A vehicle part matching method and system based on VIN code analysis

Abstract

The application relates to the technical field of vehicle part maintenance, in particular to a vehicle part matching method and system based on VIN code analysis, which comprises the following steps: a VIN code analysis module is used for segmentally analyzing the VIN code of a vehicle to be matched, and vehicle configuration information with ambiguity eliminated is generated; a part matching module is used for matching candidate part configuration information sets in an engineering database according to a candidate part matching strategy; an assembly constraint checking module is used for screening out unusable parts that do not satisfy preset reference conditions according to an assembly constraint checking strategy, and obtaining available configuration information sets; a part assessment module is used for analyzing historical maintenance, engineering change frequency and vehicle consistency data of the vehicle according to a confidence assessment strategy, and determining the confidence weight of the available configuration information sets; and a part matching output module is used for analyzing the confidence weight value according to a matching result output strategy, and generating part matching information. The application has the effects of reducing VIN code configuration ambiguity caused by engineering changes and improving part matching accuracy.

Description

Technical Field

[0001] This application relates to the field of vehicle parts maintenance technology, and in particular to a vehicle parts matching method and system based on VIN code parsing. Background Technology

[0002] VIN code parsing component matching technology is an intelligent technical solution adapted to automotive after-sales repair scenarios. It is designed to address the problem of component mismatch caused by the complex and ever-changing engineering configuration of vehicles, as well as the pain points of low accuracy and poor reliability of traditional VIN code matching methods. It helps to improve the accuracy of component matching and repair efficiency, and promotes the digital upgrade of automotive after-sales services.

[0003] In related technologies, existing aftermarket parts matching systems generally use the VIN code as a deterministic index of vehicle configuration. By default, there is a one-to-one correspondence between the VIN and the vehicle's engineering configuration. The system directly locates a single configuration and matches the corresponding parts through structured parsing of the VIN code. This solution can achieve rapid parts lookup under ideal conditions and has found some application in the highly standardized aftermarket scenarios for new cars.

[0004] Regarding the aforementioned technologies, existing VIN code matching schemes suffer from several issues. Due to factors such as engineering changes, cross-batch production, regionally differentiated configurations, optional components, and historical modifications, the information expressed by the VIN is insufficient to uniquely determine the vehicle's true assembly status. VIN parsing results often correspond to multiple potential engineering configuration branches, and components under different configuration branches have mutually exclusive, replacement, or assembly constraints. Existing systems lack technical mechanisms to identify and resolve VIN configuration ambiguities, which can easily lead to component mismatches or omissions, hindering the improvement of after-sales service accuracy and customer satisfaction. Summary of the Invention

[0005] To reduce the impact of VIN code matching ambiguities on parts repair and allocation queries and improve the accuracy of vehicle parts matching, this application provides a vehicle parts matching method and system based on VIN code parsing.

[0006] Firstly, this application provides a vehicle parts matching method and system based on VIN code parsing:

[0007] A vehicle parts matching method and system based on VIN code parsing, comprising:

[0008] The VIN code parsing module uses the configured VIN structured parsing strategy to segment and parse the VIN code of the vehicle to be matched, and generates vehicle configuration information that eliminates VIN code ambiguity.

[0009] The component matching module is configured with a candidate component matching strategy and is connected to the VIN code parsing module. It is used to match the candidate component configuration information set corresponding to the vehicle configuration information in the preset engineering database.

[0010] The assembly constraint verification module is configured with an assembly constraint verification strategy and is connected to the component matching module. It is used to analyze the mutual exclusion, replacement, and compatibility of each component candidate configuration information set to filter out unusable components that do not meet the preset benchmark conditions and obtain the usable configuration information set.

[0011] The parts evaluation module analyzes the vehicle's historical maintenance, engineering change frequency, and assembly consistency data using a configured confidence evaluation strategy to determine the confidence weight of the corresponding available configuration information set.

[0012] The component matching output module is configured with a matching result output strategy and is connected to the component evaluation module. It is used to analyze the confidence weight value and generate the component matching information with the highest confidence weight.

[0013] By adopting the above technical solution, data connections are established between multiple modules, and each module is configured with its own exclusive strategy to work collaboratively. This sequentially completes VIN code parsing and disambiguation, matching of candidate component configuration information sets, assembly constraint verification, confidence weight evaluation, and output of optimal matching information, forming a full-process vehicle component matching system. This helps to resolve VIN code configuration ambiguities, solve the problem of component mismatch and omission caused by the lack of ambiguity identification and resolution mechanisms in existing systems, improve the accuracy and reliability of vehicle component matching, adapt to the component matching needs of complex after-sales scenarios such as engineering changes, cross-batch production, and regionally differentiated configurations, and improve the operational efficiency of automotive after-sales maintenance.

[0014] Optionally, the VIN structured parsing strategy includes:

[0015] Collect the VIN code information of the vehicle to be matched and parse it in segments to determine the meaning of the vehicle configuration represented by each segment of the VIN code;

[0016] Based on the VIN code segmentation parsing results, a preset vehicle model attribute database is matched to extract vehicle configuration attributes, which include vehicle platform code, production time interval code, market area code, and powertrain type code.

[0017] Based on the extracted vehicle configuration attributes, VIN code ambiguity removal is performed using a preset ambiguity removal sub-strategy to generate vehicle configuration information in a structured data format, which is then output to the component matching module.

[0018] By adopting the above technical solutions, the VIN structured parsing strategy generates structured vehicle configuration information through segmented parsing, vehicle model attribute matching, and ambiguity elimination. This helps to accurately extract the core vehicle configuration attributes represented in the VIN code, standardize the VIN code parsing process and data output format, reduce information ambiguity in the VIN code from the parsing stage, provide standardized and highly reliable vehicle configuration data for subsequent component matching, and improve the accuracy of basic data in subsequent component matching stages.

[0019] Optionally, the ambiguity elimination sub-strategy includes:

[0020] The historical maintenance data of the users of the vehicles to be matched is analyzed to determine the users' historical maintenance selection preferences under the same configuration attributes of the parts;

[0021] Based on historical maintenance selection preference information, similar subsets of vehicle configuration attributes generated by VIN code parsing are marked, and the priority weights of the corresponding configuration attribute subsets are adjusted by a preset adjustment ratio.

[0022] The similar subsets of vehicle configuration attributes are reordered according to the adjusted priority weights to generate vehicle configuration information that eliminates VIN configuration ambiguities caused by user-personalized configurations.

[0023] By adopting the above technical solution, the ambiguity elimination sub-strategy relies on mining user historical maintenance data to identify maintenance selection preferences under the same configuration attributes. After marking similar subsets of vehicle configuration attributes, adjusting priority weights, and reordering, it specifically resolves VIN configuration ambiguities caused by user-personalized configurations and historical modifications. This helps to make the vehicle configuration information output by VIN parsing more consistent with the actual use and assembly status of the vehicle, avoiding the problem of ambiguous configuration attributes caused by personalized configurations from the source, and generating standardized and unambiguous vehicle configuration data. This provides a highly reliable basis for the subsequent component matching module to accurately match the candidate configuration information set of parts, further enhancing the accuracy and adaptability of the VIN code parsing process.

[0024] Optionally, the candidate component matching strategy includes:

[0025] The system queries the engineering rule base based on preset configuration attributes to match the engineering configuration rule set of the corresponding vehicle platform.

[0026] Multiple potential engineering configuration branches are generated based on the set of engineering configuration rules, and each engineering configuration branch corresponds to a complete parts assembly list.

[0027] The generated set of candidate configuration information for multiple components is output to the assembly constraint verification module.

[0028] By adopting the above technical solution, the candidate component matching strategy, through querying the engineering rule base, matching the engineering configuration rule set, and generating potential engineering configuration branches, helps to accurately match the engineering configuration rules of the corresponding vehicle platform, fully cover the potential engineering configuration branches generated by various factors of the vehicle, avoid the missing parts matching caused by configuration branch omissions, provide a complete component candidate configuration basis for assembly constraint verification, and improve the comprehensiveness of the component candidate configuration information set.

[0029] Optionally, the assembly constraint verification strategy includes:

[0030] Collect the component assembly list from the candidate configuration set for each project and match it with the component constraint rule database;

[0031] The mutual exclusion relationship between components is verified based on the component constraint rule database. When mutually exclusive components are detected to exist in the same configuration branch at the same time, a configuration branch removal instruction is triggered.

[0032] Based on the component constraint rule database, the component replacement rules are validated. When a replaceable component is detected, the replacement relationship is marked and the compatible configuration branch is retained.

[0033] The component version compatibility is verified based on the component constraint rule database. When version incompatibility is detected, the configuration branch is removed.

[0034] The regional adaptability is verified based on the component constraint rule database. When a regional mismatch is detected, the configuration branch is removed.

[0035] By adopting the above technical solution, the assembly constraint verification strategy matches the component constraint rule database, verifies the mutual exclusion relationship, replacement rules, version compatibility, and regional adaptability of components item by item, and performs corresponding elimination and marking operations. This helps to achieve full-dimensional consistency verification of component assembly constraints, accurately screen out unusable configuration branches that do not meet the engineering assembly requirements, avoid matching errors caused by mutual exclusion, incompatibility, and regional incompatibility of components, and improve the engineering adaptability of the available configuration information set.

[0036] Optionally, the confidence assessment strategy includes:

[0037] Collect historical maintenance records from the database and count the frequency of each engineering configuration candidate set in historical maintenance scenarios;

[0038] Collect the engineering change frequency database and obtain the number of engineering changes corresponding to each engineering configuration candidate set;

[0039] Collect the loading consistency database and obtain the matching degree between the candidate configuration set of each project and the actual loading record;

[0040] Based on the frequency of occurrence, number of engineering changes, and matching degree, a confidence score value for each engineering configuration candidate set is calculated using a confidence scoring model.

[0041] By adopting the above technical solutions, the confidence assessment strategy collects assessment indicators from multiple databases and conducts quantitative analysis based on the frequency of historical maintenance, the number of engineering changes, and the consistency matching degree of vehicle assembly. This helps to achieve multi-dimensional quantitative assessment of the available configuration information set, get rid of the subjective judgment limitations of confidence assessment, improve the scientificity and objectivity of the confidence assessment of the available configuration information set, and provide accurate and reliable assessment basis for optimal configuration selection.

[0042] Optionally, the confidence score model is calculated using the following formula:

[0043] ;

[0044] in, Configure confidence scores for the candidate set for the project. The frequency of this configuration candidate set in historical maintenance records. The maximum frequency of occurrence across all configuration candidate sets. The number of project changes corresponding to this configuration candidate set. The maximum number of engineering changes across all configuration candidate sets. The matching degree between this configuration candidate set and the actual vehicle installation records, This represents the maximum matching degree. , , These are the preset weighting coefficients for frequency of occurrence, number of engineering changes, and matching degree, respectively.

[0045] By adopting the above technical solution, the confidence score model introduces weighted coefficients to differentiate the evaluation indicators. It combines the proportion of historical repair frequency, the reverse proportion of engineering change frequency, and the proportion of vehicle assembly consistency to construct a formula for quantitative calculation. This helps to comprehensively consider the impact of various indicators on parts matching, realize the numerical and accurate expression of the confidence of available configuration information set, make the confidence score results more in line with the actual needs of automotive aftermarket parts matching, and improve the intuitiveness of confidence comparison between different configuration sets.

[0046] Optional, also includes:

[0047] The 3D assembly structure verification module is configured with an assembly structure reverse verification strategy and is connected to the component matching output module. It is used to perform reverse verification between the matched component set and the 3D assembly structure data to determine the virtual assembly verification result.

[0048] A comparative analysis is conducted based on the virtual assembly verification results and the available configuration information set to determine the available configuration information set that meets the assembly requirements.

[0049] By adopting the above technical solution, through spatial interference detection of component sets and engineering assembly logic verification, matching anomalies are marked and verification reports are generated. This helps to verify the rationality of component matching results from both physical assembly and engineering logic dimensions, making up for the shortcomings of screening configurations solely through confidence assessment, avoiding actual assembly problems caused by spatial assembly conflicts and assembly logic contradictions, and improving the actual assemblability of component matching results.

[0050] Optionally, the reverse verification strategy for the assembly structure includes:

[0051] Collect the matched set of parts and query the 3D assembly structure database;

[0052] Based on the 3D assembly structure database, obtain the spatial location information and assembly connection relationship of each component;

[0053] A virtual spatial interference analysis is performed on the spatial positions of each component in the component set. When spatial interference occurs, the marker matching is abnormal.

[0054] The assembly connection relationship of each component in the component set is logically verified. When an assembly logic conflict is detected, a matching anomaly is marked.

[0055] Based on the interference detection and logic verification results, a matching rationality verification report is generated and output.

[0056] By adopting the above technical solutions, the assembly rationality verification results are systematically organized, key information such as verification anomalies and qualification criteria are clearly marked, and verification reports are output. This helps to improve the traceability of component matching results and verification process. At the same time, it provides actual abnormal data support for the optimization of strategies such as VIN structured parsing and assembly constraint verification, and realizes continuous iterative optimization of the vehicle component matching system.

[0057] Secondly, this application provides a vehicle parts matching method and system based on VIN code parsing, employing the following technical solution:

[0058] A vehicle parts matching method and system based on VIN code parsing, comprising:

[0059] The VIN code parsing step involves segmenting and parsing the VIN code of the vehicle to be matched using the configured VIN structured parsing strategy, and generating vehicle configuration information that eliminates VIN code ambiguity.

[0060] The component matching step is configured with a candidate component matching strategy, which is connected to the VIN code parsing module and is used to match the component candidate configuration information set corresponding to the vehicle configuration information in the preset engineering database.

[0061] The assembly constraint verification step is configured with an assembly constraint verification strategy, which is connected to the component matching module. It is used to analyze the mutual exclusion, substitution, and compatibility of each component candidate configuration information set to filter out unusable components that do not meet the preset benchmark conditions and obtain the usable configuration information set.

[0062] The parts evaluation step analyzes the vehicle's historical maintenance, engineering change frequency, and assembly consistency data using a confidence evaluation strategy to determine the confidence weight of the corresponding available configuration information set.

[0063] The component matching output step is configured with a matching result output strategy, which is connected to the component evaluation module to analyze the confidence weight value and generate the component matching information output with the highest confidence weight.

[0064] By adopting the above technical solutions, the technical solutions of the vehicle parts matching system are transformed into standardized and hierarchical operation steps. Each step is precisely adapted to the system modules and corresponding strategies and is promoted in sequence. This helps to implement the closed-loop system of parts matching into a practical work process, ensure the technical consistency of the method and the system, effectively reproduce the parts matching technical effect of the system, and improve the standardization and operability of parts matching operations in automotive aftermarket scenarios.

[0065] In summary, this application includes at least one of the following beneficial technical effects:

[0066] 1. Establish data connections between multiple modules and configure exclusive strategies for collaborative work. Sequentially complete VIN code parsing and disambiguation, matching of candidate component configuration information sets, assembly constraint verification, confidence weight evaluation, and output of optimal matching information to form a full-process vehicle component matching system. This helps to resolve VIN code configuration ambiguities, solve the problem of component mismatch and omission caused by the lack of ambiguity identification and resolution mechanisms in existing systems, improve the accuracy and reliability of vehicle component matching, adapt to the component matching needs of complex after-sales scenarios such as engineering changes, cross-batch production, and regionally differentiated configurations, and improve the operational efficiency of automotive after-sales maintenance.

[0067] 2. The ambiguity elimination sub-strategy combines user historical maintenance data to mine maintenance selection preferences. Through similar configuration subset labeling, confidence weight adjustment, and candidate configuration set reordering, ambiguity elimination is completed. This helps to resolve VIN configuration ambiguities caused by user personalized configurations and historical modifications, making vehicle configuration information match the actual assembly status, further improving the accuracy of vehicle configuration information, and making up for the VIN code's inadequacy in expressing personalized configuration information.

[0068] 3. The assembly constraint verification strategy matches the component constraint rule database and verifies the mutual exclusion relationship, replacement rules, version compatibility, and regional adaptability of components item by item, and performs corresponding elimination and marking operations. This helps to achieve full-dimensional consistency verification of component assembly constraints, accurately screen out unusable configuration branches that do not meet the engineering assembly requirements, avoid matching errors caused by mutual exclusion, incompatibility, and regional incompatibility of components, and improve the engineering adaptability of the available configuration information set. Attached Figure Description

[0069] Figure 1 This is a schematic diagram of the system module connections in this application.

[0070] Figure 2 This is a flowchart of steps S100 to S102 in this application.

[0071] Figure 3 This is a flowchart of steps S1021 to S1023 in this application.

[0072] Figure 4 This is a flowchart of steps S200 to S202 in this application.

[0073] Figure 5 This is a flowchart of steps S300 to S304 in this application.

[0074] Figure 6 This is a flowchart of steps S400 to S403 in this application.

[0075] Figure 7 This is a flowchart of steps S500 to S504 in this application. Detailed Implementation

[0076] To make the purpose, technical solution, and advantages of this application clearer, the following description is provided in conjunction with the appendix. Figure 1-7 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0077] The embodiments of the present invention will now be described in further detail with reference to the accompanying drawings.

[0078] This application discloses a vehicle parts matching method and system based on VIN code parsing. Through the cooperation of multiple modules, it sequentially completes VIN code parsing disambiguation, matching of candidate configuration information sets for parts, assembly constraint verification, confidence weight evaluation, and output of optimal matching information, forming a complete vehicle parts matching system. This helps to resolve VIN code configuration ambiguities, solves the problem of parts mismatch and omission caused by the lack of ambiguity identification and resolution mechanisms in existing systems, improves the accuracy and reliability of vehicle parts matching, adapts to the parts matching needs of complex after-sales scenarios such as engineering changes, cross-batch production, and regionally differentiated configurations, and improves the operational efficiency of automotive after-sales maintenance.

[0079] Reference Figure 1 The vehicle parts matching method and system based on VIN code parsing includes the following modules:

[0080] The VIN code parsing module uses the configured VIN structured parsing strategy to segment and parse the VIN code of the vehicle to be matched, and generates vehicle configuration information that eliminates VIN code ambiguity.

[0081] It should be further explained that the VIN code of the vehicle to be matched is a unique vehicle identification code, encompassing core configuration-related information such as vehicle platform, manufacturing process, and market region. It serves as the raw input data for vehicle configuration parsing. Segmented parsing is a processing method that analyzes characters in different segments of the VIN code according to their encoding rules. Different segments correspond to different dimensions of the vehicle's configuration attributes. The vehicle configuration information, after parsing and ambiguity processing, is structured and unambiguous vehicle configuration data that can be directly used for subsequent matching operations with the engineering database.

[0082] The component matching module is configured with a candidate component matching strategy and is connected to the VIN code parsing module. It is used to match the candidate component configuration information set corresponding to the vehicle configuration information in the preset engineering database.

[0083] The pre-defined engineering database is a structured database that stores engineering configuration and component association data for all vehicle categories. It covers vehicle engineering configuration rules corresponding to different vehicle platforms, production batches, and market regions, as well as component assembly lists, component model parameters, and component compatibility relationships under each engineering configuration. It supports multi-dimensional and precise queries based on vehicle configuration information and can be updated in real time according to engineering change information. The component candidate configuration information set is obtained by matching vehicle configuration information from the engineering database. It contains a set of component configuration data for all potential engineering configuration branches of the vehicle, and each configuration branch corresponds to a complete set of component assembly information.

[0084] The assembly constraint verification module is configured with an assembly constraint verification strategy and is connected to the component matching module. It is used to analyze the mutual exclusion, substitution, and compatibility of each component candidate configuration information set to filter out unusable components that do not meet the preset benchmark conditions and obtain the usable configuration information set.

[0085] The preset baseline conditions are based on vehicle engineering assembly specifications and represent the core constraints that component assembly must meet. These constraints cover multiple dimensions of engineering assembly standards, including mutual exclusion constraints, replacement rule constraints, version compatibility constraints, and regional adaptation constraints. Unusable components refer to those that do not meet the preset baseline conditions and exhibit issues such as mutual exclusion, version incompatibility, or regional incompatibility within the same configuration branch, making normal engineering assembly impossible. The usable configuration information set is the set of component configuration data that conforms to the engineering assembly logic, after assembly constraint verification and the removal of all unusable components and their corresponding invalid configuration branches.

[0086] The parts evaluation module analyzes the vehicle's historical maintenance, engineering change frequency, and assembly consistency data using a configured confidence evaluation strategy to determine the confidence weight of the corresponding available configuration information set.

[0087] Historical maintenance data for vehicles includes records of parts replacement and configuration matching for the vehicle to be matched and similar vehicles in after-sales maintenance scenarios, covering the actual maintenance application frequency of each engineering configuration branch. Engineering change frequency data includes the number of engineering changes, change time, and change content for each vehicle engineering configuration branch.

[0088] Vehicle assembly consistency data refers to the matching degree between each engineering configuration branch and the actual vehicle production and assembly records, representing the degree of fit between the configuration branch and the actual assembly state of the vehicle. Confidence weight is a quantitative weight value assigned to each component configuration branch in the available configuration information set after multi-dimensional data quantitative analysis. The higher the weight value, the higher the degree of fit between the configuration branch and the actual assembly state of the vehicle.

[0089] The parts matching output module is configured with a matching result output strategy and is connected to the parts evaluation module. It is used to analyze the confidence weight values ​​and generate the parts matching information output with the highest confidence weight.

[0090] The confidence weight value is a quantitative evaluation value assigned by the parts evaluation module to each configuration branch in the available configuration information set. It is the core basis for selecting the optimal parts configuration branch. The parts matching information with the highest confidence weight is the parts configuration information with the highest degree of fit to the actual assembly state of the vehicle, which is selected based on the weight value comparison. It includes complete information such as parts model, specifications, assembly requirements, and adaptation instructions, and can be directly applied to parts allocation and replacement operations in automotive aftermarket repair.

[0091] Reference Figure 1 The vehicle parts matching system based on VIN code parsing also includes:

[0092] The 3D assembly structure verification module is configured with an assembly structure reverse verification strategy. It is connected to the component matching output module and is used to perform reverse verification between the matched component set and the 3D assembly structure data to determine the virtual assembly verification result.

[0093] A comparative analysis is conducted based on the virtual assembly verification results and the available configuration information set to determine the available configuration information set that meets the assembly requirements.

[0094] The 3D assembly structure verification module is a functional module that verifies the physical assembly feasibility of component matching results. It is a supplementary and upgraded link to the assembly constraint verification. Its core solution is to solve the problem that the engineering rules verification passes but there are conflicts in the actual physical assembly. Automakers rely on this type of logic to complete the assembly verification of the whole vehicle 3D digital model. For example, the whole vehicle 3D assembly verification of the Volkswagen MQB platform and the module 3D digital model verification of the Toyota TNGA architecture both use this core logic.

[0095] The assembly structure reverse verification strategy is a proprietary strategy based on the original 3D assembly structure data of the car manufacturer. It conducts virtual space interference detection and assembly logic verification on the matching parts set. The verification standard is completely consistent with the 3D digital model assembly standard of the car manufacturer's engineering design.

[0096] 3D assembly structure data refers to the three-dimensional digital assembly model data of the entire vehicle / each module built by the automaker. It covers core information such as the three-dimensional dimensions of parts, spatial installation positions, assembly connection relationships, and fit tolerances. For example, the 3D assembly data of the door module includes the spatial coordinates and assembly connection requirements of the window regulator, interior panel, and speakers. Virtual assembly verification results are the verification results obtained after performing 3D virtual assembly on the set of parts. They are divided into two categories: assembly qualified and assembly abnormal. Abnormal results include specific problem descriptions such as spatial interference and logical connection conflicts.

[0097] The available configuration information set that meets the assembly requirements is the configuration information set that, after verification by 3D virtual assembly and elimination of configuration branches with assembly anomalies, conforms to both engineering constraints and physical assembly requirements.

[0098] In practice, the 3D assembly structure verification module receives the set of matching parts and the corresponding set of available configuration information from the parts matching output module, calls the configured assembly structure reverse verification strategy, associates and matches the set of parts with the preset original factory 3D assembly structure data, performs full-dimensional 3D virtual assembly verification on the set of parts corresponding to each set of configuration information, and generates the corresponding virtual assembly verification results.

[0099] Subsequently, the virtual assembly verification results are compared and analyzed one by one with the original available configuration information set. Configuration branches with abnormal assembly results are eliminated, and only configuration branches with qualified virtual assembly verification are retained. These are then integrated to form the final available configuration information set that meets the assembly requirements, ensuring that the output component matching results not only comply with engineering rules but also have actual physical assembly feasibility.

[0100] Reference Figure 2 The VIN structured parsing strategy employs the following steps:

[0101] Step S100: Collect the VIN code information of the vehicle to be matched and perform segmented parsing to determine the meaning of the vehicle configuration represented by each segment of the VIN code;

[0102] This step is the foundational disassembly stage of VIN structured parsing. Its core function is to complete the segmentation of the VIN code and identify the configuration meaning of each segment according to standardized coding rules, providing segmented parsing results for subsequent vehicle model attribute extraction. The segmented parsing results refer to the configuration meaning list formed after each segment symbol is matched according to rules, which is the direct retrieval basis for matching with the vehicle model attribute database.

[0103] In practice, the system verifies the validity of the collected VIN code information, performs full code decomposition according to the segment division rules of the VIN code industry, calls the preset VIN code encoding rule library, performs one-to-one rule matching on the character / number combinations of each segment, accurately identifies the vehicle configuration representation meaning corresponding to each segment, and forms a structured segmented parsing result list. After completion, the list is synchronized to subsequent steps as the retrieval basis for vehicle model attribute extraction.

[0104] Step S101: Based on the VIN code segmentation parsing results, match the preset vehicle model attribute database to extract vehicle configuration attributes. Vehicle configuration attributes include vehicle platform code, production time interval code, market area code, and powertrain type code.

[0105] The pre-set vehicle attribute database is a structured database that stores the mapping relationship between VIN code segmentation parsing results and standardized configuration attribute codes, supporting accurate retrieval based on parsing results; core configuration attributes such as vehicle platform code and production time interval code are key standardized data for subsequent ambiguity elimination and parts matching.

[0106] In practice, the system uses the segmented parsing results of step S100 as search conditions to perform precise matching in the vehicle model attribute database, retrieves the corresponding standardized vehicle configuration attribute data, and extracts four core configuration attributes: vehicle platform code, production time interval code, market area code, and powertrain type code. The extracted attribute codes are then format-verified and standardized to ensure consistency with the coding rules of the subsequent engineering database. After completion, the core configuration attribute data is temporarily stored for subsequent ambiguity resolution processing.

[0107] Step S102: Based on the extracted vehicle configuration attributes, perform VIN code ambiguity removal processing using a preset ambiguity removal sub-strategy to generate vehicle configuration information in structured data format and output it to the component matching module.

[0108] The component matching module can directly interact and match with the engineering database based on the vehicle configuration information in a structured data format, which is integrated according to preset field specifications.

[0109] In practice, the system imports the core configuration attribute data extracted in step S101 into the preset ambiguity elimination sub-strategy, activates the ambiguity recognition logic, and accurately identifies the fuzzy points and multiple solutions in the configuration attributes. For the identified configuration ambiguities, targeted resolution processing is carried out according to the established processing rules of the ambiguity elimination sub-strategy. The disambiguated configuration attribute data is integrated according to the preset structured data fields and formats to generate unambiguous structured vehicle configuration information, which is output to the engineering configuration candidate set generation module in real time, providing accurate data basis for matching the component candidate configuration information set.

[0110] Reference Figure 3 The ambiguity elimination sub-strategy includes the following steps:

[0111] Step S1021: Analyze the user's historical maintenance data of the vehicle to be matched to determine the user's historical maintenance selection preference information under the same configuration attributes of parts;

[0112] User historical repair data is a complete and valid record of past after-sales repairs for the vehicle to be matched. Historical repair selection preference information is user preference data for component configurations and models under the same configuration attributes, which is the core reference for subsequent configuration subset labeling and weight adjustment.

[0113] In practice, the system uses the unique identifier of the vehicle to be matched as the search condition, retrieves all historical maintenance data from the preset vehicle maintenance file database, cleans and removes invalid and duplicate records from the data, and uses the core configuration attributes extracted in step S101 as the filtering condition to filter out the parts replacement and configuration matching records under the same configuration attributes. The system then performs statistical analysis on the filtered valid records to extract the user's parts selection preference under the configuration attribute, forming standardized historical maintenance selection preference information, which is synchronized to subsequent steps for use in configuration subset marking.

[0114] Step S1022: Based on historical maintenance selection preference information, mark the similar subsets of vehicle configuration attributes generated by VIN code parsing, and adjust the priority weight of the corresponding configuration attribute subsets by a preset adjustment ratio;

[0115] Among them, the similar subset of vehicle configuration attributes refers to multiple sets of vehicle configuration attributes that are consistent in core configuration attributes but differ in sub-configurations, resulting from factors such as user-personalized configuration and historical modifications after VIN code segmentation and parsing. For example, the same VIN code corresponding to the basic configuration of 1.4T engine + cold northern region can generate two similar subsets: ordinary halogen headlights and optional LED headlights.

[0116] The preset adjustment ratio is a fixed coefficient determined by the system based on after-sales maintenance data statistics. It is used to differentiate the priority of subsets, such as increasing the weight of preference-matching subsets by 20% and appropriately decreasing the weight of non-preference subsets. The priority weight is used to characterize the degree to which each configuration subset matches the user's actual usage preferences.

[0117] In practice, the system compares the historical repair selection preference information extracted in step S1021 with the similar subsets of all vehicle configuration attributes generated by VIN parsing from multiple dimensions. Taking the Toyota Camry based on the TNGA-K architecture as an example, if the user frequently selects the high-end intelligent vehicle system + automatic air conditioning combination in the historical repair records, the system will accurately identify the configuration attribute subset containing this combination in the similar subset and assign it a unique label.

[0118] Subsequently, the system retrieves the preset adjustment ratio, performs positive weight adjustment on the marked preference subset, and appropriately lowers the weight of the subset that does not match the preference. By adjusting the weight differently, the system highlights the priority of the configuration attributes that the user has actually used, providing a quantitative basis for subsequent ranking.

[0119] Step S1023: Reorder the similar subset of vehicle configuration attributes according to the adjusted priority weights to generate vehicle configuration information that eliminates VIN configuration ambiguity caused by user personalized configuration.

[0120] Re-sorting refers to the overall sorting of all similar subsets of vehicle configuration attributes according to their adjusted priority weights from high to low. The subset with the highest weight value is the configuration that best matches the user's actual assembly status. The vehicle configuration information is structured data compatible with the automotive manufacturer's engineering database format, integrated after sorting and filtering. It covers core fields such as vehicle platform code, powertrain type, market region code, and user preference configuration, and can be directly used for matching candidate configuration information sets of parts in the subsequent parts matching module.

[0121] In practice, the system extracts all similar subsets of vehicle configuration attributes after weight adjustment in step S1022 and sorts them according to priority weight. Taking the Volkswagen MQB platform Golf model as an example, if the subset with the highest weight after sorting is 1.4T engine + Southern region + optional panoramic sunroof, the system uses this subset as the core configuration basis, retains the high-weight subset as the primary reference for subsequent parts matching, and keeps the low-weight subset as an alternative configuration branch.

[0122] The system integrates the configuration attributes of the core subset according to a preset format to generate vehicle configuration information, completely eliminating VIN configuration ambiguities caused by user-personalized configurations and historical modifications, and ensuring that the configuration data output to the component matching module accurately matches the actual vehicle assembly status.

[0123] Reference Figure 4 The candidate component matching strategy includes the following steps:

[0124] Step S200: Based on the vehicle configuration attributes, query the engineering rule base to match the engineering configuration rule set of the corresponding vehicle model platform;

[0125] This step is the core of the rule retrieval process for matching candidate component configurations. Its main function is to establish a precise association between vehicle configuration attributes and the engineering rule base, providing the original factory engineering rule basis for the generation of potential engineering configuration branches in the future.

[0126] The system uses the extracted vehicle platform code, market area code and other core configuration attributes as the retrieval basis, and performs precise queries in the preset engineering rule library. Based on the engineering configuration rule mapping relationship of mainstream vehicle platforms such as Volkswagen MQB and Toyota TNGA stored in the library, it matches the set of engineering configuration rules that are fully compatible with the target vehicle platform. This set covers all applicable engineering assembly specifications, parts standardization standards and configuration adaptation requirements under the corresponding vehicle platform.

[0127] Step S201: Generate multiple potential engineering configuration branches based on the set of engineering configuration rules, with each engineering configuration branch corresponding to a complete parts assembly list;

[0128] This step is the core of transforming abstract engineering rules into specific component configuration schemes. Its core function is to cover all potential configuration possibilities that may arise from engineering changes or cross-batch production of the vehicle, forming a standardized component assembly list.

[0129] Based on the matching set of engineering configuration rules, the system combines configuration attribute characteristics such as vehicle production time interval and market area to decompose and generate multiple potential engineering configuration branches that meet the original factory rules. Each configuration branch corresponds to a complete and independent parts assembly list, which clearly marks the core information such as parts model, assembly specifications, fitting location and assembly quantity. For example, the MQB platform door module will generate two complete assembly lists for the low-end manual window regulator and the high-end electric one-touch window regulator.

[0130] Step S202: Output the generated multiple candidate configuration information sets of parts to the assembly constraint verification module.

[0131] The system extracts each engineering configuration branch from the candidate configuration information set of parts one by one, obtains the complete parts assembly list corresponding to each branch, and then associates and matches these lists with the preset parts constraint rule database to retrieve the original factory assembly constraint rules corresponding to each part in the list. This database stores core constraint rules such as high and low configuration mutual exclusion, version compatibility, and regional adaptation for each vehicle platform, which serve as the unified basis for subsequent verification in various dimensions.

[0132] Reference Figure 5 The assembly constraint verification strategy includes the following steps:

[0133] Step S300: Collect the component assembly list from each engineering configuration candidate set and match it with the component constraint rule database;

[0134] Parts regional compatibility refers to the original equipment manufacturer's (OEM) requirements for parts regional compatibility based on the climate, regulations, and usage scenarios of different market regions. Vehicle configuration codes in each region must be matched with parts that meet local requirements. Regional mismatch means that the parts configuration in the configuration branch is inconsistent with the OEM's compatibility requirements for the market region corresponding to the vehicle configuration code, and cannot meet local usage and regulatory requirements.

[0135] In practice, the system extracts the market area identifier from the vehicle configuration code and combines it with the regional adaptation rules corresponding to the configuration code in the parts constraint rule database to accurately check the parts assembly list of each configuration branch. It verifies whether the parts in the list meet the original factory adaptation requirements of the market area to which the vehicle belongs. If a regional mismatch is detected in a certain configuration branch, the system will directly remove the configuration branch to ensure that the parts configuration of the remaining candidate branches is highly consistent with the requirements of the actual use area of ​​the vehicle.

[0136] Step S301: Verify the mutual exclusion relationship between components based on the component constraint rule database. When mutually exclusive components are detected to exist in the same configuration branch at the same time, trigger the configuration branch removal instruction.

[0137] The mutual exclusion relationship of components refers to the combination of components that cannot be simultaneously installed in the same location or system of a vehicle, as specified in the original configuration code. This relationship is determined by the automaker's engineering design department based on the assembly logic and is a core assembly constraint requirement. For example, under the MQB platform low-end door configuration code 5G4837015D, the low-end door sheet metal and the configuration of six speakers are mutually exclusive components.

[0138] The configuration branch removal command is a deletion command generated by the system for invalid configuration branches with mutual exclusion issues. Once triggered, the configuration branch will be directly removed from the project configuration candidate set.

[0139] In practice, the system checks all component combinations in the component assembly list under the same configuration code one by one according to the matched original factory constraint rules. It identifies whether there are mutually exclusive components specified by the original factory in the list. If the system detects that the low-end door configuration branch of the MQB platform, such as the 5G4837015D list, contains both the low-end door sheet metal and the six-speaker configuration, which are mutually exclusive components, the system will immediately generate a configuration branch removal instruction and directly remove the configuration branch with the engineering logic error from the candidate set to ensure that the remaining candidate branches all meet the original factory mutual exclusion constraint requirements.

[0140] Step S302: Validate the component replacement rules based on the component constraint rule database. When a replaceable component is detected, mark the replacement relationship and retain the compatible configuration branch.

[0141] Step S303: Verify the compatibility of component versions based on the component constraint rule database. When version incompatibility is detected, remove the configuration branch.

[0142] Step S304: Verify the regional adaptability based on the component constraint rule database. When a regional mismatch is detected, remove the configuration branch.

[0143] Parts regional compatibility refers to the original equipment manufacturer's (OEM) specific compatibility requirements for parts based on the climate conditions, regulations, standards, and usage scenarios of different market regions. These requirements are tied to the vehicle's market region code and are the core assembly constraints.

[0144] The configuration branch removal instruction is a deletion command generated by the system for invalid configuration branches with regional mismatch issues. Once triggered, it will directly remove the configuration branch from the engineering configuration candidate set. During execution, the system extracts the vehicle market region code based on the matched original equipment manufacturer (OEM) constraint rules and checks each component assembly list under the same configuration code to identify whether the components in the list meet the OEM adaptation requirements for the corresponding region. If a non-compliance is detected, the system will immediately generate a configuration branch removal instruction, directly removing the configuration branch with the regional adaptation error from the candidate set, ensuring that all remaining candidate branches meet the OEM regional adaptation constraints.

[0145] Reference Figure 6 The confidence assessment strategy includes the following steps:

[0146] Step S400: Collect historical maintenance record database and count the frequency of occurrence of each engineering configuration candidate set in historical maintenance scenarios;

[0147] The historical repair record database is a structured database storing all valid after-sales repair records for all models under the automaker. The data source is the actual repair work of authorized 4S stores and repair outlets of the automaker, covering core information such as vehicle VIN code, repair time, applicable engineering configuration branches, and parts replacement models. It is the core data carrier reflecting the actual after-sales adaptability of configuration branches. The frequency of occurrence of the engineering configuration candidate set in historical repair scenarios refers to the effective number of times each configuration branch has been actually matched and applied in past after-sales repairs. The higher the frequency, the stronger the universality and adaptability of the configuration branch in actual repair scenarios. For example, the historical repair frequency of the low-end air conditioning configuration branch of the Volkswagen MQB platform Sagitar is much higher than that of its high-end customized version configuration branch.

[0148] In practice, the system uses the original manufacturer's configuration code for each engineering configuration candidate set as the unique retrieval identifier. It performs a comprehensive and precise search of the historical repair record database, filtering out all after-sales repair records that match the configuration code. The system then counts the number of valid records found, while removing invalid records due to human error or non-original manufacturer repairs to ensure the accuracy of the frequency statistics. Finally, it obtains the historical repair frequency for each engineering configuration candidate set, providing a quantitative basis for subsequent confidence scoring in terms of practical after-sales application.

[0149] Step S401: Collect the engineering change frequency database and obtain the number of engineering changes corresponding to each engineering configuration candidate set;

[0150] The engineering change frequency database is a structured database that stores the full-cycle change information of engineering configuration branches for various vehicle platforms of automakers. It covers core information such as configuration code, change time, change reason, change content, and number of changes. It records all configuration adjustments made by the engineering design side due to process optimization, component upgrades, and regulatory adjustments during the process of vehicle mass production and iteration. The number of engineering changes corresponding to the engineering configuration candidate set refers to the total number of effective engineering changes for each configuration branch since mass production. The fewer the number of changes, the stronger the engineering design stability of that configuration branch. For example, the number of engineering changes for the center console configuration branch of the Toyota Camry basic version based on the TNGA-K architecture is much lower than that of its high-end personalized customization branch.

[0151] In practice, the system uses the original manufacturer's configuration code of each engineering configuration candidate set as the search condition, matches the corresponding configuration change record in the engineering change frequency database, and counts all valid engineering change records under that configuration code to obtain the total number of engineering changes for each engineering configuration candidate set. Simultaneously, the reasons for changes are categorized and labeled, distinguishing between minor changes such as process optimization and major changes such as core configuration adjustments, providing auxiliary data for subsequent confidence scoring indicators.

[0152] Step S402: Collect the loading consistency database and obtain the matching degree between the candidate configuration set of each project and the actual loading record;

[0153] The vehicle assembly consistency database is a structured database that stores the actual vehicle configuration information at the automaker's production end. It covers core information such as vehicle VIN code, production batch, actual assembly configuration code, and component models, serving as the core data carrier connecting engineering design configurations with actual production assembly. The matching degree between the engineering configuration candidate set and the actual assembly record refers to the proportion of the engineering design component list for each configuration branch that matches the actual assembly record of the same batch and model at the automaker's production end. A higher matching degree indicates a higher degree of fit between that configuration branch and the vehicle's actual original factory assembly state. For example, the vehicle assembly consistency matching degree of the standard configuration branch for the Volkswagen EA211 engine 1.4T is close to 100%.

[0154] In practice, the system uses the original factory configuration code of each engineering configuration candidate set as the core identifier and retrieves the actual vehicle installation records for the corresponding vehicle model platform and production batch from the vehicle installation consistency database. The system compares the design parts list of each engineering configuration candidate set with the actual installed parts list one by one, calculates the matching ratio, and obtains the vehicle installation consistency matching degree for each engineering configuration candidate set. Simultaneously, non-standard installation records resulting from trial assembly or special customized modifications at the production end are eliminated to ensure that the matching degree calculation results accurately reflect the original factory assembly fit of the configuration branch.

[0155] Step S403: Based on the frequency of occurrence, number of engineering changes, and matching degree, calculate the confidence score value of each engineering configuration candidate set using a confidence scoring model.

[0156] The confidence score model is a quantitative evaluation model built by comprehensively considering multi-dimensional data from after-sales maintenance, engineering design, and production assembly. Based on the actual needs of matching automotive aftermarket parts, the model assigns differentiated weights to three core indicators: frequency of historical repairs, number of engineering changes, and consistency of vehicle assembly, enabling numerical evaluation of each engineering configuration candidate set. The confidence score is a quantitative evaluation value obtained by the model after calculating each configuration branch. A higher score indicates a higher degree of credibility in matching the configuration branch with the actual assembly state of the vehicle, serving as the core quantitative basis for subsequently selecting the optimal parts configuration branch.

[0157] In practice, the system standardizes three core data points: the historical repair frequency (statistics from step S400), the number of engineering changes (obtained from step S401), and the vehicle assembly consistency matching degree (calculated from step S402), eliminating differences in the dimensions of each indicator. The standardized data is then imported into a pre-defined confidence scoring model. The model performs a comprehensive calculation according to pre-defined indicator weights to obtain a confidence score for each engineering configuration candidate set. Subsequently, the score is bound and stored one-to-one with the original factory configuration code of each configuration branch, providing a precise and quantifiable numerical reference for the subsequent parts matching output module to select the configuration branch with the highest confidence level.

[0158] The confidence score model is calculated using the following formula:

[0159] ;

[0160] in, Configure confidence scores for the candidate set for the project. The frequency of this configuration candidate set in historical maintenance records. The maximum frequency of occurrence across all configuration candidate sets. The number of project changes corresponding to this configuration candidate set. The maximum number of engineering changes across all configuration candidate sets. The matching degree between this configuration candidate set and the actual vehicle installation records, This represents the maximum matching degree. , , These are the preset weighting coefficients for frequency of occurrence, number of engineering changes, and matching degree, respectively.

[0161] Reference Figure 7 The reverse verification strategy for the assembly structure includes the following steps:

[0162] Step S500: Collect the matched set of parts and query the 3D assembly structure database;

[0163] The matched parts set is the complete set of parts details corresponding to the parts configuration with the highest confidence level selected by the parts matching output module. It includes core information such as parts model, specifications, and quantity, such as the combination of parts such as the window regulator, six speakers, and leather interior panels corresponding to the high-end doors of the Volkswagen MQB platform.

[0164] The 3D assembly structure database is a structured database that stores 3D assembly structure data of all types of vehicles and modules from automakers. The data in the database is updated synchronously with the 3D digital models from the automaker's engineering design end. It covers the 3D modeling data of each component, spatial assembly parameters, original factory assembly standards, etc. It is the core data basis for carrying out virtual assembly verification. For example, the database stores complete 3D assembly structure data of modules such as doors, center console, and engine compartment of various models on the MQB platform.

[0165] In practice, the system collects the set of matched parts after confidence screening from the parts matching output module, extracts the original factory model codes of all parts in the set, uses these codes as the core search conditions, performs a precise query in the 3D assembly structure database, retrieves the 3D assembly structure data of the whole vehicle / corresponding module that is completely corresponding to the set of parts, and performs validity verification on the retrieved data to ensure that the 3D assembly structure data is compatible with the parts model and vehicle platform, providing accurate three-dimensional data support for subsequent virtual assembly verification.

[0166] Step S501: Obtain the spatial location information and assembly connection relationship of each component based on the 3D assembly structure database;

[0167] Spatial location information refers to the core parameters of each component in the 3D assembly structure data, such as the three-dimensional spatial coordinates, mounting reference surface, and mating clearance requirements of the whole vehicle / module. It is the key basis for judging whether there is spatial interference between components, such as the three-dimensional mounting coordinates of the door speaker of the MQB platform and the mating clearance requirements with the door sheet metal.

[0168] Assembly connection relationships are the original factory assembly connection rules between various components in 3D assembly structure data. They include the connection method of components, the assembly sequence, and the model matching requirements of mating parts, such as the wiring harness connection method between the center console screen and the vehicle host in the Toyota TNGA-K architecture, and the snap-fit ​​assembly relationship between the air conditioning panel and the center console frame.

[0169] In practice, the system uses the 3D assembly structure data retrieved in step S500 to disassemble each component according to its original manufacturer's model number. It extracts the spatial coordinates, installation datum, and fit tolerances of each component, while simultaneously compiling and extracting the original manufacturer's assembly connection rules between components, forming a standardized list of spatial location information and an assembly connection relationship list. These two lists are then bound to the component set one-to-one, ensuring that the spatial and assembly parameters of each component accurately correspond, providing a clear basis for subsequent interference analysis and logic verification.

[0170] Step S502: Perform virtual spatial interference analysis on the spatial positions of each component in the component set. When spatial interference occurs, mark the matching anomaly.

[0171] Virtual spatial interference analysis refers to the analysis method that uses 3D modeling software to virtually assemble a set of parts, simulates the installation state of each part in a preset spatial position, and detects whether there are physical interference problems such as three-dimensional spatial overlap between parts or the fit clearance being less than the original factory requirements. It is a core part of the 3D assembly verification of car manufacturers.

[0172] Spatial interference refers to the problem in which two or more components overlap in three-dimensional space during virtual assembly, or the mating clearance does not meet the original design requirements, making actual physical assembly impossible. Examples of spatial interference include overlapping door trim panels and speaker mounting positions, and excessively small mating clearances between window regulators and door sheet metal. Matching anomalies are standardized identifiers provided by the system for sets of components with spatial interference issues. These identifiers include core information such as the model of the interfering component, the location of the interference, and the type of interference.

[0173] In practice, the system imports the spatial position information of each component extracted in step S501 into the 3D virtual assembly simulation system. Following the requirements of the original manufacturer's 3D assembly structure data, the system performs a 1:1 virtual spatial assembly of the component set. The simulation system automatically detects the spatial relationships between the components, determining if there are interference issues such as spatial overlap or insufficient clearance. If any spatial interference issue is detected, the system immediately marks the component set as an anomaly and records detailed information such as the model of the interfering component, the three-dimensional interference location, and the interference clearance value, providing a basis for subsequent troubleshooting.

[0174] Step S503: Perform logical verification on the assembly connection relationship of each component in the component set. When an assembly logic conflict is detected, mark the match as abnormal.

[0175] Assembly logic conflicts refer to inconsistencies between the assembly connection relationships of various components in the component set and the assembly rules in the original manufacturer's 3D assembly structure data. This leads to problems where the assembly process cannot be completed according to the original manufacturer's requirements. Typical examples of assembly logic conflicts include incompatibility between the wiring harness interface type of the air conditioning panel and the wiring harness interface of the vehicle's main unit, and conflicts between the assembly sequence of components and the original manufacturer's rules. This verification step focuses on verifying the rationality of the assembly logic connection of components, supplementing the physical space assembly verification through spatial interference analysis, and forming a complete virtual assembly verification system.

[0176] In practice, the system checks the assembly connection rules of each component in the component set one by one based on the original assembly connection relationship list extracted in step S501, focusing on verifying whether the connection method, interface model and assembly fit relationship of the components are consistent with the original rules.

[0177] If any assembly logic conflict is detected, such as the wiring harness interface of a certain model's central control screen not matching the vehicle's host interface model, the system will mark the set of parts as a matching anomaly and record in detail the conflicting part model, conflict type, original factory standard assembly rules, and other information. This information will be integrated with the spatial interference anomaly mark to form a complete list of matching anomaly information.

[0178] Step S504: Based on the interference detection and logic verification results, generate and output a matching rationality verification report.

[0179] The matching rationality verification report is a standardized report generated by the system after systematically organizing the virtual assembly verification results of the component set. The report covers core content such as basic information of the component set, spatial interference detection results, assembly logic verification results, overall verification conclusions, and details of abnormal issues. It serves as an important basis for judging whether the component matching results are feasible for actual assembly and also as reference data for automakers to optimize engineering configuration rules. The report output supports multiple formats, including system interface display and file export, adapting to different usage needs in after-sales maintenance and engineering design.

[0180] In practice, the system integrates the spatial interference detection results of step S502 with the assembly logic verification results of step S503, and classifies and organizes all verification data: for the set of parts that have passed verification, the overall verification conclusion is clearly marked as reasonable assembly; for the set of parts marked as having abnormal matching, a detailed list of abnormal problems is compiled, including the model of the interfering / conflicting parts, the location of the problem, the type of the problem, and the original factory standard requirements.

[0181] The system integrates the above information into a matching rationality verification report according to the preset report format, displays it synchronously on the system interface, and supports exporting files in PDF, Excel and other formats. The report is synchronized to the parts matching output module, providing a direct basis for subsequent screening of available configuration information sets that meet assembly requirements.

[0182] Based on the same inventive concept, embodiments of the present invention provide a vehicle parts matching method based on VIN code parsing, including:

[0183] The VIN code parsing step involves segmenting and parsing the VIN code of the vehicle to be matched using the configured VIN structured parsing strategy, and generating vehicle configuration information that eliminates VIN code ambiguity.

[0184] The component matching step is configured with a candidate component matching strategy, which is connected to the VIN code parsing module to match the candidate component configuration information set corresponding to the vehicle configuration information in the preset engineering database.

[0185] The assembly constraint verification step is configured with an assembly constraint verification strategy, which is connected to the component matching module. It is used to analyze the mutual exclusion, substitution, and compatibility of each component candidate configuration information set to filter out unusable components that do not meet the preset benchmark conditions and obtain the usable configuration information set.

[0186] The parts evaluation step analyzes the vehicle's historical maintenance, engineering change frequency, and assembly consistency data using a confidence evaluation strategy to determine the confidence weight of the corresponding available configuration information set.

[0187] The component matching output step is configured with a matching result output strategy, which is connected to the component evaluation module to analyze the confidence weight value and generate the component matching information output with the highest confidence weight.

[0188] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0189] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.

Claims

1. A vehicle parts matching system based on VIN code analysis, characterized by, include: The VIN code parsing module uses the configured VIN structured parsing strategy to segment and parse the VIN code of the vehicle to be matched, and generates vehicle configuration information that eliminates VIN code ambiguity. The VIN structured parsing strategy includes: Collect the VIN code information of the vehicle to be matched and parse it in segments to determine the meaning of the vehicle configuration represented by each segment of the VIN code; Based on the VIN code segmentation parsing results, a preset vehicle model attribute database is matched to extract vehicle configuration attributes, which include vehicle platform code, production time interval code, market area code, and powertrain type code. Based on the extracted vehicle configuration attributes, VIN code ambiguity removal is performed using a preset ambiguity removal sub-strategy to generate vehicle configuration information in a structured data format, which is then output to the component matching module. The aforementioned ambiguity elimination sub-strategy includes: The historical maintenance data of the users of the vehicles to be matched is analyzed to determine the users' historical maintenance selection preferences under the same configuration attributes of the parts; Based on historical maintenance selection preference information, similar subsets of vehicle configuration attributes generated by VIN code parsing are marked, and the priority weights of the corresponding configuration attribute subsets are adjusted by a preset adjustment ratio. The similar subsets of vehicle configuration attributes are reordered according to the adjusted priority weights to generate vehicle configuration information that eliminates VIN configuration ambiguities caused by user personalized configurations. The component matching module is configured with a candidate component matching strategy and is connected to the VIN code parsing module. It is used to match the candidate component configuration information set corresponding to the vehicle configuration information in the preset engineering database. The assembly constraint verification module is configured with an assembly constraint verification strategy and is connected to the component matching module. It is used to analyze the mutual exclusion, replacement, and compatibility of each component candidate configuration information set to filter out unusable components that do not meet the preset benchmark conditions and obtain the usable configuration information set. The parts evaluation module analyzes the vehicle's historical maintenance, engineering change frequency, and assembly consistency data using a configured confidence evaluation strategy to determine the confidence weight of the corresponding available configuration information set. The component matching output module is configured with a matching result output strategy and is connected to the component evaluation module. It is used to analyze the confidence weight value and generate the component matching information with the highest confidence weight.

2. The vehicle parts matching system based on VIN code analysis according to claim 1, characterized in that, The candidate component matching strategy includes: Based on the vehicle configuration attributes, the engineering rule base is queried to match the engineering configuration rule set of the corresponding vehicle model platform; Multiple potential engineering configuration branches are generated based on the set of engineering configuration rules, and each engineering configuration branch corresponds to a complete parts assembly list. The generated set of candidate configuration information for multiple components is output to the assembly constraint verification module.

3. The vehicle parts matching system based on VIN code analysis as claimed in claim 1 wherein, The assembly constraint verification strategy includes: Collect the component assembly list from the candidate configuration set for each project and match it with the component constraint rule database; The mutual exclusion relationship between components is verified based on the component constraint rule database. When mutually exclusive components are detected to exist in the same configuration branch at the same time, a configuration branch removal instruction is triggered. Based on the component constraint rule database, the component replacement rules are validated. When a replaceable component is detected, the replacement relationship is marked and the compatible configuration branch is retained. The component version compatibility is verified based on the component constraint rule database. When version incompatibility is detected, the configuration branch is removed. The regional adaptability is verified based on the component constraint rule database. When a regional mismatch is detected, the configuration branch is removed.

4. The vehicle parts matching system based on VIN code analysis as claimed in claim 1, wherein, The confidence assessment strategy includes: Collect historical maintenance records from the database and count the frequency of each engineering configuration candidate set in historical maintenance scenarios; Collect the engineering change frequency database and obtain the number of engineering changes corresponding to each engineering configuration candidate set; Collect the loading consistency database and obtain the matching degree between the candidate configuration set of each project and the actual loading record; Based on the frequency of occurrence, number of engineering changes, and matching degree, a confidence score value for each engineering configuration candidate set is calculated using a confidence scoring model.

5. A vehicle parts matching system based on VIN code parsing according to claim 4, characterized in that, The confidence score model is calculated using the following formula: ； in, Configure confidence scores for the candidate set for the project. The frequency of this configuration candidate set in historical maintenance records. The maximum frequency of occurrence across all configuration candidate sets. The number of project changes corresponding to this configuration candidate set. The maximum number of engineering changes across all configuration candidate sets. The matching degree between this configuration candidate set and the actual vehicle installation records, This represents the maximum matching degree. , , These are the preset weighting coefficients for frequency of occurrence, number of engineering changes, and matching degree, respectively.

6. A vehicle parts matching system based on VIN code parsing according to claim 1, characterized in that, Also includes: The 3D assembly structure verification module is configured with an assembly structure reverse verification strategy and is connected to the component matching output module. It is used to perform reverse verification between the matched component set and the 3D assembly structure data to determine the virtual assembly verification result. A comparative analysis is conducted based on the virtual assembly verification results and the available configuration information set to determine the available configuration information set that meets the assembly requirements.

7. A vehicle parts matching system based on VIN code parsing according to claim 6, characterized in that, The aforementioned assembly structure reverse verification strategy includes: Collect the matched set of parts and query the 3D assembly structure database; Based on the 3D assembly structure database, obtain the spatial location information and assembly connection relationship of each component; A virtual spatial interference analysis is performed on the spatial positions of each component in the component set. When spatial interference occurs, the marker matching is abnormal. The assembly connection relationship of each component in the component set is logically verified. When an assembly logic conflict is detected, a matching anomaly is marked. Based on the interference detection and logic verification results, a matching rationality verification report is generated and output.

8. A vehicle parts matching method based on VIN code parsing, applied to the system described in any one of claims 1-7, characterized in that, include: The VIN code parsing step involves segmenting and parsing the VIN code of the vehicle to be matched using the configured VIN structured parsing strategy, and generating vehicle configuration information that eliminates VIN code ambiguity. The component matching step is configured with a candidate component matching strategy, which is connected to the VIN code parsing module and is used to match the component candidate configuration information set corresponding to the vehicle configuration information in the preset engineering database. The assembly constraint verification step is configured with an assembly constraint verification strategy, which is connected to the component matching module. It is used to analyze the mutual exclusion, substitution, and compatibility of each component candidate configuration information set to filter out unusable components that do not meet the preset benchmark conditions and obtain the usable configuration information set. The parts evaluation step analyzes the vehicle's historical maintenance, engineering change frequency, and assembly consistency data using a confidence evaluation strategy to determine the confidence weight of the corresponding available configuration information set. The component matching output step is configured with a matching result output strategy, which is connected to the component evaluation module to analyze the confidence weight value and generate the component matching information output with the highest confidence weight.

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