Methods and systems for traceability and management of quality data throughout the entire lifecycle of automotive parts

Abstract

This application relates to the field of automotive quality control technology, and in particular to a method and system for tracing and managing the quality data of automotive parts throughout their entire lifecycle. The method includes: first, acquiring the quality data of the parts throughout their entire lifecycle and identifying the corresponding lifecycle stages; then, retrieving a matching set of quality management rules from a preset strategy library and extracting core and auxiliary quality actions; next, traversing historical operation records to verify whether all core actions have been executed, calculating the execution ratio of auxiliary actions after achieving the target, and determining whether the stage completeness threshold has been reached; if the target is met, generating a stage migration instruction and a list of quality tasks for the next stage, executing the instruction to update the stage identifier, distributing tasks to the corresponding control nodes, and triggering subsequent data collection and verification. This application helps to achieve standardized and closed-loop control of quality data throughout the entire lifecycle, improving the efficiency and accuracy of quality traceability and control.

Description

Technical Field

[0001] This application relates to the field of automotive quality control technology, and in particular to a method and system for tracing and managing quality data throughout the entire lifecycle of automotive parts. Background Technology

[0002] With the development of intelligent and connected vehicles, the categories of auto parts are becoming increasingly complex, and the supply chain is constantly lengthening, making quality control of parts increasingly difficult. Traditional parts quality management often adopts a segmented control model, with quality data at each stage stored in a scattered manner and in inconsistent formats, resulting in data silos. At the same time, quality verification lacks standardized processes, and the distinction between core control nodes and auxiliary control nodes is vague, which can easily lead to the omission of key quality actions and the lack of basis for judging data completeness, resulting in disordered flow of parts throughout their life cycle.

[0003] Furthermore, unstructured quality data is difficult to reuse directly, and there is a lack of intelligent repair mechanisms for issues such as missing or incorrectly formatted structured data. When quality defects occur in the after-sales process, it is impossible to quickly trace the root cause of the defect, the determination of responsibility is unclear, and quality control strategies are difficult to iterate and optimize. In summary, existing technologies cannot achieve closed-loop management and accurate traceability of quality data throughout the entire lifecycle of parts, which restricts the improvement of the overall quality control level of automobiles. Summary of the Invention

[0004] To facilitate the standardization and closed-loop management of quality data throughout the entire lifecycle and improve the efficiency and accuracy of quality traceability, this application provides a method and system for tracing and managing quality data throughout the entire lifecycle of automotive parts.

[0005] Firstly, this application provides a method for tracing and managing the quality data of automotive parts throughout their entire lifecycle, employing the following technical solution: A method for tracing and managing the quality data throughout the entire lifecycle of automotive parts, comprising: Acquire quality data generated throughout the entire lifecycle of the target automotive component and identify the current lifecycle stage of the quality data; Based on the lifecycle stage, retrieve the quality management rule set corresponding to the lifecycle stage from the preset stage management strategy library; Analyze the quality management rule set and extract the core quality actions and auxiliary quality actions for verifying quality data; Iterate through the historical operation records of the target automotive parts' quality data to verify whether all core quality actions have been completed. If the core quality action has been completed, calculate the proportion of the number of auxiliary quality actions that have been completed to the total number of auxiliary quality actions in the quality management rule set. Determine whether the ratio value reaches the preset quality data completeness threshold for the corresponding stage; If the ratio reaches the quality data completeness threshold, then a state transition instruction and quality task list for advancing the target automotive component from the current life cycle stage to the next stage will be generated based on the quality management rule set. Execute the state transition instruction to update the lifecycle stage identifier of the target automotive component; Distribute the quality task list to the quality management execution nodes associated with the next phase, and trigger the execution of the quality data collection and verification tasks for the next phase.

[0006] Optionally, before obtaining the quality data generated by the target automotive component throughout its entire lifecycle, the following may also be included: Receive raw component quality data and identify the corresponding raw data format; If the original data format is unstructured text or image format, then an automated feature extraction process adapted to the format is invoked. An automated feature extraction process is executed to convert raw component quality data into standardized quality records containing structured quality fields; If the original data format is a structured table format, then verify whether the original data format conforms to the predefined component quality data standard specification; If the component quality data does not conform to the standard specifications, analyze the specific type of non-compliance, including missing field types and formatting errors. Based on the specific type identified in the analysis, the corresponding data cleaning and completion rules are invoked to repair the original component quality data; If the data conforms to the standards and specifications for component quality data, the original component quality data will be directly used as the standardized quality record. Standardized quality records are identified as the quality data for the target automotive parts, and the corresponding lifecycle stage of the quality data is determined based on the identification and verification results of the original data format.

[0007] Optionally, if the original data format is unstructured text or image format, the automated feature extraction process adapted to the format will be invoked, including: If the original data format is unstructured, identify the data modality category contained in the original component quality data. The data modality category includes at least text description, detection image or sound signal. Determine the security level of computing resources required to process raw component quality data based on the data modality category; Verify whether the current processing environment for executing the automated feature extraction process meets the requirements of the computing resource security level; If the requirements are met, the corresponding automated feature extraction process will be selected and invoked based on the data modality category; If the requirements are not met, the original component quality data will be stored in an isolated buffer and marked as data to be audited for safety. After receiving the security audit release instruction for the data to be audited, the corresponding automated feature extraction process is invoked based on the data modality category.

[0008] Optionally, depending on the specific type identified in the analysis, the corresponding data cleaning and completion rules can be invoked to repair the original component quality data, including: Based on the specific types identified in the analysis, determine whether the defects in the original component quality data are of a single type. If it is a single type of defect, the independent repair rule instance corresponding to the single defect will be directly matched and called from the preset data repair rule library; The original component quality data is repaired using independent repair rule instances to generate standardized quality records after repair. If it is not a single type of defect, assess the operational compatibility of repairing each defect independently. Determine whether there are logical conflicts or execution timing dependencies between the independent repair operations of all defects; If there is no logical conflict, then assign a corresponding repair sub-process to each type of defect; Generate a composite repair strategy by combining repair sub-processes based on execution time dependencies; Based on the aforementioned composite repair strategy, a standardized quality record after repair is generated; The generated, repaired, standardized quality records are identified as the quality data for the target automotive parts.

[0009] Optionally, after verifying that all core quality actions have been performed, the following may also be included: If there are any unexecuted core quality actions, the corresponding core quality actions will be defined as critical deficiencies, and a mandatory supplementary notification will be generated. Monitor whether a confirmation signal for the mandatory supplementary notification is received within the preset mandatory response period; If a confirmation signal for supplementation is received, the historical operation record of quality data will be updated based on the supplemented core quality action. Recalculate the scale value and determine whether the recalculated scale value reaches the data completeness threshold.

[0010] Optionally, after determining whether the proportion value reaches the preset quality data completeness threshold for the corresponding stage, the following steps are also included: If the proportion does not reach the quality data completeness threshold, then unexecuted items will be selected from all auxiliary quality actions as actions to be supplemented. Parse the quality management rule set to obtain a list of data fields required to perform the pending data entry action; Retrieve existing quality data files and historical operation records of the target automotive parts, and verify whether they contain the data content required by the supplementary data field list; If the required data content is included, a supplementary recording operation record corresponding to the action to be supplemented will be generated based on the retrieved data content. The supplementary data entry records are associated and bound with the actions to be supplemented, forming a supplementary archive of quality data for the corresponding lifecycle stage; Based on the quality management rule set that integrates supplementary quality data archives, state transition instructions and a quality task list are generated.

[0011] Optionally, after verifying whether the data content required by the supplementary data field list is included, the following may also be included: If the verification result is that the required data content is not included, then assess the impact of the supplementary data entry action on achieving the quality data completeness threshold corresponding to the current life cycle stage. Based on the action type and impact of the action to be supplemented, a corresponding multi-level data retrieval scheme is matched from the preset handling strategy library; According to the multi-level data tracing scheme, data supplementation requests are sent to different data source nodes in sequence until the data content required by the supplementary data field list is completely supplemented.

[0012] Optionally, if the required data content is included, a supplementary data entry record corresponding to the action to be supplemented is generated based on the retrieved data content, including: If the requested data content is included, determine whether the retrieved data content involves the key quality characteristic parameters of the component; If critical quality characteristic parameters are involved, identify the permission level required to access and modify the critical quality characteristic parameters; Verify whether the management account currently performing the data entry operation has the required permissions. If the permission level is not available, the data content involving key quality characteristic parameters will be marked as controlled frozen fields during the data entry process; Upon receiving an authorization unfreezing instruction from an account with the appropriate permissions, a supplementary entry record is generated based on the data content; If the required permissions are met, a supplementary entry record will be generated directly based on the data content.

[0013] Optional, also includes: When a quality defect is detected in a target automotive part during the after-sales maintenance phase, the system can trace back the historical operation records of the quality data to generate all related core quality actions and corresponding execution results of the part throughout its entire life cycle. Based on the core quality actions and their corresponding execution results, identify the upstream responsibility stage corresponding to the quality defect and the key quality action node that caused the defect in the upstream responsibility stage. Encapsulate key quality action nodes and the associated quality data into a quality traceability evidence package. The quality traceability evidence package is pushed to the quality management center, triggering the process of optimizing the quality control strategy for the upstream responsibility stage.

[0014] Secondly, this application also discloses a quality data traceability and management system for the entire life cycle of automotive parts, which adopts the following technical solution: A quality data traceability and management system for the entire lifecycle of automotive parts, comprising: The data acquisition module is used to acquire quality data generated by the target automotive parts throughout their entire life cycle and identify the current life cycle stage of the quality data. The rule retrieval module is used to retrieve the set of quality management rules corresponding to the lifecycle stage from the preset stage management strategy library. The rule parsing module is used to parse the quality management rule set and extract the core quality actions and auxiliary quality actions for verifying quality data. The action verification module is used to traverse the historical operation records of the quality data of the target automotive parts and verify whether all core quality actions have been executed. The calculation module is used to calculate the proportion of the number of auxiliary quality actions that have been executed to the total number of auxiliary quality actions in the quality management rule set if the core quality action has been completed. The judgment module is used to determine whether the ratio value has reached the preset quality data completeness threshold for the corresponding stage; If the proportion value reaches the quality data completeness threshold, the phase advancement module is used to generate state transition instructions and quality task lists to advance the target automotive parts from the current life cycle stage to the next stage based on the quality management rule set. The identifier update module is used to execute state transition instructions and update the lifecycle stage identifier of the target automotive component. The task distribution module is used to distribute the quality task list to the quality management execution nodes associated with the next stage, and trigger the execution of the quality data collection and verification tasks for the next stage.

[0015] In summary, this application includes the following beneficial technical effects: By developing standardized quality management rules in stages and clarifying the boundaries of core and auxiliary quality actions, the orderly flow of parts throughout their lifecycle is achieved, solving the problem of disorder in traditional segmented control. Intelligent data preprocessing technology is adopted, compatible with structured and unstructured multi-source heterogeneous data, and equipped with defect repair and secure extraction mechanisms to overcome the challenges of data silos and low reuse rates. Through a three-tiered mechanism of hierarchical verification, missing item correction, and access control, the quality baseline is strictly controlled while improving the completeness and security of quality data. Relying on an after-sales reverse traceability mechanism, the root cause of defects is quickly located, responsibility nodes are identified, and control strategy optimization is triggered simultaneously, achieving closed-loop traceability and refined management of automotive parts' quality data throughout their entire lifecycle. This effectively reduces quality risks, shortens after-sales defect handling cycles, and comprehensively improves the quality control level of the automotive industry chain. Attached Figure Description

[0016] Figure 1 This is a main flowchart of an embodiment of this application; Figure 2 This is a flowchart of steps S201 to S208; Figure 3 This is a flowchart of steps S301 to S306; Figure 4 This is a system block diagram of an embodiment of this application.

[0017] Explanation of reference numerals in the attached figures: 1. Data Acquisition Module; 2. Rule Retrieval Module; 3. Rule Parsing Module; 4. Action Verification Module; 5. Calculation Module; 6. Judgment Module; 7. Stage Progression Module; 8. Identifier Update Module; 9. Task Distribution Module. Detailed Implementation

[0018] Firstly, this application discloses a method for tracing and managing the quality data of automotive parts throughout their entire lifecycle.

[0019] Reference Figure 1 A method for tracing and managing the quality data of automotive parts throughout their entire lifecycle, comprising steps S101 to S109: Step S101: Obtain the quality data generated by the target automotive parts throughout their entire life cycle, and identify the current life cycle stage of the quality data.

[0020] Specifically, the lifecycle stage refers to the entire process of a component from R&D and design to after-sales disposal, including at least five core stages: component design, manufacturing, logistics and distribution, vehicle assembly, and after-sales maintenance. Each stage corresponds to specific quality control requirements. Quality data refers to the relevant data generated by the component at each stage, such as performance testing, dimensional verification, environmental adaptation, operation and maintenance records, and qualification judgment, which is the core basis for quality control. This step binds the full lifecycle data through the component's unique identification code (such as a QR code, RFID chip, or batch serial number) and uses a stage matching algorithm to quickly locate the current stage based on the data source and collection scenario, avoiding stage judgment errors.

[0021] Step S102: Based on the lifecycle stage, retrieve the quality management rule set corresponding to the lifecycle stage from the preset stage management strategy library.

[0022] Specifically, the stage management strategy library is a pre-built standardized database containing configuration information such as quality control standards, verification processes, data format requirements, and flow conditions specific to each lifecycle stage, supporting quick retrieval and retrieval. The quality management rule set is a complete set of quality control guidelines for the corresponding stage, including data verification standards, quality action execution specifications, and stage entry and exit conditions. The rule sets for different stages are independent of each other and accurately adapt to the control needs of each stage. This step automatically retrieves the corresponding rule set by matching the stage identifier with the strategy library index, eliminating the need for manual screening and improving rule adaptation efficiency.

[0023] Step S103: Parse the quality management rule set and extract the core quality actions and auxiliary quality actions for verifying quality data.

[0024] Specifically, core quality actions are mandatory verification actions that ensure the basic quality of components meets standards and determine eligibility for stage-by-stage transfer. None of them can be omitted. Examples include material physical and chemical testing, full inspection of dimensional accuracy, and pressure resistance testing in the production process. Failure to perform these actions will result in a quality failure. Auxiliary quality actions are supplementary actions that improve the precision of quality control and complete data archives. Examples include appearance re-inspection, batch information archiving, and environmental temperature and humidity recording. While not all of these actions are mandatory, they must meet completeness threshold requirements. This step uses rule semantic parsing and keyword extraction technology to break down the rule set content, accurately distinguish between the two types of actions, and form a list.

[0025] Step S104: Traverse the historical operation records of the target automotive parts' quality data to verify whether all core quality actions have been completed.

[0026] Specifically, the historical operation record of quality data is an electronic ledger of the execution of each quality action of the parts. It records in detail the action name, execution time, executor, execution result, data voucher and other information, and has the characteristic of being tamper-proof. This step uses a line-by-line comparison algorithm to match the list of core quality actions in the rule set with the historical operation record to check whether there are any omissions, failures, or lack of vouchers. If the core actions do not meet the standards, the stage flow is blocked to strictly control the bottom line of quality.

[0027] Step S105: If the core quality action has been completed, calculate the ratio of the number of executed auxiliary quality actions to the total number of auxiliary quality actions in the quality management rule set.

[0028] Specifically, the ratio, or the execution rate of auxiliary quality actions, is a core indicator for quantifying the completeness of quality data at the current stage. The calculation formula is: Ratio = (Number of auxiliary quality actions executed ÷ Total number of auxiliary quality actions) × 100%. This step uses the counting and statistics module to automatically count the number of executed and total auxiliary actions, quickly complete the ratio calculation, and provide data support for subsequent completeness determination.

[0029] Step S106: Determine whether the ratio value reaches the preset quality data completeness threshold for the corresponding stage.

[0030] Specifically, the quality data completeness threshold is the minimum standard for the execution of auxiliary actions pre-set by the vehicle manufacturer's quality control department at each stage. It is configured differently according to the importance of each stage. For example, the threshold is set at 90% for the production and manufacturing stage, 85% for the logistics and distribution stage, and 80% for the after-sales maintenance stage. This step compares the calculated execution rate with the threshold to determine whether the data completeness requirements for the stage flow are met.

[0031] Step S107: If the ratio reaches the quality data completeness threshold, then generate a state transition instruction and quality task list to advance the target automotive component from the current life cycle stage to the next stage based on the quality management rule set.

[0032] Specifically, the state transition instruction is an electronic control instruction that triggers changes in the lifecycle stage of a component, containing core information such as the component's unique identifier, current stage, target stage, and effective time; the quality task list is a detailed list of quality control tasks to be performed in the next stage, specifying data collection items, verification actions, execution deadlines, and responsibility nodes; this step automatically generates instructions and lists based on the rule set configuration, ensuring that the stage transition is based on evidence.

[0033] Step S108: Execute the state transition instruction to update the lifecycle stage identifier of the target automotive component.

[0034] Specifically, the lifecycle stage identifier is an electronic tag bound to the unique code of the component, used to mark the control stage of the component in real time, and stored synchronously in the cloud database and local terminal; this step automatically updates the identifier information through the instruction parsing module and synchronizes the change record to the end-to-end quality management platform to ensure that the suppliers, vehicle manufacturers, logistics providers, maintenance organizations and other participants obtain consistent stage information.

[0035] Step S109: Distribute the quality task list to the quality management execution nodes associated with the next stage, and trigger the execution of the quality data collection and verification tasks for the next stage.

[0036] Specifically, the quality management execution nodes are the responsible entities for quality control at each stage of the lifecycle. For example, the design stage corresponds to the technology R&D team, the production stage to the workshop quality inspection team, the logistics stage to the warehouse inspector, the vehicle assembly stage to the production line quality inspector, and the after-sales stage to the maintenance technician. The quality data collection and verification task is the core control work of the next stage, including collecting quality data according to specifications, performing corresponding quality actions, and retaining operation records. This step adopts a dual distribution method of platform push + SMS / email reminder to accurately push the task to the corresponding responsible node, while automatically triggering the task execution process and starting the quality control closed loop of the next stage.

[0037] Reference Figure 2 In one embodiment of this example, before obtaining the quality data generated by the target automotive component throughout its entire life cycle in step S101, steps S201 to S208 are further included: Step S201: Receive the original component quality data and identify the corresponding original data format.

[0038] Specifically, raw component quality data refers to initial quality information that has not undergone processing, including supplier inspection data, production line off-line inspection data, logistics tracking records, vehicle assembly inspection data, and after-sales maintenance records. This step connects to various data sources through data interfaces, automatically receives raw data, and then uses a format recognition algorithm to determine whether the data is in structured (Excel spreadsheets, database forms, CSV files) or unstructured (text reports, images, audio, PDF documents) format, laying the foundation for subsequent processing.

[0039] Step S202: If the original data format is unstructured text or image format, then invoke the automated feature extraction process adapted to the format.

[0040] Specifically, the automated feature extraction process is an intelligent processing program for different types of unstructured data, used to extract effective quality feature information hidden in text and images, replacing manual input and reducing errors. This step matches the corresponding extraction algorithm according to the specific type of unstructured data to achieve automated capture of quality information.

[0041] Step S203: Execute an automated feature extraction process to convert the raw component quality data into standardized quality records containing structured quality fields.

[0042] Specifically, structured quality fields are unified quality data fields across the entire platform, including component model, batch number, production number, testing parameters, pass / fail judgment results, execution time, etc.; standardized quality records are quality data files that conform to unified format specifications and can be directly stored and reused, eliminating format barriers between different data sources; after this step executes the extraction process, the scattered unstructured information is integrated into standardized records, improving data universality.

[0043] Step S204: If the original data format is a structured table format, then verify whether the original data format conforms to the predefined component quality data standard specification.

[0044] Specifically, the component quality data standard specification is an industry-wide unified data format guideline that clearly stipulates requirements such as field names, data types, value ranges, required fields, and character lengths to prevent data confusion and mismatches. This step uses a format verification module to compare the original structured data with the specification line by line to determine whether it is compliant.

[0045] Step S205: If the component quality data does not conform to the standard specifications, analyze the specific type of non-compliance. The specific types include missing field types and format error types.

[0046] Specifically, the "field missing" type refers to the absence of data in required quality fields or the lack of key information; the "format error" type refers to data type mismatch, values ​​exceeding the acceptable range, or disordered character format, such as entering numeric parameters as text or having incorrect batch number formats. This step uses an anomaly analysis algorithm to locate the specific defect type, providing direction for subsequent repairs.

[0047] Step S206: Based on the specific type identified in the analysis, call the corresponding data cleaning and completion rules to repair the original component quality data.

[0048] Specifically, the data cleaning and completion rules are preset repair logics for different defect types. For example, missing fields can be completed by calling historical data from the same batch, format errors can be corrected by calling format conversion tools, and abnormal values ​​can be removed or replaced. This step matches the corresponding rules according to the defect type and automatically completes the data repair to ensure data availability.

[0049] Step S207: If the component quality data conforms to the standard specifications, the original component quality data will be directly used as the standardized quality record.

[0050] Specifically, compliant structured data does not require secondary processing; it can be directly verified and incorporated into the quality database, reducing data processing steps and improving overall efficiency.

[0051] Step S208: The standardized quality record is identified as the quality data of the target automotive parts, and the life cycle stage corresponding to the quality data is determined based on the identification results and verification results of the original data format.

[0052] Specifically, the sources, formats, and content characteristics of raw data differ significantly across different lifecycle stages. For example, data in the design stage is mostly technical parameter documents, data in the production stage is mostly offline testing forms, and data in the after-sales stage is mostly maintenance records. This step combines format recognition and verification results with a feature matching algorithm to accurately determine the lifecycle stage to which the data belongs, thus achieving data-stage binding.

[0053] Reference Figure 3 In one embodiment of this example, if the original data format is unstructured text or image format, step S202 calls an automated feature extraction process adapted to the format, including steps S301 to S306: Step S301: If the original data format is unstructured, identify the data modality category contained in the original component quality data. The data modality category includes at least text description, detection image or sound signal.

[0054] Specifically, data modality categories refer to the presentation format of unstructured data. Text descriptions include quality inspection reports, technical parameter descriptions, and certificate texts; inspection images include component flaw detection images, actual appearance photos, and dimensional measurement maps; sound signals include component abnormal noise detection audio and operating condition vibration audio. Different modalities have different data characteristics and require specific extraction algorithms.

[0055] Step S302: Determine the computing resource security level required to process the raw component quality data based on the data modality category.

[0056] Specifically, the computing resource security level is a classification of computing power usage permissions based on the degree of data confidentiality and processing complexity. Text and images involving core technical parameters and trade secrets require high-level security computing power, while ordinary audio and appearance images can use low-level computing power. This step matches the corresponding computing resource security level according to the modality category to prevent the leakage of core data.

[0057] Step S303: Verify whether the current processing environment for executing the automated feature extraction process meets the requirements of the computing resource security level.

[0058] Specifically, the processing environment refers to the servers, terminal devices, and software systems that perform data extraction; the environment verification dimensions include computing power permissions, data encryption capabilities, access log management, and anti-leakage mechanisms; this step uses a security verification module to determine whether the current environment meets the computing power security requirements, thus ensuring data processing security.

[0059] Step S304: If the requirements are met, select and call the corresponding automated feature extraction process based on the data modality category.

[0060] Specifically, the text modality uses OCR text recognition and keyword extraction algorithms, the image modality uses image segmentation and parameter recognition models, and the sound modality uses spectrum analysis and anomaly detection algorithms. This step uses a precise matching algorithm to achieve efficient extraction of quality features from unstructured data.

[0061] Step S305: If the requirements are not met, store the original component quality data in the isolation buffer and mark it as data to be audited for safety.

[0062] Specifically, when the processing environment does not meet the security level, unstructured data is stored in an isolation buffer. This area is an independent encrypted storage unit, which prohibits unauthorized access and read / write operations. At the same time, it is marked as data to be audited for security purposes, and the processing flow is suspended to prevent data leakage or tampering. The buffer data is archived separately, marked with an audit mark, and awaits verification and release by dedicated personnel.

[0063] Step S306: After obtaining the security audit release instruction for the data to be audited, call the corresponding automated feature extraction process according to the data modality category.

[0064] Specifically, the security audit release order is issued by a dedicated data security officer after reviewing the data and confirming the compliance of the processing environment. After the security auditor completes the environment rectification and data verification, the security audit release order is issued. After receiving the order, the system switches to the compliant processing environment, calls the corresponding automated feature extraction process according to the data modality category determined in the early stage, completes the standardized transformation of unstructured data, and archives the release record and processing log simultaneously to achieve full traceability.

[0065] In one embodiment of this example, step S206, based on the specific type analyzed, calls the corresponding data cleaning and completion rules to repair the original component quality data, including steps S401 to S409: Step S401: Based on the specific type identified in the analysis, determine whether the defects in the original component quality data are of a single type.

[0066] Specifically, a single defect refers to data containing only one type of problem, such as missing fields or format errors, without any overlap. A compound defect, on the other hand, involves both missing fields and format errors, where the two types of defects affect each other and cannot be fixed by a single rule. This step uses defect statistics and classification algorithms to automatically traverse all data fields, marking defect locations and type attributes, quickly completing defect classification. This provides a precise basis for formulating subsequent differentiated repair strategies, avoiding data distortion or incomplete repair caused by blind repair.

[0067] Step S402: If it is a single type of defect, then directly match and call the independent repair rule instance corresponding to the single defect from the preset data repair rule library.

[0068] Specifically, for quality data defects determined to be of a single type, the system accurately retrieves corresponding independent repair rule instances from a pre-built data repair rule library. This rule library contains standardized repair templates for various single defects, which can be executed directly without secondary debugging. For defects with missing fields, the system retrieves field completion rule instances and fills in the gaps using historical data of parts from the same batch, system default compliance values, and fallback data from related data sources. For defects with format errors, the system retrieves format calibration rule instances and corrects abnormal data through data type conversion, character normalization, and numerical threshold correction. The rule matching process is fully automated, requiring no manual intervention, significantly improving the efficiency of single defect repair.

[0069] Step S403: Apply independent repair rule instances to repair the original component quality data and generate a standardized quality record after repair.

[0070] Specifically, an independent repair rule instance is loaded and a full repair operation is executed. Data changes are monitored in real time during the repair process to prevent erroneous or missed repairs. After repair, a secondary compliance verification module is initiated to compare the repaired data against the component quality data standards and specifications item by item, verifying whether defects have been completely eliminated and whether data fields are complete and compliant. Upon successful verification, a standardized quality record is generated, simultaneously annotating the record with traceability information such as repair identifier, repair time, rule number used, and operation node, and archiving it to the quality database. This prevents unqualified repair data from flowing into subsequent control stages, ensuring data availability and traceability.

[0071] Step S404: If it is not a single type of defect, assess the operational compatibility of repairing each defect independently.

[0072] Specifically, operational compatibility refers to whether different defect repair operations interfere with or conflict with each other. For example, is there a logical contradiction between field completion and format correction? For compound defects involving both missing fields and format errors, a single repair mode is abandoned, and a special assessment of repair operation compatibility is conducted. The core analysis focuses on whether there are conflicts between the independent repair logic of the two types of defects and whether there are temporal dependencies in the repair execution. For example, some fields require format calibration before the missing content can be accurately filled; reversing the order will result in invalid data filling. This step uses a compatibility analysis model to simulate the parallel and serial execution effects of the two types of repair operations, identify conflict points and temporal priorities, and generate a defect repair feasibility report. This lays the foundation for building a compound repair strategy and avoids problems such as data corruption and secondary defects during the repair process.

[0073] Step S405: Determine whether there are logical conflicts or execution timing dependencies between the independent repair operations of all defects.

[0074] Specifically, based on the compatibility assessment results, the constraints for repairing composite defects are further clarified. Logical conflict refers to two types of repair operations making opposite modification instructions on the same data field, leading to data anomalies. Execution timing dependency means that the repair of one type of defect requires the completion of the repair of another type of defect as a prerequisite, with no independent execution condition. Composite defects determined to have no logical conflict can be repaired in parallel to improve efficiency. Composite defects with logical conflict or timing dependency must have their repair order adjusted, execution priorities defined, and a serial repair mode adopted to ensure a smooth repair process and accurate data.

[0075] Step S406: If there is no logical conflict, assign a corresponding repair sub-process to each type of defect.

[0076] Specifically, separate repair sub-processes are assigned to address two types of issues: missing fields and format errors. These two sub-processes are independent and do not interfere with each other, and are executed in parallel by dedicated repair modules. The missing field sub-process focuses on accurately completing missing fields, while the format error sub-process focuses on standardizing and calibrating abnormal data. Data synchronization nodes are set during execution to avoid data cross-interference between the two sub-processes. After parallel repair is completed, the repair results are summarized and integrated for verification, balancing repair efficiency and data quality.

[0077] Step S407: Generate a composite repair strategy by combining the repair sub-processes according to the execution time sequence dependency.

[0078] Specifically, the execution order, triggering conditions, and priority weights of each repair sub-process are coordinated to form a systematic composite repair strategy. This strategy strictly adheres to the core logic of "calibrating the format first, then filling in the fields." First, the format error repair sub-process is executed to calibrate data types, character specifications, and numerical thresholds. Then, based on the calibrated compliant data, the field missing repair sub-process is executed to fill in the missing content. The strategy clearly defines the start conditions, interruption mechanisms, and exception handling rules for each sub-process, forming a closed-loop repair plan, which is then loaded into the system repair module for execution.

[0079] Step S408: Based on the composite repair strategy, generate a standardized quality record after repair.

[0080] Specifically, following the timeline, rules, and control requirements set by the composite repair strategy, the entire repair operation is carried out in a coordinated manner. The repair monitoring module is activated throughout the process to verify the compliance and data accuracy of each repair operation in real time. If any repair anomalies or data deviations occur, the operation is immediately suspended and an alert is issued, with the system automatically retrying or manual intervention for troubleshooting. After all defects are completely eliminated and the repair data fully meets the quality standards and specifications, the repair results are integrated to generate a standardized quality record. This record is characterized by complete fields, uniform format, and accurate data, and can be directly archived and reused, providing reliable data support for subsequent quality verification and phased transfer.

[0081] Step S409: The generated, repaired, standardized quality record is identified as the quality data of the target automotive parts.

[0082] Specifically, the generated standardized quality records after repair undergo final verification. Once it is confirmed that there are no residual defects or data anomalies, they are officially identified as the quality data of the target automotive parts. Simultaneously, the unique identification code of the parts is bound to them, linked to the full life cycle quality archive, and archived to the cloud quality database, enabling interconnection and interoperability with data from various stages such as design, production, and logistics.

[0083] In one embodiment of this example, after step S104 verifies whether all core quality actions have been completed, steps S501 to S504 are further included: Step S501: If there are any unexecuted core quality actions, define the corresponding core quality actions as critical deficiencies and generate a mandatory supplement notification.

[0084] Specifically, during the verification of core quality actions, if any items are found to be unexecuted, failed to be executed, or have unqualified results, these actions are immediately defined as critical deficiencies. Such deficiencies directly violate quality control standards, leading to invalidation of component quality assessments and disruption of stage-based workflows, and are therefore subject to mandatory rectification. The system automatically generates a mandatory supplementary notice, clearly specifying the name of the critical deficiency, its corresponding stage, rectification requirements, a list of supporting materials, a rectification deadline, and accountability clauses. This notice is pushed to the relevant responsible parties through multiple channels, including platform pop-ups, SMS, WeChat, and email, urging them to complete the rectification within a specified timeframe and eliminate quality control loopholes. Critical deficiencies are core control loopholes affecting component quality assessments and disrupting stage-based workflows; the mandatory supplementary notice serves as a warning document urging responsible parties to complete rectification, pushed via platform pop-ups, SMS, and email, clearly specifying the name of the deficiency, rectification requirements, and completion deadline.

[0085] Step S502: Monitor whether a confirmation supplement signal for the mandatory supplement notification is received within the preset mandatory response period.

[0086] Specifically, a standardized mandatory response cycle is set based on the severity of the defect (generally 24 hours for critical defects and 12 hours for major safety defects). The system monitors in real time within the response cycle to verify whether a confirmation and supplementary signal from the responsible party has been received. The mandatory response cycle is a pre-set rectification period, set to 24 hours, 48 ​​hours, etc., depending on the urgency of the defect. The confirmation and supplementary signal is a valid receipt of the completion of the core action rectification, which must be accompanied by supporting materials such as execution records, testing certificates, and data reports. The system initially verifies the completeness of the materials. If the signal is successfully received within the cycle, the subsequent rectification process begins; if it is not received within the time limit, the warning is automatically escalated to management, and a special supervision mechanism is activated to ensure that rectification is not delayed.

[0087] Step S503: If a confirmation supplement signal is received, update the historical operation record of quality data according to the supplemented core quality action.

[0088] Specifically, after the system verifies the authenticity and validity of the supplementary signals and supporting materials, the core quality action correction information is synchronously updated to the quality data historical operation record. Detailed information such as the corrected action name, execution time, executor, test result, data voucher number, and rectification reviewer is entered. The updated electronic ledger is tamper-proof and is archived in conjunction with the original operation record, forming a complete quality action execution trajectory. At the same time, key missing item markers are cleared, and the flow blockage is removed, clearing obstacles for subsequent data completeness assessments.

[0089] Step S504: Recalculate the scale value and determine whether the recalculated scale value reaches the data completeness threshold.

[0090] Specifically, after the missing core quality actions are corrected, the system recalculates the number of auxiliary quality actions executed and the total number, calculates the execution ratio according to a predetermined formula, and then compares this ratio with the quality data completeness threshold for the corresponding lifecycle stage. If the ratio meets the standard, the quality data is deemed complete, and the stage workflow is initiated; if the ratio still does not meet the standard, the system automatically switches to the auxiliary quality action supplementation process to further improve the quality data archive and ensure that there are no blind spots or omissions in the entire process of quality control.

[0091] In one embodiment of this example, after determining in step S106 whether the ratio value has reached the preset quality data completeness threshold for the corresponding stage, steps S601 to S606 are further included: Step S601: If the ratio value does not reach the quality data completeness threshold, then select the unexecuted items from all auxiliary quality actions as actions to be supplemented.

[0092] Specifically, actions to be supplemented are control items that have not been completed in the auxiliary quality actions and have resulted in substandard execution rates. This step involves comparison and screening to form a list of actions to be supplemented, clarifying the objectives and scope of the supplementation.

[0093] Step S602: Parse the quality management rule set to obtain a list of supplementary data fields necessary for performing the supplementary data entry action.

[0094] Specifically, the list of data fields to be supplemented is a collection of core data items required to complete the supplementary data entry action. For example, the supplementary data entry for appearance re-inspection needs to include fields such as part number, re-inspection result, appearance photo, and re-inspection time. This step extracts the list to clarify the data content required for supplementary data entry.

[0095] Step S603: Retrieve the existing quality data archives and historical operation records of the target automotive parts, and verify whether they contain the data content required by the supplementary data field list.

[0096] Specifically, in this embodiment, a full data retrieval module is activated to traverse the existing quality data archives, historical operation records, local cache, cloud backups, and upstream related data sources of the target components. Through keyword matching, field mapping, and code association, the module verifies whether the existing data contains all the content required for the supplementary list. The existing quality data archives are the full quality information of the components already in the database; historical operation records are traces of past quality actions. Compliant existing data is reused first to avoid duplicate collection and wasted manpower and computing power. The entire retrieval process is logged, and a retrieval report is generated, clearly identifying reusable data points and missing data.

[0097] Step S604: If the required data content is included, generate a supplementary recording operation record corresponding to the action to be supplemented based on the retrieved data content.

[0098] Specifically, after the existing data is confirmed to contain all compliant content of the supplementary entry list, the system automatically retrieves the corresponding existing data, accurately associates and matches it with the auxiliary actions to be supplemented, and generates supplementary entry operation records in a standardized format. The records are marked with information such as data source, association time, operator, and matching result. The auxiliary actions can be supplemented without manual data collection, which greatly improves the efficiency of supplementary entry, while ensuring the authenticity and traceability of the data.

[0099] Step S605: Associate and bind the supplementary recording operation record with the action to be supplemented to form a quality data supplementary archive for the corresponding lifecycle stage.

[0100] Specifically, the generated supplementary data entry records are bound one by one with the corresponding auxiliary actions to be supplemented, and the original quality data is integrated to form a supplementary quality data archive for the current lifecycle stage. The supplementary archive is archived in association with the main quality archive and incorporated into the component's full lifecycle quality database, serving as an important basis for determining data completeness. The archive adopts an encrypted storage mode to prevent data tampering and loss, ensuring the integrity and security of the quality archive.

[0101] Step S606: Based on the quality management rule set that integrates the supplementary quality data archive, generate state transition instructions and a quality task list.

[0102] Specifically, after the supplementary files are integrated, the system recalculates the execution ratio of auxiliary quality actions. Once the standards are met and the quality data is complete, it automatically generates state transition instructions and a list of quality tasks for the next stage based on the quality management rule set for the corresponding stage. The instructions and lists are encrypted synchronously and associated with the unique component codes, then pushed to the management platform to officially start the component lifecycle stage flow, continuing the closed loop of full-process quality control.

[0103] In one embodiment of this example, after checking whether the data content required by the supplementary data field list is included in step S603, steps S701 to S703 are further included: Step S701: If the verification result is that the required data content is not included, then assess the impact of the supplementary data entry action on the achievement of the quality data completeness threshold corresponding to the current life cycle stage.

[0104] Specifically, when it is confirmed that the existing data cannot meet the supplementary entry requirements, an impact assessment is conducted on the auxiliary actions to be supplemented. These actions are categorized into three levels—high, medium, and low—based on their impact on the completeness threshold of quality data, stage flow, and component quality assessment: High-impact actions directly determine whether the threshold is met and are related to the effectiveness of quality assessment; medium-impact actions affect the completeness of the archives but do not hinder the flow; low-impact actions are routine archiving items with minimal impact. Differentiated supplementary entry priorities are established based on the impact level to ensure that core supplementary entry matters are handled first.

[0105] Step S702: Based on the action type and impact of the action to be supplemented, match the corresponding multi-level data tracing scheme from the preset handling strategy library.

[0106] Specifically, based on the impact level and data type of the action to be supplemented, a multi-level data tracing scheme is matched from the preset handling strategy library, and tracing is carried out according to the priority of the data source: Level 1 tracing local cache, historical backup, and data retained on the operation terminal; Level 2 tracing upstream suppliers, production workshops, logistics warehouses, assembly lines and other related node databases; Level 3 tracing cloud backup, industry supervision platform, and archived data of third-party testing institutions, expanding the tracing scope layer by layer to maximize the collection of supplemented data; among them, the handling strategy library is a database that stores data tracing schemes for different scenarios.

[0107] Step S703: In accordance with the multi-level data tracing scheme, data supplementation requests are sent to different data source nodes in sequence until the data content required by the supplementary data field list is completely supplemented.

[0108] Specifically, following the hierarchical order set by the multi-level data tracing scheme, standardized data supplementation requests are sent sequentially to the corresponding data source responsibility nodes. The request file clearly indicates the unique component code, the names of the fields to be supplemented, data format requirements, feedback deadlines, and collaboration requirements. Upon receiving the request, each node must complete data retrieval, formatting, and return the data within the specified timeframe. The system verifies the completeness and compliance of the returned data in real time. If the data retrieved in a single instance is insufficient, the tracing level is automatically escalated and a second request is initiated until all required data fields are collected before initiating subsequent supplementation operations.

[0109] In one embodiment of this example, if step S604 includes the required data content, then generating a supplementary recording operation record corresponding to the action to be supplemented based on the retrieved data content includes steps S801 to S806: Step S801: If the required data content is included, determine whether the retrieved data content involves the key quality characteristic parameters of the component.

[0110] Specifically, before supplementing auxiliary action data, the data to be supplemented is screened for attributes to determine whether it contains key quality characteristic parameters. These parameters are core indicators that directly determine the safety performance, assembly accuracy, and service life of parts, such as material strength, dimensional tolerance, sealing pressure, torque threshold, and pressure resistance value. They are core confidential data for quality control. Once tampered with or misrecorded, they will directly cause hidden quality problems and safety accidents in the whole vehicle. Accurate judgment must be made through keyword matching and parameter threshold verification.

[0111] Step S802: If critical quality characteristic parameters are involved, identify the permission level required to access and modify the critical quality characteristic parameters.

[0112] Specifically, for the key quality characteristic parameters identified through screening, three levels of operational permissions are assigned based on the degree of confidentiality and risk level: Level 1, general permissions, only allow viewing, with no modification or supplementary entry permissions; Level 2, quality control permissions, allow routine supplementary entry, requiring double verification; Level 3, chief technical engineer permissions, are the highest level, allowing the handling of abnormal parameters, unlocking frozen fields, and approving special supplementary entry applications. The system automatically retrieves the corresponding permission configuration rules for each parameter, clearly defining the minimum operational permission requirements and strengthening the permission control defense.

[0113] Step S803: Verify whether the management account currently performing the supplementary entry operation has the required permissions.

[0114] Specifically, the account permission verification module is activated to authenticate the account initiating the supplementary data entry operation, compare permissions, and retrieve information such as the account's role, authorization scope, and historical operation records to verify whether the account's actual permissions meet the minimum level required for supplementing the key parameters. The entire verification process is logged, generating a permission verification log. If permissions match, the supplementary data entry process begins; if permissions are insufficient, the operation is immediately blocked, triggering a permission warning and pushing it to the superior administrator. Unauthorized operations are strictly prohibited.

[0115] Step S804: If the permission level is not available, the data content involving key quality characteristic parameters will be marked as controlled frozen fields during the data entry process.

[0116] Specifically, when the current operating account's permissions are found to be insufficient, the system automatically marks the key quality characteristic parameter as a controlled frozen field. An AES encryption locking mechanism is used to prohibit any unauthorized viewing, modification, copying, or supplementary entry operations. The frozen status is synchronized to all platform terminals to prevent malicious tampering or accidental operation of the parameter. Controlled frozen fields are those that are prohibited from being viewed, modified, or accessed by unauthorized accounts, and are encrypted to prevent malicious tampering or accidental operation of key parameters. A freeze log is also generated, noting the freeze time, parameter name, permission gap, and operating account information for subsequent auditing and traceability. During the freeze period, this parameter is not included in the completeness ratio calculation.

[0117] Step S805: After receiving the authorization unfreezing instruction from an account with the corresponding permissions, generate a supplementary entry operation record based on the data content.

[0118] Specifically, operators with insufficient permissions need to submit a critical parameter supplementation authorization application, specifying the reason for supplementation, data source, operation plan, and risk control measures. After online review and approval by a high-privilege administrator, an authorization unfreezing command is issued. Upon receiving the command, the system automatically unfreezes the field. Operators complete the critical parameter supplementation according to the specifications. After supplementation, the administrator needs to conduct a second review and confirmation. Once the review is successful, a formal supplementation operation record is generated, recording the bound authorization number, unfreezing time, and reviewer information, thus achieving full traceability and accountability for critical parameter supplementation.

[0119] Step S806: If the permission level is available, generate the supplementary entry record directly based on the data content.

[0120] Specifically, when the current operating account permissions fully meet the requirements for supplementing key parameters, no additional approval process is required. The system can directly retrieve compliant data to complete the binding of the auxiliary actions to be supplemented. The system automatically verifies whether the parameter values ​​and formats meet the standard specifications. Once the verification is passed, a supplementary entry operation record is generated immediately, and the quality data archive and auxiliary action execution ledger are updated synchronously. Under the premise of strictly adhering to permission control and ensuring data security, efficient supplementary entry operations are achieved.

[0121] In one embodiment of this example, steps S901 to S904 are further included: Step S901: When a quality defect is detected in the target automotive part during the after-sales maintenance stage, based on the historical operation records of quality data, reverse traceability is used to generate all related core quality actions and corresponding execution results of the part throughout its entire life cycle.

[0122] Specifically, when functional defects, safety hazards, or substandard performance of parts are detected during after-sales maintenance, the system automatically activates a full lifecycle reverse traceability mechanism. Using the unique identification code of the part as the core index, it retrieves quality data from the entire process, from design and development, sample production, mass production, logistics and warehousing, vehicle assembly to after-sales maintenance. It focuses on tracing back the execution records, test results, data vouchers, operator and equipment information of core quality actions at each stage, reconstructing the full lifecycle control trajectory of the part, accurately locating the source of the defect, and breaking the industry pain points of lack of evidence for after-sales accountability and difficulty in tracing the source. Among them, reverse traceability starts with after-sales defects and reversely investigates the quality control trajectory of each link in design, production, logistics and assembly.

[0123] Step S902: Based on the core quality actions and their corresponding execution results, identify the upstream responsibility stage corresponding to the quality defect and the key quality action node that caused the defect in the upstream responsibility stage.

[0124] Specifically, a deep analysis of the entire process quality data obtained through reverse tracing is conducted. By combining defect characteristics and failure modes, and comparing the quality control standards and core action execution results at each stage, the upstream responsibility stage for the defect is accurately identified. This distinguishes whether the defect is caused by unreasonable design parameters, omissions in production testing, unqualified assembly verification, inadequate logistics protection, or improper after-sales maintenance operations. At the same time, the key action nodes that cause the defect are located, and the specific quality actions that were not executed, omitted, or executed unqualified are clearly identified. The responsible parties and dereliction of duty are clearly defined, providing an objective basis for subsequent rectification and accountability. Among these, the upstream responsibility stage is the source of the defect, and the key quality action nodes are the core control oversights that cause the defect, such as missed material testing in the production process or unqualified torque verification in the assembly process.

[0125] Step S903: Encapsulate the key quality action nodes and the associated quality data into a quality traceability evidence package.

[0126] Specifically, the quality traceability evidence package is an encrypted file containing defect descriptions, problem nodes, action records, quality data, and responsibility information. It integrates and encrypts the complete set of traceability materials, including defect description reports, responsibility stage determination results, key action node records, related quality data, execution ledgers, and testing certificates, according to a standardized format, and packages them into a quality traceability evidence package. The evidence package has the characteristics of being tamper-proof, verifiable, and archiveable, and completely retains the information of the entire defect traceability process. It serves as valid evidence for responsibility definition, internal rectification, and external collaborative accountability. At the same time, it is backed up to both cloud and local storage to prevent evidence loss.

[0127] Step S904: Push the quality traceability evidence package to the quality management center and trigger the quality control strategy optimization process for the upstream responsibility stage.

[0128] Specifically, the quality traceability evidence package is pushed to the quality management center, responsible departments, and management, simultaneously triggering the quality control strategy optimization process for the corresponding upstream responsibility stage. Based on the evidence package, the quality management department analyzes the root cause of defects, revises the stage quality management rules, supplements core quality actions, improves verification standards, strengthens authority control, and simultaneously updates the stage management strategy library and repair rule library, forming an iterative mechanism of "defect traceability - responsibility definition - strategy optimization - closed-loop control" to prevent the recurrence of similar quality defects and continuously improve the quality control level throughout the entire life cycle. The quality management center is the vehicle manufacturer's dedicated quality control department.

[0129] Secondly, this application also discloses a quality data traceability and management system for the entire life cycle of automotive parts.

[0130] Reference Figure 4 A quality data traceability and management system for the entire lifecycle of automotive parts, comprising: The data acquisition module is used to acquire quality data generated by the target automotive parts throughout their entire life cycle and identify the current life cycle stage of the quality data. The rule retrieval module is used to retrieve the set of quality management rules corresponding to the lifecycle stage from the preset stage management strategy library. The rule parsing module is used to parse the quality management rule set and extract the core quality actions and auxiliary quality actions for verifying quality data. The action verification module is used to traverse the historical operation records of the quality data of the target automotive parts and verify whether all core quality actions have been executed. The calculation module is used to calculate the proportion of the number of auxiliary quality actions that have been executed to the total number of auxiliary quality actions in the quality management rule set if the core quality action has been completed. The judgment module is used to determine whether the ratio value has reached the preset quality data completeness threshold for the corresponding stage; If the proportion value reaches the quality data completeness threshold, the phase advancement module is used to generate state transition instructions and quality task lists to advance the target automotive parts from the current life cycle stage to the next stage based on the quality management rule set. The identifier update module is used to execute state transition instructions and update the lifecycle stage identifier of the target automotive component. The task distribution module is used to distribute the quality task list to the quality management execution nodes associated with the next stage, and trigger the execution of the quality data collection and verification tasks for the next stage.

[0131] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A method for tracing and managing the quality data throughout the entire lifecycle of automotive parts, characterized in that, include: Acquire quality data generated throughout the entire lifecycle of the target automotive component and identify the current lifecycle stage of the quality data; Based on the lifecycle stage, retrieve the quality management rule set corresponding to the lifecycle stage from the preset stage management strategy library; Analyze the quality management rule set and extract the core quality actions and auxiliary quality actions for verifying quality data; Iterate through the historical operation records of the target automotive parts' quality data to verify whether all core quality actions have been completed. If the core quality action has been completed, calculate the proportion of the number of auxiliary quality actions that have been completed to the total number of auxiliary quality actions in the quality management rule set. Determine whether the ratio value reaches the preset quality data completeness threshold for the corresponding stage; If the ratio reaches the quality data completeness threshold, then a state transition instruction and quality task list for advancing the target automotive component from the current life cycle stage to the next stage will be generated based on the quality management rule set. Execute the state transition instruction to update the lifecycle stage identifier of the target automotive component; Distribute the quality task list to the quality management execution nodes associated with the next phase, and trigger the execution of the quality data collection and verification tasks for the next phase.

2. The method for tracing and managing the quality data of automotive parts throughout their entire lifecycle, as described in claim 1, is characterized in that... Before obtaining the quality data generated throughout the entire lifecycle of the target automotive component, the following steps are also included: Receive raw component quality data and identify the corresponding raw data format; If the original data format is unstructured text or image format, then an automated feature extraction process adapted to the format is invoked. An automated feature extraction process is executed to convert raw component quality data into standardized quality records containing structured quality fields; If the original data format is a structured table format, then verify whether the original data format conforms to the predefined component quality data standard specification; If the component quality data does not conform to the standard specifications, analyze the specific type of non-compliance, including missing field types and formatting errors. Based on the specific type identified in the analysis, the corresponding data cleaning and completion rules are invoked to repair the original component quality data; If the data conforms to the standards and specifications for component quality data, the original component quality data will be directly used as the standardized quality record. Standardized quality records are identified as the quality data for the target automotive parts, and the corresponding lifecycle stage of the quality data is determined based on the identification and verification results of the original data format.

3. The method for tracing and managing the quality data of automotive parts throughout their entire lifecycle, as described in claim 2, is characterized in that... If the original data format is unstructured text or image, the automated feature extraction process adapted to that format will be invoked, including: If the original data format is unstructured, identify the data modality category contained in the original component quality data. The data modality category includes at least text description, detection image or sound signal. Determine the security level of computing resources required to process raw component quality data based on the data modality category; Verify whether the current processing environment for executing the automated feature extraction process meets the requirements of the computing resource security level; If the requirements are met, the corresponding automated feature extraction process will be selected and invoked based on the data modality category; If the requirements are not met, the original component quality data will be stored in an isolated buffer and marked as data to be audited for safety. After receiving the security audit release instruction for the data to be audited, the corresponding automated feature extraction process is invoked based on the data modality category.

4. The method for tracing and managing the quality data of automotive parts throughout their entire lifecycle, as described in claim 2, is characterized in that... Based on the specific type identified in the analysis, the corresponding data cleaning and completion rules are invoked to repair the original component quality data, including: Based on the specific types identified in the analysis, determine whether the defects in the original component quality data are of a single type. If it is a single type of defect, the independent repair rule instance corresponding to the single defect will be directly matched and called from the preset data repair rule library; The original component quality data is repaired using independent repair rule instances to generate standardized quality records after repair. If it is not a single type of defect, assess the operational compatibility of repairing each defect independently. Determine whether there are logical conflicts or execution timing dependencies between the independent repair operations of all defects; If there is no logical conflict, then assign a corresponding repair sub-process to each type of defect; Generate a composite repair strategy by combining repair sub-processes based on execution time dependencies; Based on the aforementioned composite repair strategy, a standardized quality record after repair is generated; The generated, repaired, standardized quality records are identified as the quality data for the target automotive parts.

5. The method for tracing and managing the quality data of automotive parts throughout their entire lifecycle, as described in claim 1, is characterized in that... After verifying that all core quality actions have been completed, the process also includes: If there are any unexecuted core quality actions, the corresponding core quality actions will be defined as critical deficiencies, and a mandatory supplementary notification will be generated. Monitor whether a confirmation signal for the mandatory supplementary notification is received within the preset mandatory response period; If a confirmation signal for supplementation is received, the historical operation record of quality data will be updated based on the supplemented core quality action. Recalculate the scale value and determine whether the recalculated scale value reaches the data completeness threshold.

6. The method for tracing and managing the quality data of automotive parts throughout their entire lifecycle as described in claim 1, characterized in that, After determining whether the proportion value has reached the preset quality data completeness threshold for the corresponding stage, the process also includes: If the proportion does not reach the quality data completeness threshold, then unexecuted items will be selected from all auxiliary quality actions as actions to be supplemented. Parse the quality management rule set to obtain a list of data fields required to perform the pending data entry action; Retrieve existing quality data files and historical operation records of the target automotive parts, and verify whether they contain the data content required by the supplementary data field list; If the required data content is included, a supplementary recording operation record corresponding to the action to be supplemented will be generated based on the retrieved data content. The supplementary data entry records are associated and bound with the actions to be supplemented, forming a supplementary archive of quality data for the corresponding lifecycle stage; Based on the quality management rule set that integrates supplementary quality data archives, state transition instructions and a quality task list are generated.

7. The method for tracing and managing the quality data of automotive parts throughout their entire lifecycle, as described in claim 6, is characterized in that... After verifying whether the data content required by the supplementary data field list is included, the following is also included: If the verification result is that the required data content is not included, then assess the impact of the supplementary data entry action on achieving the quality data completeness threshold corresponding to the current life cycle stage. Based on the action type and impact of the action to be supplemented, a corresponding multi-level data retrieval scheme is matched from the preset handling strategy library; According to the multi-level data tracing scheme, data supplementation requests are sent to different data source nodes in sequence until the data content required by the supplementary data field list is completely supplemented.

8. The method for tracing and managing the quality data of automotive parts throughout their entire lifecycle as described in claim 6, characterized in that, If the requested data content is included, a supplementary data entry record will be generated based on the retrieved data content, corresponding to the action to be supplemented, including: If the requested data content is included, determine whether the retrieved data content involves the key quality characteristic parameters of the component; If critical quality characteristic parameters are involved, identify the permission level required to access and modify the critical quality characteristic parameters; Verify whether the management account currently performing the data entry operation has the required permissions. If the permission level is not available, the data content involving key quality characteristic parameters will be marked as controlled frozen fields during the data entry process; Upon receiving an authorization unfreezing instruction from an account with the appropriate permissions, a supplementary entry record is generated based on the data content; If the required permissions are met, a supplementary entry record will be generated directly based on the data content.

9. A method for tracing and managing the quality data of automotive parts throughout their entire lifecycle, as described in claim 1, is characterized in that... Also includes: When a quality defect is detected in a target automotive part during the after-sales maintenance phase, the system can trace back the historical operation records of the quality data to generate all related core quality actions and corresponding execution results of the part throughout its entire life cycle. Based on the core quality actions and their corresponding execution results, identify the upstream responsibility stage corresponding to the quality defect and the key quality action node that caused the defect in the upstream responsibility stage. Encapsulate key quality action nodes and the associated quality data into a quality traceability evidence package. The quality traceability evidence package is pushed to the quality management center, triggering the process of optimizing the quality control strategy for the upstream responsibility stage.

10. A traceability and management system for the entire lifecycle quality data of automotive parts, characterized in that, include: The data acquisition module is used to acquire quality data generated by the target automotive parts throughout their entire life cycle and identify the current life cycle stage of the quality data. The rule retrieval module is used to retrieve the set of quality management rules corresponding to the lifecycle stage from the preset stage management strategy library. The rule parsing module is used to parse the quality management rule set and extract the core quality actions and auxiliary quality actions for verifying quality data. The action verification module is used to traverse the historical operation records of the quality data of the target automotive parts and verify whether all core quality actions have been executed. The calculation module is used to calculate the proportion of the number of auxiliary quality actions that have been executed to the total number of auxiliary quality actions in the quality management rule set if the core quality action has been completed. The judgment module is used to determine whether the ratio value has reached the preset quality data completeness threshold for the corresponding stage; If the proportion value reaches the quality data completeness threshold, the phase advancement module is used to generate state transition instructions and quality task lists to advance the target automotive parts from the current life cycle stage to the next stage based on the quality management rule set. The identifier update module is used to execute state transition instructions and update the lifecycle stage identifier of the target automotive component. The task distribution module is used to distribute the quality task list to the quality management execution nodes associated with the next stage, and trigger the execution of the quality data collection and verification tasks for the next stage.

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