehicle.

[0065] Based on the above scheme, the abnormal information of the user's vehicle suspension is intelligently analyzed from two perspectives: the vehicle suspension feedback information of the user's vehicle and the vehicle suspension structure detection information, so as to comprehensively identify the abnormal information of the user's vehicle suspension, avoiding the limitations, bias and errors of human judgment. Then, when generating a test plan, this scheme first generates a suspension test plan based on the suspension abnormal information, and then adjusts the suspension test plan based on the identified suspension structure abnormal information, thereby ensuring that the generated suspension test plan can effectively conduct a comprehensive test on the vehicle suspension abnormal information, and the tested plan can comprehensively test the suspension structure abnormality and the suspension abnormal information, and can also reduce the test process, ensuring the comprehensiveness of the test of the generated target suspension test plan and the simplicity of the test process, thereby comprehensively improving the detection efficiency of the vehicle suspension.

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

[0067] In this embodiment, the terminal identifies the various suspension abnormality features of the user's vehicle and the degree of suspension abnormality of each suspension abnormality feature through a semantic recognition network based on the vehicle suspension feedback information. Among them, the semantic recognition network is a large language model based on natural language processing technology. When the vehicle suspension feedback information is audio information, the terminal first uses the audio information through a text recognition network to identify the text content corresponding to the audio information, and then uses the semantic recognition network to identify the various suspension abnormality features of the user's vehicle and the degree of suspension abnormality of each suspension abnormality feature. Among them, the suspension abnormality feature is the feature information corresponding to the suspension abnormality problem. The suspension abnormality feature includes, but is not limited to, abnormal features such as vehicle bumps, long vehicle braking distance, large vehicle jitter, and excessive vehicle curve roll. The degree of suspension abnormality of each suspension abnormality feature is, for example, the degree of suspension abnormality such as vehicle bumps, vehicle braking distance range, vehicle jitter, and vehicle roll angle. Then, based on the suspension abnormality information corresponding to each suspension abnormality feature of the user's vehicle, the terminal queries each initial suspension abnormality information of the user's vehicle in the suspension abnormality database, and identifies the abnormality degree range corresponding to each initial suspension abnormality information. After that, based on the suspension abnormality degree of each suspension abnormality feature and the abnormality degree range corresponding to each initial suspension abnormality information, the terminal screens the suspension abnormality information of the user's vehicle in each initial suspension abnormality information. Among them, the suspension abnormality database stores the corresponding relationship between different suspension abnormality information and suspension abnormality features, and each suspension abnormality information corresponds to a suspension abnormality feature range. The suspension abnormality feature range includes the abnormality degree range of each suspension abnormality feature. The terminal identifies the suspension abnormality information corresponding to the user's vehicle through the above-mentioned corresponding relationship.

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

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

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

[0071] Then, based on the target vehicle suspension 3D structure model, the terminal identifies the structural difference range in the vehicle suspension 3D structure model and the structural difference information corresponding to each structural difference range, and uses the structural difference information corresponding to all structural difference ranges as the suspension structure abnormality information of the user vehicle. The terminal uses the target vehicle suspension 3D structure template as the standard to identify the difference 3D structure model range in the vehicle suspension 3D structure model as the structural difference range, and the structural difference information corresponding to each structural difference range is a structural difference type recognition network constructed by a convolutional neural network based on a self-attention mechanism, and the structural difference type corresponding to each structural difference range is used as the structural difference information corresponding to each structural difference range.

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

[0073] Optionally, based on the suspension structure abnormality information of the user vehicle, the suspension abnormal structure area of ​​the user vehicle is identified, including: splitting the vehicle suspension three-dimensional structure model of the user vehicle into sub-three-dimensional structure models of each vehicle suspension structure, and in each sub-three-dimensional structure model of the vehicle suspension structure, respectively identifying the unit structure range corresponding to each suspension unit of each vehicle suspension structure through a three-dimensional structure recognition network; 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 using each target unit structure range corresponding to each structural difference range as the suspension abnormal structure area of ​​the user vehicle.

[0074] In this embodiment, the terminal splits the vehicle suspension three-dimensional structure model of the user's vehicle into sub-three-dimensional structure models of each vehicle suspension structure, and in each sub-three-dimensional structure model of the vehicle suspension structure, the unit structure range corresponding to each suspension unit of each vehicle suspension structure is respectively identified through the three-dimensional structure recognition network. Among them, each suspension unit of the vehicle suspension structure is a structural unit that constitutes each vehicle suspension structure. Among them, the vehicle-linked suspension structure includes but is not limited to suspension structures such as shock absorbers, suspension springs, anti-roll bars, sub-beams, control arms, longitudinal rods, steering knuckle arms, rubber bushings and connecting rods, and the structural units of each suspension structure are, for example, shock absorbers, lower spring pads, dust covers, springs, shock pads, upper spring pads, spring seats, bearings, top rubbers, nuts and other structural units of the shock absorber. And each structural unit corresponds to a unit structure range in the sub-three-dimensional structure model of the vehicle suspension structure, and the unit structure range is the structural model range of the structural unit in the sub-three-dimensional structure model.

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

[0076] Based on the above solution, the vehicle suspension is split into structural units of each vehicle suspension structure, and then the structural difference range is identified, thereby improving the accuracy of the difference range identification.

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

[0078] In this embodiment, the terminal identifies the suspension abnormality type corresponding to the suspension abnormality information, and based on the suspension abnormality type corresponding to the suspension abnormality information, queries each vehicle test process corresponding to the suspension abnormality information and the vehicle test target corresponding to each vehicle test process in the suspension test database. In the suspension test database, the vehicle test process corresponding to the suspension abnormality information of each suspension abnormality type is stored, and each vehicle test process corresponds to a vehicle test target. In the vehicle test target, the vehicle test target is used to indicate the vehicle test purpose of the vehicle test process, that is, the test result of the vehicle test target can be obtained through the vehicle test process.

[0079] Then, the terminal uses all vehicle test processes and vehicle test targets corresponding to all vehicle test processes as the initial suspension test plan for the user's vehicle.

[0080] Based on the above solution, after identifying the suspension abnormality type corresponding to the suspension abnormality information, the vehicle test process is screened, thereby improving the recognition accuracy and efficiency of the vehicle test process.

[0081] Optionally, based on the suspension structure abnormality information of the user vehicle and the suspension abnormality structure area of ​​the user vehicle, the initial suspension test plan is tested and adjusted to obtain a target suspension test plan for the user vehicle, including: identifying the structural difference type corresponding to each structural difference information, and based on each target unit structure range 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 abnormality information caused by each structural difference range through a suspension structure abnormality identification network; querying each associated vehicle test process corresponding to the associated vehicle abnormality information and the associated vehicle test target corresponding to each associated vehicle test process in a suspension test database, and generating a target suspension test process for the user vehicle based on each vehicle test process and each associated vehicle test process through a process optimization processing strategy; generating a suspension test target for 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 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 each target 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, identifies the associated vehicle abnormality information caused by each structural difference range through the suspension structure abnormality recognition network. The suspension structure abnormality recognition network is an artificial neural network based on reinforcement learning, and the artificial neural network is a suspension structure abnormality recognition network obtained by a large amount of training through each sample unit structural range, sample structural difference type, and sample associated vehicle abnormality information. The associated vehicle abnormality information is the influence information of each structural difference information on the vehicle suspension abnormality, or the vehicle suspension abnormality information generated by the structural difference information.

[0083] Then, the terminal queries the associated vehicle test processes corresponding to the associated vehicle abnormal information and the associated vehicle test targets corresponding to the associated vehicle test processes in the suspension test database, and generates the target suspension test process of the user vehicle through the process optimization processing strategy based on the vehicle test processes and the associated vehicle test processes. After that, the terminal generates the suspension test targets 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 uses the target suspension test process and the suspension test targets of the target suspension test process as the target suspension test scheme of the user vehicle. Among them, the process optimization processing strategy is to identify each vehicle test method and vehicle test steps of each vehicle test process, and combine the same vehicle test method and vehicle test steps to obtain the target suspension test process, so that the suspension test targets corresponding to the obtained target suspension test process are one or more.

[0084] Based on the above scheme, after merging and optimizing the vehicle abnormal information and the vehicle test process related to the vehicle abnormal 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 a suspension test optimization example, such as Figure 2 As shown, the specific processing process includes the following steps:

[0086] Step S201, obtaining vehicle suspension feedback information of the user's vehicle and vehicle suspension structure detection information of the user's vehicle.

[0087] Step S202: Based on the vehicle suspension feedback information, the semantic recognition network is used to identify various suspension abnormality features of the user's vehicle and the degree of suspension abnormality of each suspension abnormality feature.

[0088] Step S203, based on the suspension abnormality information corresponding to each suspension abnormality feature of the user's vehicle, query each initial suspension abnormality information of the user's vehicle in the suspension abnormality database, and identify the abnormality degree range corresponding to each initial suspension abnormality information.

[0089] Step S204 , based on the suspension abnormality degree of each suspension abnormality feature and the abnormality degree range corresponding to each initial suspension abnormality information, the suspension abnormality information of the user's vehicle is screened in each initial suspension abnormality information.

[0090] Step S205 , based on the vehicle suspension structure detection information of the user vehicle, a vehicle suspension three-dimensional structure model of the user vehicle is generated, and a target vehicle suspension three-dimensional structure model of the user vehicle is searched in a database.

[0091] Step S206, based on the target vehicle suspension three-dimensional structure model, identifying the structural difference range in the vehicle suspension three-dimensional structure model and the structural difference information corresponding to each structural difference range, and using the structural difference information corresponding to all structural difference ranges as the suspension structure abnormality information of the user vehicle.

[0092] Step S207, splitting the vehicle suspension three-dimensional structural model of the user's vehicle into sub-three-dimensional structural models of each vehicle suspension structure, and in each sub-three-dimensional structural model of the vehicle suspension structure, respectively identifying the unit structure range corresponding to each suspension unit of each vehicle suspension structure through a three-dimensional structure 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 use each target unit structural range corresponding to each structural difference range as the abnormal suspension structure area of ​​the user's vehicle.

[0094] Step S209, identifying the suspension abnormality type corresponding to the suspension abnormality information, and based on the suspension abnormality type corresponding to the suspension abnormality information, querying each vehicle test process corresponding to the suspension abnormality information and the vehicle test target corresponding to each vehicle test process in the suspension test database.

[0095] Step S210: All vehicle test processes and vehicle test targets corresponding to all vehicle test processes are used as an initial suspension test plan for the user's vehicle.

[0096] Step S211, identifying the structural difference type corresponding to each structural difference information, and based on each target 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, identifying the associated vehicle abnormality information caused by each structural difference range through the suspension structure abnormality recognition network.

[0097] Step S212, in the suspension test database, query each associated vehicle test process corresponding to the associated vehicle abnormal information, and the associated vehicle test target corresponding to each associated vehicle test process, and based on each vehicle test process and each associated vehicle test process, generate a target suspension test process for the user vehicle through a process optimization processing strategy.

[0098] Step S213, based on the vehicle test targets of each vehicle test process and the associated vehicle test targets corresponding to each associated vehicle test process, generate the suspension test targets of the target suspension test process, and use the target suspension test process and the suspension test targets of the target suspension test process as the target suspension test plan for the user vehicle.

[0099] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0100] Based on the same inventive concept, the embodiment of the present application also provides a suspension test optimization device for implementing the suspension test optimization method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more suspension test optimization device embodiments provided below can refer to the limitations of the suspension test optimization method above, and will not be repeated here.

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

[0102] An acquisition module 310 is used to acquire vehicle suspension feedback information of a user vehicle and vehicle suspension structure detection information of the user vehicle, and based on the vehicle suspension feedback information, query the suspension abnormality information of the user vehicle in a suspension test database;

[0103] An identification module 320, configured to identify suspension structure abnormality information of the user vehicle based on the vehicle suspension structure detection information of the user vehicle, and identify a suspension abnormality structure area of ​​the user vehicle based on the suspension structure abnormality information 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 abnormality information of the user vehicle, and to perform test adjustment processing on the initial suspension test plan based on the suspension structure abnormality information of the user vehicle and the suspension abnormality structure area of ​​the user vehicle to obtain a target suspension test plan for the user vehicle.

[0105] Optionally, the acquisition module 310 is specifically configured to:

[0106] Based on the vehicle suspension feedback information, identifying each suspension abnormality feature of the user vehicle and the degree of suspension abnormality of each suspension abnormality feature through a semantic recognition network;

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

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

[0109] Optionally, the identification module 320 is specifically configured to:

[0110] Based on the vehicle suspension structure detection information of the user vehicle, a vehicle suspension three-dimensional structure model of the user vehicle is generated, and a target vehicle suspension three-dimensional structure model of the user vehicle is searched in a 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 of the structural difference ranges are identified, and the structural difference information corresponding to all the structural difference ranges is used as the suspension structure abnormality information of the user vehicle.

[0112] Optionally, the identification module 320 is specifically configured to:

[0113] The vehicle suspension three-dimensional structure model of the user vehicle is split into three-dimensional sub-structure models of each vehicle suspension structure, and in each three-dimensional sub-structure model of the vehicle suspension structure, a unit structure range corresponding to each suspension unit of each vehicle suspension structure is respectively identified by a three-dimensional structure 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, each 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 used as the abnormal suspension structure area of ​​the user vehicle.

[0115] Optionally, the adjustment module 330 is specifically configured to:

[0116] Identify the suspension abnormality type corresponding to the suspension abnormality information, and based on the suspension abnormality type corresponding to the suspension abnormality information, query each vehicle test process corresponding to the suspension abnormality information and the vehicle test target corresponding to each vehicle test process in a suspension test database;

[0117] All vehicle test processes and vehicle test targets corresponding to all vehicle test processes are used as the initial suspension test plan for the user vehicle.

[0118] Optionally, the adjustment module 330 is specifically configured to:

[0119] Identifying the structural difference type corresponding to each of the structural difference information, and based on each target 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, identifying the associated vehicle abnormality information caused by each structural difference range through a suspension structure abnormality identification network;

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

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

[0122] Each module in the above suspension test optimization device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0123] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 4As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a suspension test optimization method is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0124] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0125] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps corresponding to the suspension test optimization method when executing the computer program.

[0126] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps corresponding to the suspension test optimization method are implemented.

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

[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, stored data, displayed data, 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 relevant data must comply with relevant regulations.

[0129] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present 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), magnetoresistive 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. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0130] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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 above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A suspension test optimization method, characterized in that: The method comprises: Acquire vehicle suspension feedback information of a user vehicle and vehicle suspension structure detection information of the user vehicle, and query suspension abnormality information of the user vehicle in a suspension test database based on the vehicle suspension feedback information; Based on the vehicle suspension structure detection information of the user vehicle, identifying the suspension structure abnormality information of the user vehicle, and based on the suspension structure abnormality information of the user vehicle, identifying the suspension abnormality structure area of ​​the user vehicle; Based on the suspension abnormality information of the user vehicle, an initial suspension test plan of the user vehicle is generated, and based on the suspension structure abnormality information of the user vehicle and the suspension abnormality structure area of ​​the user vehicle, the initial suspension test plan is tested and adjusted to obtain a target suspension test plan of the user vehicle.

2. The method according to claim 1, characterized in that The querying of the suspension abnormality information of the user vehicle in a suspension test database based on the vehicle suspension feedback information includes: Based on the vehicle suspension feedback information, identifying each suspension abnormality feature of the user vehicle and the degree of suspension abnormality of each suspension abnormality feature through a semantic recognition network; Based on the suspension abnormality information corresponding to each suspension abnormality feature of the user vehicle, query each initial suspension abnormality information of the user vehicle in a suspension abnormality database, and identify the abnormality degree range corresponding to each initial suspension abnormality information; Based on the suspension abnormality degree of each of the suspension abnormality features and the abnormality degree range corresponding to each of the initial suspension abnormality information, the suspension abnormality information of the user vehicle is screened in each of the initial suspension abnormality information.

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

4. The method according to claim 3, characterized in that The step of identifying the abnormal suspension structure area of ​​the user vehicle based on the abnormal suspension structure information of the user vehicle includes: The vehicle suspension three-dimensional structure model of the user vehicle is split into three-dimensional sub-structure models of each vehicle suspension structure, and in each three-dimensional sub-structure model of the vehicle suspension structure, a unit structure range corresponding to each suspension unit of each vehicle suspension structure is respectively identified by a three-dimensional structure 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, each 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 used as the abnormal suspension structure area of ​​the user vehicle.

5. The method according to claim 1, characterized in that The generating an initial suspension test plan for the user vehicle based on the suspension abnormality information of the user vehicle comprises: Identify the suspension abnormality type corresponding to the suspension abnormality information, and based on the suspension abnormality type corresponding to the suspension abnormality information, query each vehicle test process corresponding to the suspension abnormality information and the vehicle test target corresponding to each vehicle test process in a suspension test database; All vehicle test processes and vehicle test targets corresponding to all vehicle test processes are used as the initial suspension test plan for the user vehicle.

6. The method according to claim 4, characterized in that The step of performing test adjustment processing on the initial suspension test plan based on the suspension structure abnormality information of the user vehicle and the suspension abnormal structure area of ​​the user vehicle to obtain a target suspension test plan of the user vehicle includes: Identifying the structural difference type corresponding to each of the structural difference information, and based on each target 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, identifying the associated vehicle abnormality information caused by each structural difference range through a suspension structure abnormality identification network; In the suspension test database, query each associated vehicle test process corresponding to the associated vehicle abnormal information and the associated vehicle test target corresponding to each associated vehicle test process, and generate a target suspension test process of the user vehicle through a process optimization processing strategy based on each vehicle test process and each associated vehicle test process; Based on the vehicle test targets of each of the vehicle test processes and the associated vehicle test targets corresponding to each of the associated vehicle test processes, the suspension test targets of the target suspension test process are generated, and the target suspension test process and the suspension test targets of the target suspension test process are used as the target suspension test scheme for the user vehicle.

7. A suspension test optimization device, characterized in that: The device comprises: An acquisition module, used to acquire vehicle suspension feedback information of a user's vehicle and vehicle suspension structure detection information of the user's vehicle, and query suspension abnormality information of the user's vehicle in a suspension test database based on the vehicle suspension feedback information; an identification module, configured to identify suspension structure abnormality information of the user vehicle based on vehicle suspension structure detection information of the user vehicle, and to identify a suspension abnormality structure area of ​​the user vehicle based on the suspension structure abnormality information of the user vehicle; An adjustment module is used to generate an initial suspension test plan for the user vehicle based on the suspension abnormality information of the user vehicle, and to perform test adjustment processing on the initial suspension test plan based on the suspension structure abnormality information of the user vehicle and the suspension abnormality structure area of ​​the user vehicle to obtain a target suspension test plan for the user vehicle.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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