A method for preventing problems in automobile assembly quality reoccurrence and a system thereof

Abstract

The application relates to an automobile assembly quality reoccurrence prevention problem management method and system, which comprises the following steps: detecting current assembly quality according to a problem characteristic prompt word to obtain current quality problem occurrence, wherein the current quality problem occurrence comprises an occurrence type of the current quality problem and a corresponding occurrence frequency; updating the level of a historical quality problem according to the current quality problem occurrence, wherein the updating comprises the following steps: screening quality problems of the same type from the occurrence type of the current quality problem and the occurrence type of the historical quality problem; if the occurrence frequency is 0 within an observation period, the level of the quality problem of the type is reduced; otherwise, the level of the quality problem of the type is increased; and for the remaining types of quality problems after screening, the type of the quality problem is divided into levels based on the type of the quality problem and the corresponding occurrence frequency. The application avoids the problem that a traditional static problem library becomes bloated and ineffective with the prolongation of use time.

Description

Technical Field

[0001] This application relates to the field of vehicle information management, specifically to a management method and system for preventing recurrence of automobile assembly quality problems. Background Technology

[0002] Vehicle prototyping is a crucial step in the overall vehicle development process, and the quality of the prototype directly determines the representativeness of the experimental results. Currently, various assembly problems often arise during the assembly of prototype vehicles. Due to the limited number of assembly personnel, many assembly problems are not effectively organized and passed down, resulting in multiple common assembly issues across different vehicle models. This reduces the representativeness of the prototype vehicles and affects the experimental results.

[0003] As the types of prototype vehicles increase and new mechanisms and technologies emerge one after another, the number of assembly problems also increases, as do the number of recurrence prevention issues and countermeasures. Many old problems are still on record, including some insignificant and long-unresolved issues. This results in a lengthy and complicated checklist for recurrence prevention countermeasures that new vehicles must face during prototype production, increasing the workload. Summary of the Invention

[0004] This application provides a method and system for managing recurrence prevention of automotive assembly quality issues. It can solve the problems in the prior art where the recurrence prevention problem database is outdated, bloated, and lacks specificity, resulting in the need to handle a large number of irrelevant issues during the trial production process and increasing the workload.

[0005] In a first aspect, embodiments of this application provide a method for managing and preventing recurrence of automotive assembly quality problems, characterized in that it includes: The current assembly quality is detected based on the problem feature prompts to obtain the current quality problem occurrence status, which includes the current quality problem occurrence type and the corresponding occurrence frequency. The levels of historical quality problems are updated based on the current occurrence of quality problems. The update includes: selecting quality problems of the same type from the current occurrence types and the occurrence types of historical quality problems; if the occurrence frequency of a quality problem is 0 within the observation period, the level of that type of quality problem is reduced; otherwise, the level of that type of quality problem is increased; for the remaining types of quality problems after screening, the quality problem type is classified into levels based on the type of quality problem and the corresponding occurrence frequency. The level of the historical quality problem is obtained by classifying the historical quality problems in the recurrence prevention quality problem database based on the occurrence type and frequency of the historical quality problems. The recurrence prevention quality problem database is obtained by extracting features from historical quality problems and includes problem feature prompts and historical occurrence frequencies.

[0006] In conjunction with the first aspect, in one implementation, before detecting the current assembly quality based on problem feature prompts to obtain the current quality problem status, the method further includes: extracting features from historical quality problems to generate a recurrence prevention quality problem database containing multiple problem feature prompts. Extract preset information from historical quality issues as problem feature prompts. The preset information includes at least one of vehicle model information, process name, and part name. Associate each problem feature keyword with a corresponding problem description, and establish the historical occurrence frequency for each historical quality problem to generate a database for preventing recurrence of quality problems.

[0007] In conjunction with the first aspect, in one implementation, the historical quality problems in the recurrence prevention quality problem database are divided into at least two levels based on the severity of the problem type and the frequency of historical occurrence; The problem level increases with the severity of the problem type and / or the frequency of its occurrence in the past.

[0008] In conjunction with the first aspect, in one implementation, historical quality issues in the historical quality issue database are divided into three levels, wherein: Level 1 issues are historical quality issues that are occurring for the first time and whose severity is classified as Category 1 or Category 2. Level 2 issues are historical quality issues that occur within a set threshold range and whose severity is classified as Category 1 or Category 2. Level 3 issues are historical quality issues that occur more frequently than a set threshold or whose severity falls into the third or fourth category.

[0009] In conjunction with the first aspect, in one implementation, when a historical quality problem with a severity level of three or four is updated based on the current occurrence of quality problems within the observation period, and the problem has not recurred, the level of the historical quality problem remains at three.

[0010] In conjunction with the first aspect, in one implementation, the current assembly quality is detected based on problem characteristic prompts to obtain the current status of the quality problem, specifically including: Generate a prototype BOM containing problem prompts based on problem characteristic prompts; Based on the issues raised in the trial production BOM, conduct a spot check to confirm the current assembly quality; When the inspection result is unqualified and the quality problem is an assembly problem, the occurrence type and corresponding frequency of the quality problem shall be recorded as the current quality problem occurrence status.

[0011] In conjunction with the first aspect, in one implementation method, a prototype BOM containing problem prompts is generated based on problem characteristic prompt words, specifically including: The feature identifiers in the vehicle BOM are used as the first feature set, and the feature identifiers include at least one of vehicle model information, process name and part name; The problem feature keywords and corresponding recurrence prevention countermeasures in the recurrence prevention quality problem database are used as the second feature set; Establish a mapping relationship between the first feature set and the second feature set. When an element in the first feature set matches a problem feature hint word in the second feature set, the corresponding recurrence prevention countermeasure will be associated with the corresponding element in the first feature set, and a prototype BOM containing the problem hint will be generated.

[0012] In conjunction with the first aspect, in one implementation, the quality problems in the recurrence prevention quality problem database include specific quality problems and general quality problems; The trial production BOM includes a specific trial production BOM and a general trial production BOM.

[0013] In conjunction with the first aspect, in one implementation, the specific quality problem is a quality problem that is only related to a single vehicle type; The general quality issues mentioned are those that are related to two or more different vehicle models.

[0014] Secondly, embodiments of this application provide a vehicle assembly quality recurrence prevention management system, comprising: an assembly quality detection unit and a quality problem database update unit. The assembly quality detection unit is used to detect the current assembly quality based on problem feature prompts to obtain the current occurrence status of quality problems. The current occurrence status of quality problems includes the occurrence type of the current quality problem and its corresponding occurrence frequency. The quality problem database update unit is used to update the level of historical quality problems based on the current occurrence status of quality problems. The update includes: filtering quality problems of the same type from the occurrence types of current quality problems and historical quality problems; if the occurrence frequency is 0 within the observation period, the level of the quality problem of that type is reduced; otherwise, the level of the quality problem of that type is increased; for the remaining types of quality problems after filtering, the quality problem type is classified into levels based on the type of the quality problem and its corresponding occurrence frequency. The level of the historical quality problem is obtained by classifying the historical quality problems in the recurrence prevention quality problem database based on the occurrence type and frequency of the historical quality problems. The recurrence prevention quality problem database is obtained by extracting features from historical quality problems and includes problem feature prompts and historical occurrence frequencies.

[0015] The beneficial effects of the technical solutions provided in this application include: This application provides a method and system for managing and preventing recurring quality issues in automotive assembly. By organizing and summarizing past quality problems and differentiating their importance based on type and frequency, the system makes problem management more hierarchical. During actual assembly, the system prompts workers to inspect based on the characteristics of past problems, making the inspection process more targeted. By establishing a dynamic update mechanism for problem levels based on observation cycles, the system can automatically identify problems that have been effectively controlled and lower their priority, while simultaneously increasing attention to recurring problems. This dynamic adjustment allows the problem database to be automatically optimized and streamlined, avoiding the problem of traditional static problem databases becoming bloated and ineffective over time. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the management method for preventing recurrence of automotive assembly quality problems in this application. Figure 2 This is a schematic diagram of the specific process for step 101; Figure 3 For this application Figure 1 Diagram of the prompt word module and the question management module. Detailed Implementation

[0017] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0018] This application provides a method and system for managing recurrence prevention of automotive assembly quality issues. It can solve the problems in the prior art where the recurrence prevention problem database is outdated, bloated, and lacks specificity, resulting in the need to handle a large number of irrelevant issues during the trial production process and increasing the workload.

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0020] In a first aspect, embodiments of this application provide a method for managing the prevention of recurrence of automotive assembly quality problems, comprising: 101: Detect the current assembly quality based on the problem characteristic prompt words to obtain the current quality problem occurrence status. The current quality problem occurrence status includes the current quality problem occurrence type, namely, A, B, C, and D type problems classified according to severity, as well as the corresponding occurrence frequency, which is the specific number of times the problem has occurred cumulatively within the preset observation period, thereby clarifying the quality risk status of the current assembly process.

[0021] 102: Update the levels of historical quality problems based on the current occurrence of quality problems. The update includes: filtering quality problems of the same type from both current and historical occurrence types; if the frequency of occurrence within the observation period is 0, the level of that type of quality problem is lowered; otherwise, the level is raised. For the remaining types of quality problems after filtering, the level is classified based on the type and corresponding frequency of occurrence. In other words, during the dynamic update of the recurrence prevention quality problem database, a method of type matching and frequency linkage is used to adjust the level, replacing the traditional fixed-period assessment. The system automatically compares the occurrence types of current and historical quality problems to filter out a set of quality problems of the same type. For problems within this set, if the frequency of occurrence within the statistical period is zero, it indicates that the risk is disappearing, and the level of that type of quality problem is automatically lowered to reduce redundant attention; if the frequency of occurrence is not zero, it indicates that the risk persists, and the level of that type of quality problem is raised to strengthen risk management. For the remaining quality issues after filtering, i.e. newly occurring types that do not match historical records, an initial level classification is made for the quality issue type based on its type characteristics and corresponding occurrence frequency.

[0022] In this application, by organizing and summarizing past quality issues and classifying their importance based on their type and frequency, problem management becomes more hierarchical. During actual assembly, the characteristics of past problems prompt staff to conduct inspections, making the inspection process more targeted. By establishing a dynamic update mechanism for problem levels based on observation cycles, the system can automatically identify problems that have been effectively controlled and lower their priority, while increasing attention to recurring problems. This dynamic adjustment allows the problem database to be automatically optimized and streamlined, avoiding the problem of traditional static problem databases becoming bloated and ineffective over time.

[0023] The historical quality problem levels are determined by classifying historical quality problems in the recurrence prevention quality problem database based on their occurrence type and frequency. This database is derived from feature extraction of historical quality problems and includes problem characteristic keywords and historical occurrence frequencies. This level classification mechanism aims to dynamically assess the risk level of problems through quantitative indicators, rather than static storage. Occurrence type distinguishes the attribute categories of problems, while historical occurrence frequency reflects the activity level of problems within a specific observation period. Together, these two factors constitute the basic data dimensions for level determination, ensuring that the recurrence prevention quality problem database can accurately represent the recurrence trend and severity distribution of historical quality problems.

[0024] Therefore, in this application, before detecting the current assembly quality based on problem feature prompts to obtain the current quality problem situation, the method further includes: extracting features from historical quality problems to generate a recurrence prevention quality problem database containing multiple problem feature prompts.

[0025] Building a recurrence prevention database for quality issues is a fundamental preliminary step before performing current assembly quality inspections, aiming to provide data support for subsequent quality risk identification. Specific steps include: first, extracting preset information from historical quality issues as problem feature keywords; then, associating each problem feature keyword with a corresponding problem description, establishing the historical occurrence frequency for each historical quality issue, and generating the recurrence prevention database for quality issues.

[0026] It should be noted that, in this embodiment, the preset information includes at least one of vehicle model information, process name, and part name. The selection of these information fields directly corresponds to the structural features of the vehicle prototype BOM (Bill of Materials), ensuring that problem feature prompts can establish a mapping relationship with specific items in the production list, thereby achieving targeted countermeasure prompts.

[0027] Specifically, key information is extracted from descriptions of known historical quality issues, such as vehicle model information, process name, or part name that caused the issue. This information is used as problem feature keywords (represented by letters A, B, C, etc. for ease of subsequent description) and entered into the recurrence prevention quality issue database along with the corresponding problem description. Then, the recurrence prevention quality issue database is divided according to the existing problem severity judgment criteria and the frequency of problem occurrence.

[0028] In this application, historical quality issues in the recurrence prevention database are categorized into at least two levels based on the severity of the issue type and the frequency of occurrence, thus constructing a hierarchical management architecture. The hierarchical logic follows the principle that the issue level increases with the severity of the issue type and / or the frequency of occurrence, ensuring that high-risk issues receive high-priority attention and that low-risk issues are prevented from excessively consuming management resources.

[0029] Specifically, in this embodiment, historical quality issues in the historical quality issue database are divided into three levels, wherein: Level 1 issues are historical quality issues that occur for the first time and are classified as Category 1 or 2 in severity. In this embodiment, they correspond to issues that occur for the first time (with a historical occurrence frequency of n = 1) and are classified as Category C or 3 in severity. These issues are included in the basic attention scope as newly emerging low-risk issues. Level 2 issues are historical quality issues that occur within a set threshold range and are classified as Category 1 or 2 in severity. In this embodiment, they correspond to issues that occur multiple times (2 < n ≤ 10) and are classified as Category C or 3 in severity. These issues are included in the monitoring scope as medium-frequency risks, aiming to prevent general issues from becoming chronic problems. Level 3 issues are historical quality issues that occur more frequently than a set threshold or are classified as Category 3 or 4 in severity. In this embodiment, they correspond to issues that occur multiple times (n > 10) or are classified as Category A or 4 in severity. These issues are included in the key control scope as high-frequency or high-severity issues, aiming to forcibly block the outflow of serious defects.

[0030] In other words, the recurrence prevention quality problem database can be divided into three levels. Level 1 problems are defined as those occurring for the first time (historical occurrence frequency is 1, expressed as n) and with a severity level of C or D. Level 2 problems are defined as those occurring multiple times (2 < n ≤ 10) and with a severity level of C or D. Level 3 problems are defined as those occurring multiple times (n > 10) or with a severity level of A or B (the severity level mentioned in this paper can be determined by company-level standards to classify problems into A, B, C, and D, where A severity > B severity > C severity > D severity). In this application, Level 1 focuses on initial low-risk problems, Level 2 monitors medium-frequency risks, and Level 3 focuses on managing high-frequency or high-severity problems. Through a tiered setting of frequency thresholds n=1, 2 < n ≤ 10, and n > 10, combined with the weight differences in severity levels (A, B, C, D), it is ensured that serious problems, regardless of frequency, are included in the highest level of management, while general problems are gradually escalated as frequency accumulates.

[0031]

[0032] Meanwhile, this hierarchical status is not static and fixed, but serves as the initial state benchmark for the problem escalation and de-escalation mechanism. When a new problem occurs or the frequency of an old problem changes, the system recalculates the level based on this standard to ensure that the database evolves dynamically with actual production, avoids an outdated and bloated problem database, and improves the accuracy and efficiency of risk avoidance during the trial production stage.

[0033] To further improve the accuracy of problem matching, problems in each level of the database are categorized into specialized and general categories. Specifically, some problems that only occur in pure electric vehicles are categorized as BEV-specific, while others that only occur in hydrogen fuel cell vehicles are categorized as FCV-specific. Problems arising from mechanisms present in all vehicle types are categorized as general categories. This categorization method allows for the selective retrieval of specialized or general problems from the database based on the specific configuration of the prototype vehicle when generating guidance documents. During the matching process, the system defines a set of prompt terms in the vehicle's BOM and a set of prompt terms in the recurrence prevention database. Through mapping relationships, key points of the problem are associated with corresponding terms in the vehicle's BOM. If a prompt term is a subclass, it is mapped through hierarchical relationships to avoid interference from irrelevant problems. This addresses the situation in actual production where prototyping a pure electric vehicle requires checking for problems specific to previous gasoline-powered vehicles, ensuring the relevance of the guidance documents.

[0034] Based on the above embodiments, in this embodiment, when the level of a historical quality problem with a severity of third or fourth category is updated according to the current occurrence of quality problems within the observation period, the problem has not recurred and remains at the third-level problem level.

[0035] Specifically, to prevent recurrence and avoid an bloated database of quality issues, the system sets a fixed observation period for escalation and escalation of issues. This period defaults to 180 calendar days, but can be flexibly configured according to the development cycle of the actual production model or quality control requirements to adapt to the pace of different projects. This ensures that the observation window can cover a sufficiently long period of production stability while responding promptly to changes in quality.

[0036] Within each observation period, the system automatically collects and statistically analyzes the current occurrence of various quality issues, and updates the levels of historical quality issues based on their occurrence types and severity. The specific level classification and update logic follows the principle of combining severity priority with frequency accumulation, ensuring that high-risk issues receive high-priority attention while avoiding excessive consumption of management resources for low-risk issues.

[0037] First, for historical quality issues classified as Category I or II (i.e., Category A or B) in terms of severity, due to their high potential risk, the system directly classifies them into the Level 3 database and permanently retains them. These issues do not participate in subsequent downgrade processes and remain at the highest control level regardless of changes in their occurrence frequency during the observation period, ensuring that no serious defects are overlooked. Second, for historical quality issues classified as Category III or IV (i.e., Category C or D) in terms of severity, the system performs refined classification based on their historical occurrence frequency n: when the occurrence frequency n equals 1, it is defined as an initial low-risk issue and recorded in the Level 1 database, aiming to capture potential risks at their inception; when the occurrence frequency n is greater than 2 and less than or equal to 10, it is defined as a medium-frequency risk issue and recorded in the Level 2 database, aiming to prevent general issues from becoming chronic problems; when the occurrence frequency n is greater than 10, it is defined as a high-frequency risk issue and recorded in the Level 3 database, aiming to forcibly block the outflow of serious defects.

[0038] After the observation period ends, the system updates the recurrence prevention quality problem database in batches according to the preset degradation expression, forming a closed-loop management. For problems in the first-level database, if the frequency n does not increase during the period, it indicates that the initial problem has not recurred, and the system performs a deletion operation (n→0), removing it from the database and freeing up storage space; if the frequency increases, it is upgraded to the second- or third-level database based on the increment. For problems in the second-level database, if the frequency n does not increase during the period, it indicates that the risk is stabilizing, and the system downgrades its level to the first-level database (n→1), reducing the resources for attention; if the frequency increases, it is upgraded to the third-level database. For problems in the third-level database, they are handled according to severity: if they are of category A or B, they are retained in the third-level database; if they are of category C or D and the frequency n is 0 during the period (i.e., they have not recurred), it indicates that the high-frequency risk has been controlled, and the system downgrades its level to the second-level database (n→2-10), achieving risk degradation; if the frequency still occurs, it is retained in the third-level database.

[0039] For ease of understanding, suppose there is an assembly problem with a severity level of C. Initially, its frequency (n) is 5, and it is classified into the second-level database according to the rules. After a 180-day observation period, if the system detects that the frequency of this problem has not increased during this period and remains at 5, then a downgrade logic is triggered. The problem is downgraded from the second-level database to the first-level database, and its frequency marker is updated to 1. If the problem still does not occur in the next period and its frequency does not increase, it is downgraded again until it is deleted from the database, thus automatically removing outdated problems. Conversely, if the frequency of this problem increases to 12 during the observation period, it is directly upgraded to the third-level database for focused management, regardless of its original severity level. For problems with a severity level of A, regardless of the frequency (n), they are always retained in the third-level database and are not downgraded or deleted, thus ensuring the traceability of high-risk problems and ensuring a combination of rigor and flexibility in quality management.

[0040] Based on the above embodiments, in this embodiment, the current assembly quality is detected according to problem feature prompts to obtain the current quality problem situation, specifically including: First, based on the problem characteristic prompts, a prototype BOM containing problem prompts is generated. This process combines the vehicle BOM with the recurrence prevention quality problem database, matching problem information with the vehicle BOM using the prompts to generate a vehicle prototype BOM with recurrence prevention countermeasure prompts. This vehicle prototype BOM serves as the actual guidance for the operation. To facilitate subsequent expression, letters A1, A2, B1, B2, etc., are used to represent specific countermeasure content. During matching, the system defines a set of prompts in the vehicle BOM and a set of prompts in the recurrence prevention quality problem database. Through mapping relationships, problem points are associated with corresponding words in the vehicle BOM. If a prompt is a subclass, a hierarchical mapping is used to ensure that the generated guidance document only contains quality risk points related to the current vehicle model configuration, avoiding interference from outdated problems of unrelated models in the work process.

[0041] Then, based on the problem prompts in the trial production BOM, the current assembly quality is checked and confirmed. When the inspection result is unqualified and the quality problem is an assembly problem, the occurrence type and corresponding frequency of the quality problem are recorded as the current quality problem occurrence status. In specific execution, during the operation, the inspection is confirmed as OK or NG according to the problem prompts. The system receives the inspection results. If NG occurs, it is notified to the supervisor to determine whether the NG is caused by the final assembly. This judgment step is used to filter quality anomalies caused by non-assembly factors. If it is an NG caused by the final assembly, the system re-enters the problem into the recurrence prevention quality problem database, increases the problem frequency and enters the corresponding level database, for example, the frequency n is incremented by 1, and the level is re-determined based on the new frequency; if it is not caused by the final assembly, it is judged as OK, and the frequency is not updated, ensuring that the database only records valid assembly quality risks and maintains the accuracy of level classification.

[0042] After the vehicle assembly line rolls off the production line, quality inspectors can conduct final inspections based on the OK items (problems identified in the prototype BOM). If an OK item fails to meet the requirements, it can be re-entered, increasing its frequency and adding it to the corresponding level database to strengthen the control level of high-risk issues. If an OK item meets the requirements, the frequency of the issue is reduced; if downgrade conditions are triggered, the issue is downgraded sequentially. The downgrade logic is based on preset frequency thresholds and observation periods. For example, when the frequency of an issue drops below the next level threshold within a period, the system automatically downgrades it from the level 3 database to the level 2 database, or from the level 2 database to the level 1 database, until the frequency reaches zero and it is deleted from the database. By reducing the frequency through final inspection pass feedback, combined with periodic upgrade and downgrade rules, the recurrence prevention quality problem database is dynamically updated, ensuring that the problems in the database always reflect the true risk level of the current assembly quality and preventing old problems from occupying management resources for a long time.

[0043] Specifically, based on the problem's characteristic prompts, a prototype BOM containing problem prompts is generated, including: The first feature set is the feature identifiers in the whole vehicle BOM, which include at least one of vehicle model information, process name and part name. This set represents all structural elements and process paths of the vehicle to be produced. The second feature set is the problem feature prompts and corresponding recurrence prevention countermeasures in the recurrence prevention quality problem database. The recurrence prevention countermeasures are sub-items under the prompts. Multiple sub-items can appear under one prompt. This set represents the historically accumulated quality risk knowledge base.

[0044] Then, a mapping relationship is established between the first feature set and the second feature set. When an element in the first feature set matches a problem feature hint word in the second feature set, the corresponding recurrence prevention countermeasure is associated with the corresponding element in the first feature set, generating a prototype BOM containing the problem hint. In this process, the second feature set is a subset of the first feature set, ensuring that only risk points existing in the overall vehicle structure are extracted, avoiding redundancy of irrelevant information, and achieving structured alignment between risk data and production data.

[0045] The recurrence prevention quality problem database includes both specific quality problems and general quality problems; the prototype BOM includes specific prototype BOMs and general prototype BOMs. Specific quality problems are those related only to a single vehicle type, such as assembly risks of a specific powertrain system; general quality problems are those related to two or more different vehicle types, such as torque risks of common fasteners. The complete vehicle prototype BOM needs to be mapped and matched in two stages: Complete Vehicle Prototype BOM = BOM1 (Specific) + BOM2 (General). This step-by-step matching ensures the priority identification of specific risks and comprehensive coverage of general risks, avoiding omissions or conflicts caused by a single matching logic. First, a specific version (BOM1) is mapped and matched based on the information of the prototype vehicle. For example, if a certain FCV model needs to generate a prototype BOM with recurrence prevention countermeasures, first, based on the FCV vehicle characteristics, the FCV specific risk is selected from the recurrence prevention quality problem database, excluding interference items specific to non-FCV models. Then, mapping and matching are performed to ensure the specific risk is targeted.

[0046] During the mapping and matching process, the prompts A, B, C... in the vehicle BOM are treated as a set of elements S, with the expression: S={A,B,C...}. The prompts A, B, C... and recurrence prevention countermeasures A1, A2, B1, B2... in the recurrence prevention quality problem database are also treated as a set of elements Q, with the expression: Q={A / A1 / A2,B / B1 / B2,....}. Set Q is a subset of set S (Q∈S), meaning that the prompts in the recurrence prevention quality problem database correspond to the prompts in the vehicle BOM. Define mapping f: Q→S represents associating the key points of the recurrence prevention quality problem database (such as A1, A2) with the corresponding words (such as A) in the whole vehicle BOM. The matching logic is as follows: if the prompt words in set Q are completely consistent with the prompt words in set S, then the mapping is direct; if the prompt words in set Q are a subclass of a prompt word in set S (such as A1 belonging to A), then the mapping is done through hierarchical relationship. For example, the risk at the part level is mapped to the item at the component level BOM. The tree structure of the BOM is used to realize the risk association between the upper and lower levels. Through the above steps, a prototype BOM1 with recurrence prevention countermeasures specifically for FCV models is obtained.

[0047] Subsequently, the recurrence prevention library was screened using the same logic, and the mapping and matching were used to obtain the prototype BOM2 of FCV models with recurrence prevention countermeasures. This step ensures that common risks across models are not overlooked and supplements the general process risks not covered by the special matching.

[0048] Combining these two methods yields the complete vehicle prototype BOM with desired recurrence prevention measures. This BOM integrates all risk countermeasures for specific and general issues, forming a complete operational guideline. Through two mapping and matching processes, the system can distinguish between vehicle-specific and general risks, ensuring both the relevance of specific issues and the comprehensiveness of general issues. In the final generated complete vehicle prototype BOM, each relevant BOM element is associated with a specific recurrence prevention measure, achieving a precise binding between quality risks and production structure. This provides a structured data foundation for subsequent inspections and feedback, ensuring the accuracy and completeness of the production guidance documents.

[0049] For ease of understanding, see Figure 3As shown, the basic data units of the recurrence prevention quality problem database include a prompt word module and a problem management module. The prompt word module contains prompt words such as problem attributes, line codes, assembly processes, and part names. Among them, problem attributes are used to distinguish the scope of application of the problem, such as general items, fuel-specific items, BEV-specific items, etc. This is the key identifier for the system to filter BOM1 or BOM2, and determines whether the record is loaded by a specific vehicle model. Line codes, such as T2 and C2, correspond to specific production lines and are used to locate the physical production line where the problem occurs. Assembly processes and part names locate the operation links and physical objects where the problem occurs, forming elements in the feature set S used for matching.

[0050] The problem management module includes a description of assembly problem points, recurrence prevention measures, library level, problem type, and frequency. Among them, library level and problem type determine the priority and control intensity of the problem, while frequency serves as the calculation basis for the upgrade and downgrade mechanism and is directly related to the retention and deletion status of the problem in the library.

[0051] exist Figure 3 In the example, the common issues demonstrate a risk management logic applicable across vehicle models. These data constitute the common prototyping BOM2 section within the prototyping BOM. For instance, in the common-item-T2 line record, the assembly process is "installing tweeters to the front strut upper guard plate," and the assembly problem is described as "poor or loose tweeter connection, leading to functional failure." The corresponding recurrence prevention countermeasure is "pull and press after connection to confirm good condition; apply paint markings after inspection." This problem is marked as a secondary library level, category C, and frequency 3 times, indicating a medium-frequency risk. When generating a prototyping BOM for any vehicle model passing through the T2 line, the system will invoke this countermeasure through prompt word matching to ensure consistency in basic assembly quality. In another example, in the common-item-C2 line, the process is "installing left and right rear shock absorbers to the lower control arm," and the problem is "incorrect bolt hole direction for the rear shock absorber and lower control arm connection." The countermeasure is "assemble according to process document requirements and confirm key points; check according to assembly key points," and is classified as secondary library level, category C, and frequency 2 times. These general data items are not dependent on a specific powertrain platform and are applicable to multiple vehicle models. Therefore, they are classified as general items, which avoids the problem of repeatedly entering the same data in different vehicle model databases and reduces database redundancy.

[0052] In summary, this application achieves risk avoidance during the product trial production stage by establishing a database to prevent recurrence of quality problems and using the mapping and matching of problem feature prompts with the vehicle BOM to generate a trial production BOM containing countermeasures for historical problems. Potential risk points are marked in advance, reducing repeated defects in product trial production and improving trial production quality. In particular, through special and general item classification, it ensures that the risks of specific powertrain models are accurately identified and irrelevant problems are effectively eliminated.

[0053] To achieve dynamic management and control of quality issues in digital prototyping, a tiering mechanism based on the severity of the problem and its historical frequency is established. Combined with a preset observation period, long-term unresolved old records are automatically removed, ensuring the effectiveness and relevance of prevention and countermeasures for recurrence during prototyping and final assembly. This reduces workload and avoids the need to inspect for issues specific to fuel-powered vehicles when prototyping a pure electric vehicle. At the same time, the problem frequency is updated cyclically through work feedback, ensuring that the database evolves dynamically with actual production, achieving differentiated and precise management of quality risks and optimized resource allocation.

[0054] Secondly, this application provides an automotive assembly quality recurrence prevention management system, comprising: an assembly quality detection unit and a quality problem database update unit. The assembly quality detection unit is used to detect the current assembly quality based on problem feature prompts to obtain the current quality problem occurrence status, which includes the occurrence type and corresponding frequency of the current quality problem. The quality problem database update unit is used to update the level of historical quality problems based on the current quality problem occurrence status. The update includes: selecting quality problems of the same type from the occurrence types of current and historical quality problems; if the occurrence frequency is 0 within the observation period, the level of the quality problem of that type is reduced; otherwise, the level of the quality problem of that type is increased. For the remaining types of quality problems after filtering, the quality problem type is classified into levels based on the type and corresponding frequency of the quality problem. The level of historical quality problems is obtained by classifying historical quality problems in the recurrence prevention quality problem database based on the occurrence type and historical frequency of historical quality problems. The recurrence prevention quality problem database is obtained by feature extraction of historical quality problems and includes problem feature prompts and historical frequency of occurrence.

[0055] By organizing and summarizing past quality issues and classifying their importance based on type and frequency, problem management becomes more hierarchical. During actual assembly, the system prompts staff to conduct inspections based on the characteristics of past problems, making the inspection process more targeted. By establishing a dynamic update mechanism for problem levels based on observation cycles, the system can automatically identify problems that have been effectively controlled and lower their priority, while increasing attention to recurring problems. This dynamic adjustment allows the problem database to be automatically optimized and streamlined, avoiding the problem of traditional static problem databases becoming bloated and ineffective over time.

[0056] The functions of each module in the above-mentioned automobile assembly quality recurrence prevention management system correspond to the steps in the above-mentioned automobile assembly quality recurrence prevention management method embodiment, and their functions and implementation processes will not be described in detail here.

[0057] Thirdly, embodiments of this application provide a vehicle assembly quality recurrence prevention management device, which can be a personal computer (PC), laptop computer, server, or other device with data processing capabilities.

[0058] In this embodiment, the vehicle assembly quality recurrence prevention management device may include a processor, a memory, a communication interface, and a communication bus.

[0059] The communication bus can be of any type and is used to interconnect the processor, memory, and communication interface.

[0060] The communication interface includes input / output (I / O) interfaces, physical interfaces, and logical interfaces used for interconnecting components within the automotive assembly quality recurrence prevention and control management equipment, as well as interfaces used for interconnecting the equipment with other devices (such as other computing devices or user equipment). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user equipment can be displays, keyboards, etc.

[0061] Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0062] The processor can be a general-purpose processor, which can call the automotive assembly quality recurrence prevention management program stored in the memory and execute the automotive assembly quality recurrence prevention management method provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the automotive assembly quality recurrence prevention management program is called can be referred to in the various embodiments of the automotive assembly quality recurrence prevention management method of this application, and will not be repeated here.

[0063] Fourthly, embodiments of this application also provide a computer-readable storage medium.

[0064] The present application stores an automotive assembly quality recurrence prevention management program on a computer-readable storage medium, wherein when the automotive assembly quality recurrence prevention management program is executed by a processor, it implements the steps of the automotive assembly quality recurrence prevention management method as described above.

[0065] The method implemented when the automobile assembly quality recurrence prevention problem management procedure is executed can be referred to in the various embodiments of the automobile assembly quality recurrence prevention problem management method of this application, and will not be repeated here.

[0066] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0067] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.

[0068] In the description of the embodiments of this application, terms such as "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a concrete manner.

[0069] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0070] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0071] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0072] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A management method for preventing recurrence of quality problems in automobile assembly, characterized in that, It includes: The current assembly quality is detected based on the problem feature prompts to obtain the current quality problem occurrence status, which includes the current quality problem occurrence type and the corresponding occurrence frequency. The levels of historical quality problems are updated based on the current occurrence of quality problems. The update includes: selecting quality problems of the same type from the current occurrence types and the occurrence types of historical quality problems; if the occurrence frequency of a quality problem is 0 within the observation period, the level of that type of quality problem is reduced; otherwise, the level of that type of quality problem is increased; for the remaining types of quality problems after screening, the quality problem type is classified into levels based on the type of quality problem and the corresponding occurrence frequency. The level of the historical quality problem is obtained by classifying the historical quality problems in the recurrence prevention quality problem database based on the occurrence type and frequency of the historical quality problems. The recurrence prevention quality problem database is obtained by extracting features from historical quality problems and includes problem feature prompts and historical occurrence frequencies.

2. The method for preventing recurrence of automotive assembly quality problems as described in claim 1, characterized in that, Before detecting the current assembly quality based on problem feature keywords to obtain the current quality problem status, the method further includes: extracting features from historical quality problems to generate a recurrence prevention quality problem database containing multiple problem feature keywords. Extract preset information from historical quality issues as problem feature prompts. The preset information includes at least one of vehicle model information, process name, and part name. Associate each problem feature keyword with a corresponding problem description, and establish the historical occurrence frequency for each historical quality problem to generate a database for preventing recurrence of quality problems.

3. The method for preventing recurrence of automotive assembly quality problems as described in claim 1, characterized in that: The historical quality issues in the recurrence prevention quality issue database are divided into at least two levels based on the severity of the problem type and the frequency of historical occurrence; The problem level increases with the severity of the problem type and / or the frequency of its occurrence in the past.

4. The method for preventing recurrence of automotive assembly quality problems as described in claim 3, characterized in that, The historical quality issues in the historical quality issue database are divided into three levels, among which: Level 1 issues are historical quality issues that are occurring for the first time and whose severity is classified as Category 1 or Category 2. Level 2 issues are historical quality issues that occur within a set threshold range and whose severity is classified as Category 1 or Category 2. Level 3 issues are historical quality issues that occur more frequently than a set threshold or whose severity falls into the third or fourth category.

5. The method for preventing recurrence of automotive assembly quality problems as described in claim 4, characterized in that: Historical quality issues classified as Category 3 or 4 in severity are maintained at Category 3 if the issue has not recurred when the level of the historical quality issue is updated based on the current occurrence of quality issues within the observation period.

6. The method for preventing recurrence of automotive assembly quality problems as described in claim 1, characterized in that, The current assembly quality is inspected based on problem characteristic prompts to determine the current quality issue, specifically including: Generate a prototype BOM containing problem prompts based on problem characteristic prompts; Based on the issues raised in the trial production BOM, conduct a spot check to confirm the current assembly quality; When the inspection result is unqualified and the quality problem is an assembly problem, the occurrence type and corresponding frequency of the quality problem shall be recorded as the current quality problem occurrence status.

7. The method for preventing recurrence of automotive assembly quality problems as described in claim 6, characterized in that, Based on the problem characteristic prompts, generate a prototype BOM containing the problem prompts, specifically including: The feature identifiers in the vehicle BOM are used as the first feature set, and the feature identifiers include at least one of vehicle model information, process name and part name; The problem feature keywords and corresponding recurrence prevention countermeasures in the recurrence prevention quality problem database are used as the second feature set; Establish a mapping relationship between the first feature set and the second feature set. When an element in the first feature set matches a problem feature hint word in the second feature set, the corresponding recurrence prevention countermeasure will be associated with the corresponding element in the first feature set, and a prototype BOM containing the problem hint will be generated.

8. The method for preventing recurrence of automotive assembly quality problems as described in claim 6, characterized in that: The quality issues in the recurrence prevention quality problem database include specific quality issues and general quality issues; The trial production BOM includes a specific trial production BOM and a general trial production BOM.

9. The method for preventing recurrence of automotive assembly quality problems as described in claim 8, characterized in that: The specific quality issues mentioned are those related to only a single vehicle model type. The general quality issues mentioned are those that are related to two or more different vehicle models.

10. A vehicle assembly quality recurrence prevention management system, characterized in that, It includes: An assembly quality inspection unit is used to inspect the current assembly quality based on problem feature prompts to obtain the current quality problem occurrence status, which includes the current quality problem occurrence type and corresponding occurrence frequency; The quality problem database update unit is used to update the levels of historical quality problems based on the current occurrence of quality problems. The update includes: filtering quality problems of the same type from the current occurrence types and the occurrence types of historical quality problems; if the occurrence frequency is 0 within the observation period, the level of the quality problem of that type is reduced; otherwise, the level of the quality problem of that type is increased; for the remaining types of quality problems after filtering, the quality problem is classified into different levels based on its type and corresponding occurrence frequency. The level of the historical quality problem is obtained by classifying the historical quality problems in the recurrence prevention quality problem database based on the occurrence type and frequency of the historical quality problems. The recurrence prevention quality problem database is obtained by extracting features from historical quality problems and includes problem feature prompts and historical occurrence frequencies.

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