Method for transmitting process steps in mass tool

Abstract

The invention provides a method for transmitting process steps in a quality tool, relates to the technical field of manufacturing industry management and data processing, and realizes automatic transmission of the process steps and special characteristics among APQP, PFMEA, CP, DFMEA, MSA and SPC modules by fusing a bill of materials, production parameters and process images to generate an adaptive semantic association model and combining an adaptive dynamic topology network. According to the process steps, an APQP flow chart is generated and then automatically transmitted to a PFMEA generation report, special characteristics are imported into APQP from a DFMEA list and marked on CP, and finally the APQP is output to an MSA / SPC module to generate a measurement task and a process capability report, so that the consistency of data in different files is ensured. Compared with a fixed transmission path described by a file, by dynamically adjusting the network topology weight, monitoring the data integrity in real time and automatically repairing format errors or omissions, an automatic transmission mechanism remarkably improves the collaboration efficiency between modules, and errors caused by manual intervention are reduced.

Description

Technical Field

[0001] This invention relates to the fields of manufacturing management and data processing technology, specifically a method for transferring process steps in quality tools. Background Technology

[0002] In modern manufacturing, quality management is a crucial link in ensuring that products meet design requirements and customer expectations. With increasing production complexity, especially in demanding industries such as automotive and aerospace, quality tools such as APQP (Advanced Product Quality Planning), PFMEA (Process Failure Mode and Effects Analysis), CP (Control Planning), DFMEA (Design Failure Mode and Effects Analysis), MSA (Measurement System Analysis), and SPC (Statistical Process Control) are widely used in product development and production processes. These tools systematically identify potential risks, develop control measures, and monitor the production process to ensure product quality and consistency. However, existing technologies face numerous challenges in transferring process steps and special characteristics between these quality tool modules. Currently, data transfer in quality tools mainly relies on manual operation or simple software interfaces, which easily leads to problems such as mismatches in descriptions of step numbers, failure modes, or special characteristics between different modules. Furthermore, there is a lack of dynamic optimization and traceability support for the data transfer process. In complex production environments, real-time changes in equipment status and quality feedback require data transfer paths to adapt dynamically.

[0003] The existing technology has the following technical problems when used: Problem 1: In existing technologies, the transfer of process steps between quality tools (such as APQP, PFMEA, CP, DFMEA, MSA, SPC) mainly relies on manual operation or simple file import and export. This makes it difficult to ensure the consistency of data throughout the transfer process. In particular, for data with special characteristics, it is difficult to distinguish them in a timely manner, which can easily lead to loss or omission. Furthermore, it is inefficient, lacks real-time performance, and cannot dynamically adjust the transferred content according to production parameters or equipment status. As a result, high-risk steps cannot be transferred and processed in a timely manner, ultimately affecting product quality and production efficiency. Question 2: In existing technologies, the data transmission methods of quality tools usually adopt static paths. The transmission path is fixed and cannot be dynamically adjusted according to equipment status, output fluctuations or quality feedback. This results in high-risk steps or special characteristics not being prioritized, and lacks dynamic optimization and traceability support. Especially in multi-party collaboration scenarios, data tampering or omission may cause quality disputes and is difficult to adapt to complex production environments and strict compliance requirements. Summary of the Invention

[0004] To overcome the aforementioned deficiencies of the prior art, the present invention is achieved through the following technical solution: a method for transferring process steps in a quality tool, the method comprising: Acquire bill of materials data, production parameters, and process flow diagrams, fuse them to form multimodal data, and generate an adaptive semantic association model; The semantic association model is parsed, and the network topology weights are dynamically adjusted based on equipment status, output fluctuations, and quality feedback to generate an adaptive dynamic topology network and data packets to be transmitted. Based on the adaptive dynamic topology network, functional topology and failure topology are constructed. Failure risk priority is calculated by combining real-time production data, and an adaptive risk control strategy is generated. High-risk step data is transmitted to the CP module through the adaptive dynamic topology network to generate a PFMEA report. Based on the failure topology and adaptive risk control strategy, an initial control plan is automatically generated, inconsistencies with PFMEA module data are detected and repaired in real time, and a complete CP report is generated. Based on the semantic association model, a list of product special characteristics is generated in the DFMEA module, the special characteristic data is imported into the APQP module to form a dynamic special characteristic topology diagram, and the special characteristic data is transferred to the MSA module and SPC module to generate measurement task and process capability analysis report. The system monitors data transmission between modules, detects format errors or data omissions, automatically triggers an adaptive repair mechanism to complete or correct data, and provides suggestions for optimizing transmission paths through adaptive topology network analysis. Each step of data transmission is recorded on a distributed trust ledger, generating time-series encrypted signatures and multi-dimensional verification codes.

[0005] Preferably, the step of acquiring bill of materials data, production parameters, and process flow diagrams, fusing them to form multimodal data, and generating an adaptive semantic association model includes: Extract process descriptions, step numbers, and relationships from the process flow diagram to generate structured image data; Parse bill of materials data from product data management system and product lifecycle management system to extract part numbers, material attributes and hierarchical relationships; Real-time production parameters, including temperature, pressure, and speed, are collected from sensors on the production line to generate time-series datasets; Image data, bill of materials data, and production parameters are mapped to a unified semantic space. Semantic association rules are used to match the relationships between steps, parts, and parameters to form an adaptive semantic association model. The semantic association model is stored in a distributed knowledge base, and a dynamic update mechanism is set up to synchronize the changes in production parameters every minute to maintain the real-time performance of the model.

[0006] Preferably, the step of parsing the semantic association model and dynamically adjusting the network topology weights based on device status, output fluctuations, and quality feedback to generate an adaptive dynamic topology network and data packets to be transmitted includes: The logical relationships between process steps are extracted from the semantic association model to generate an initial topology network, where nodes represent steps and edges represent dependencies between steps. Real-time acquisition of equipment operating status, output fluctuation data, and quality feedback data; calculation of dynamic weights for each step node; adjustment of edge connection strength of the topology network based on weight changes; and prioritization of strengthening the transmission paths of high-risk steps. High-risk steps are marked as priority delivery objects, generating data packets to be delivered that contain step priorities and data content, and the adjusted topology network is stored as an adaptive dynamic topology network.

[0007] Preferably, the step of constructing a functional topology and a failure topology based on the adaptive dynamic topology network, calculating failure risk priorities by combining real-time production data, and generating an adaptive risk control strategy includes: The functional attributes of the steps are extracted from the adaptive dynamic topology network to generate a functional topology that maps the association between the steps and the production target. The potential failure modes of the steps are analyzed to generate a failure topology that records the causal relationship between the failure modes and the steps. By combining real-time production data, the priority of failure risks is calculated, and the severity, probability of occurrence, and detection difficulty are comprehensively considered through a weighted formula. Based on the failure topology and failure risk priority, an adaptive risk control strategy is generated. The adaptive risk control strategy includes adjusting process parameters, adding detection points, and optimizing the step sequence. The adaptive risk control strategy and the corresponding data are encapsulated into a data packet and transmitted to the CP module through an adaptive dynamic topology network.

[0008] Preferably, based on the failure topology and adaptive risk control strategy, an initial control plan is automatically generated, inconsistencies with the PFMEA module data are detected and corrected in real time, and a complete CP report is generated, including: Key failure modes and control measures are extracted from the failure topology to generate a preliminary control plan that includes control points, detection methods, and responsibility allocation. The preliminary control plan is compared with the step numbers, failure modes and control measures of the PFMEA module data to identify inconsistencies. Based on the production parameters, missing control points are automatically filled in and parameter values ​​are corrected. Users interact with the system to adjust and control the initial control plan, confirm the correction results, and finally generate a complete CP report containing all control points and correction records, which is stored in a distributed knowledge base.

[0009] Preferably, the step of generating a list of product-specific characteristics in the DFMEA module based on the semantic association model includes: Extract the part attributes and design requirements of the bill of materials from the semantic association model, identify key product characteristics, analyze the relationship between product characteristics and design functions, and generate a product characteristic list. Based on the design failure modes, characteristics that have a significant impact on product quality and safety are screened and marked as special characteristics; The list of special characteristics is associated with production parameters to verify its feasibility in actual production. The verified list of product special characteristics is then stored in a distributed knowledge base, and access permissions are set for subsequent modules to call.

[0010] Preferably, the step of importing special characteristic data into the APQP module to form a dynamic special characteristic topology graph includes: Extract the product's special characteristics list from the distributed knowledge base and import it into the APQP module; Based on the semantic association model, a topological relationship between special characteristics is generated, where nodes represent special characteristics and edges represent dependencies between special characteristics. The priority weights of nodes in the topological graph are dynamically adjusted according to the production plan and quality requirements. The dynamic special characteristic topology graph is associated with the dynamic topology network of process steps to generate a cross-module collaborative mapping, and the dynamic special characteristic topology graph is stored.

[0011] Preferably, the step of transferring special characteristic data to the MSA module and SPC module to generate a measurement task and process capability analysis report includes: Extract the special characteristics that need to be measured from the dynamic special characteristic topology graph, and generate a measurement task list containing measurement parameters and frequencies; In the MSA module, a calibration plan for the measurement equipment is set based on the measurement task list to verify the repeatability and reproducibility of the measurement system; In the SPC module, production data related to special characteristics are collected, process capability index is calculated, and statistical analysis charts are generated. Verify the consistency between the measurement task list and the product special characteristics list, align the analysis results with production goals, generate a report containing measurement results and process capability analysis, and store it in a distributed knowledge base.

[0012] Preferably, during data transmission between the monitoring modules, if format errors or data omissions are detected, an adaptive repair mechanism is automatically triggered to complete or correct the data. Adaptive topology network analysis is used to provide optimized transmission path suggestions, including: Real-time monitoring of data transfer between modules, checking data format, step numbering, and content integrity; By comparing the expected data in the semantic association model, format errors and data omissions are identified, and relevant patterns are extracted from historical data based on production data to generate repair suggestions. It automatically performs repair operations, completing missing steps and standardizing data formats. Based on the weight analysis of the adaptive dynamic topology network, the data transmission path is optimized, and optimization suggestions are given.

[0013] Preferably, the step of recording each step of data transmission on a distributed trust ledger and generating a time-series encrypted signature and a multi-dimensional verification code includes: For each step of data transmission, a record containing a timestamp, module identifier, and data content is generated. A time-series encrypted signature is generated using an encryption algorithm, and a multi-dimensional verification code is generated, covering data content, transmission path, and consistency between modules, for multi-party collaborative verification. Records are stored in a distributed trust ledger, and security is protected through a multi-node consensus mechanism. A dynamic traceability query interface is set up, allowing users to query transmission records by time range, module type, and risk level, and generate a visual traceability path diagram. Beneficial effects

[0014] This invention provides a method for transferring process steps in a quality tool. It has the following beneficial effects: This invention generates an adaptive semantic association model by integrating bill of materials, production parameters, and process flow diagrams. Combined with an adaptive dynamic topology network, it achieves automatic transfer of process steps and special characteristics among the APQP, PFMEA, CP, DFMEA, MSA, and SPC modules. Process steps are automatically transferred from the APQP flowchart to PFMEA to generate reports, while special characteristics are imported from the DFMEA list into APQP and identified in CP. Finally, they are output to the MSA / SPC modules to generate measurement tasks and process capability reports, ensuring data consistency across different files. Compared to the fixed transfer paths described in the documentation, by dynamically adjusting network topology weights and monitoring data integrity in real time, automatically correcting format errors or omissions, the automated transfer mechanism significantly improves inter-module collaboration efficiency and reduces errors caused by manual intervention.

[0015] 2. This invention employs an adaptive dynamic topology network and a distributed trust ledger, combined with failure risk priority calculation and an adaptive repair mechanism, to achieve dynamic optimization of process steps and special characteristics in quality tools. Compared to simple automatic file capture, this invention dynamically adjusts topology weights by collecting real-time equipment status and production data, prioritizing the transmission of high-risk steps and generating encrypted signatures and multi-dimensional verification codes recorded in the trust ledger to ensure the tamper-proof and traceability of the transmission process. Simultaneously, it uses cross-module collaborative mapping, such as the transmission of special characteristics from DFMEA to MSA / SPC, and reduces transmission path redundancy through optimization suggestions, improving the reliability and transparency of data transmission. It also meets the needs of multi-party collaborative verification, reduces disputes in quality management, shortens the quality control cycle, and saves costs. Attached Figure Description

[0016] Figure 1 A flowchart illustrating a method for transferring process steps in a quality tool according to the present invention; Figure 2 This is a data transmission diagram illustrating a method for transferring process steps in a quality tool according to the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Specific Implementation Example 1:

[0018] like Figures 1 to 2 As shown, a method for transferring process steps in a quality tool, the method includes: Step S100: Acquire bill of materials data, production parameters, and process flow diagrams, integrate them to form multimodal data, and generate an adaptive semantic association model; the semantic association model stores static relationships and achieves adaptive adjustment through real-time synchronization of production parameters. When temperature parameter fluctuations exceed the threshold, the association rules are automatically updated to highlight the functions of potentially heat-sensitive steps, ensuring the applicability of the model in complex production environments, reducing data silos, ensuring the connection in data transmission, and improving the accuracy of subsequent risk analysis; Step S101: Parse the bill of materials data from the product data management system and product lifecycle management system to extract part numbers, material properties and hierarchical relationships; collect real-time production parameters from production line sensors, including temperature, pressure and speed, to generate a time series dataset; and extract process descriptions, step numbers and relationships from the process images using optical character recognition technology to generate structured image data. In this embodiment, when extracting part numbers, the bill of materials table is scanned and parsed line by line to identify unique identifiers and associate them with material properties. The generation of time series datasets involves a sampling frequency of once per second, while the data is filtered for noise. Image extraction uses an edge detection algorithm to identify the boundaries of the step flowchart, parses the text content, and establishes relationships. The time series dataset is usually expressed as a sequence of timestamp and value pairs, such as CSV format: [timestamp, parameter value], for example, "2025-08-15 10:00:00, 150℃", which is used for trend analysis and supports chart visualization.

[0019] A Product Data Management (PDM) system primarily includes product data storage, version control, collaboration tools, and document management. The PDM system covers CAD files, design drawings, technical specifications, change order tracking, and product data sharing functions, ensuring centralized management and revision tracking of design data. It is suitable for the early stages of product development, helping teams collaborate and maintain data consistency.

[0020] Product Lifecycle Management (PLM) encompasses the entire product lifecycle from concept to obsolescence, covering conceptual design, Product Data Management (PDM), change management, process workflows, collaboration tools, lifecycle analysis, and supply chain integration. PLM is more comprehensive than PDM, integrating the entire process from design to maintenance and supporting global team collaboration and data traceability.

[0021] Bill of Materials (BOM) data includes categories such as raw materials, components, sub-components, quantities, supplier information, costs, and hierarchical structure. Parsing BOM data typically involves using structured extraction tools, such as OCR scanning of PDF or Excel files, importing into a PDM / PLM system via API, or using custom scripts to parse the hierarchical structure. Parsing involves sequentially reading the file format, identifying the hierarchy (parent-child relationship), extracting field values, and verifying completeness. Assuming the BOM represents automotive engine components, we extract part numbers such as "ENG-001" (engine housing) and "ENG-002" (piston); material properties such as "aluminum alloy" (hardness > 50 HRC) and "steel" (strength > 500 MPa); and hierarchical relationships such as "ENG-001" as the parent and "ENG-002" as the child (piston embedded in the housing). During parsing, a script is used to traverse the Excel rows, recognizing indentation to indicate the hierarchy.

[0022] Production line sensors include categories such as temperature sensors, pressure sensors, vibration sensors, vision sensors, and flow sensors, with temperature ranges from -50℃ to +500℃, pressure ranges from 0 to 1000 bar, and vibration frequencies from 0 to 10 kHz. The process flow diagrams are exported from design software such as Visio or CAD, scanned paper drawings, or photographs taken at the production site. After extraction, they are represented as process descriptions such as "Assemble parts A to B", step numbers such as "Step 1", and relationships such as "Input: Part A; Output: Components AB".

[0023] Structured image data is used to extract text and generate shapes, including nodes, edges, and attributes, through OCR. The topology is then generated by image preprocessing, OCR text recognition, and shape detection algorithms.

[0024] Step S102: Map image data, bill of materials data, and production parameters to a unified semantic space. Use vector embedding to convert text descriptions into 512-dimensional vectors. Calculate the cosine similarity between parts and parameters. A threshold > 0.8 is considered a strong association, thus forming a model. Match the relationship between steps and parts / parameters using semantic association rules to form an adaptive semantic association model. Store the semantic association model in a distributed knowledge base and set a dynamic update mechanism. The dynamic update mechanism listens to the sensor API, pulls new data every minute, recalculates the association strength, and synchronizes changes in production parameters every minute to maintain the model's real-time performance.

[0025] In this embodiment, a unified semantic space is implemented through vector embedding or ontology mapping, converting different data sources such as text and images into a shared representation space for easy integration. Mapping is then completed sequentially through standardized data formats, calculation of cosine similarity, and construction of an association graph.

[0026] Semantic association rules refer to rules based on ontology or knowledge graphs, including attribute matching rules, causal rules, and hierarchical rules. Matching relationships are established through similarity calculations or rule engines, such as matching the step "welding" with the part "metal" (attribute: melting point) and the parameter "temperature" (rule: temperature > melting point).

[0027] Adaptive semantic association models are formed through generative models, such as Transformer variants, and by integrating real-time data to adjust the associations. The resulting model exists as a knowledge graph or vector database. For example, the initial model associates "temperature" with "welding steps" with a weight of 0.8; after adaptation, if the temperature fluctuates, the weight is adjusted to 0.9.

[0028] The dynamic update mechanism includes event triggers, API listening, and batch recalculation. By synchronizing changes in production parameters, listening to sensor data, pulling updates every minute, and recalculating correlation strength, such as triggering rule rematching based on temperature changes.

[0029] Step S200: Parse the semantic association model, dynamically adjust the network topology weights based on equipment status, output fluctuations, and quality feedback, and generate an adaptive dynamic topology network and data packets to be transmitted; Step S201: Extract the logical relationships of process steps from the semantic association model and generate an initial topology network, where nodes represent steps and edges represent dependencies between steps; In this embodiment, when extracting process steps and logical relationships, dependencies are identified by traversing the semantic model vectors and calculating similarity. For example, a vector similarity > 0.7 is considered a dependency, and the logical relationship is expressed in a graph structure or matrix. For example, the dependency matrix [step A, step B, 1] indicates that A depends on B. For example, the logical relationship of the step "melting" is input "raw material" and output "molten liquid", which is expressed as "melting -> cooling" (dependency edge).

[0030] The topological network structure consists of nodes (steps) and edges (relationships), including weighted edges (priority) and attribute labels. Represented as a directed graph, node attributes include ID and description, while edge attributes include type (dependency / parallelism) and strength.

[0031] Step S202: Collect equipment operating status, output fluctuation data and quality feedback data in real time, calculate the dynamic weight of each step node, adjust the edge connection strength of the topology network according to the weight changes, and prioritize strengthening the transmission path of high-risk steps. The dynamic weight calculation uses a weighted average formula, with a weight of 0.4 for fusion equipment status, a weight of 0.3 for production fluctuations, and a weight of 0.3 for quality feedback. This ensures that the network automatically prioritizes bottleneck steps during peak production periods. For example, in aerospace parts manufacturing, it can respond to quality feedback in real time, preventing low-priority steps from blocking high-risk paths. Equipment operating status includes utilization rate, failure rate, standby time, and maintenance status; production fluctuation data includes production deviation rate and capacity utilization rate; quality feedback data includes defect rate and rework rate, collected every 5 seconds via sensor API; The dynamic weight calculation formula is W = 0.4 utilization rate + 0.3(1-volatility) + 0.3*(1-defect rate). The allocation is based on a threshold. For example, if W>0.7, higher weights are given priority. The weight changes with real-time data. For example, if the defect rate increases by 10%, the weight decreases by 0.2. The edge strength is adjusted by updating the matrix to strengthen high-weight edges.

[0032] High-risk steps are determined based on RPN > threshold, such as >100, and priority is given to strengthening the path by increasing edge weights or short path routing.

[0033] Step S203: Mark high-risk steps as priority transmission objects, generate a data packet to be transmitted containing step priority and data content, and store the adjusted topology network as an adaptive dynamic topology network; the step priority is set based on the weight threshold, such as W>0.8 high priority, or user-defined rules, the content of the data packet to be transmitted includes step ID, priority field, data value and check code, and the data packet to be transmitted is transmitted through a priority queue after being encapsulated in JSON and encrypted.

[0034] Step S300: Based on the adaptive dynamic topology network, construct functional topology and failure topology, calculate failure risk priority by combining real-time production data, generate adaptive risk control strategy, and transmit high-risk step data to CP module through the adaptive dynamic topology network to generate PFMEA report; Step S301: Extract the functional attributes of the steps from the adaptive dynamic topology network, generate a functional topology that maps the association between the steps and the production target, analyze the potential failure modes of the steps, generate a cause-effect graph by enumerating the historical database, analyze the potential failure modes, and generate a failure topology that records the causal relationship between the failure modes and the steps; combine real-time production data to calculate the failure risk priority, and combine the severity, probability of occurrence and detection difficulty through a weighted formula. In this embodiment, when extracting functional attributes, network node attributes are queried and mapped to targets. This is done sequentially by traversing nodes and extracting attributes, such as output rate and associated targets (e.g., >95%), to obtain functional attributes. The functional topology is represented as a graph, with nodes having functional attributes and edges connecting them. For example, the node "welding" has the attribute "strength," and the edge connects to "melting." Network node attributes are queried and mapped to production targets, such as a step output rate target >95%. Failure mode analysis involves enumerating a historical failure database and generating a causal relationship graph, such as failure A causing step B to be interrupted. When combining data, batch information is retrieved every 10 seconds, and formula parameters are updated, such as the probability of occurrence increasing from 5 to 7. When generating strategies, scenarios are simulated for adjustment, such as increasing detection points to reduce detection difficulty to 3. The data packet is encapsulated, including RPN values ​​and strategy JSON, and transmitted using a network path algorithm to select the shortest, highest-weighted path. This ensures that the generated report includes a tabular list of RPNs and strategy recommendations, improving report readability.

[0035] Step S302: Based on the failure topology and failure risk priority, generate an adaptive risk control strategy. The adaptive risk control strategy includes adjusting process parameters, adding detection points, and optimizing the step sequence, such as optimizing process temperature, adding sensors, and optimizing processing sequence. The adaptive risk control strategy and corresponding data are encapsulated into a data packet. The encapsulated data packet includes the RPN value, strategy JSON, and data. It is transmitted to the CP module through the adaptive dynamic topology network. The CP module decapsulates the data, integrates the failure topology, generates a table report, verifies consistency, and processes and generates a PFMEA report.

[0036] The Risk Priority for Failure (RPN) is calculated as: RPN = Severity × Probability of Occurrence × Detection Difficulty (each score ranging from 1 to 10). It is dynamically recalculated based on real-time data, such as changes in material batches leading to an increased probability of occurrence. For example, in automotive manufacturing, data from high-RPN steps, such as welding failures, can be prioritized to ensure timely response from the production process control (CP) module, thereby improving overall production safety and efficiency.

[0037] Step S400: Based on the failure topology and adaptive risk control strategy, automatically generate a preliminary control plan, detect and repair inconsistencies with PFMEA module data in real time, and generate a complete CP report; Step S401: Extract key failure modes and control measures from the failure topology, traverse the failure topology nodes, select items with RPN>80 to generate templates, and generate a preliminary control plan containing control points, detection methods and responsibility allocation; Step S402: Compare the step numbers, failure modes, and control measures of the preliminary control plan with the data in the PFMEA module. Use string matching to check the consistency of the numbers and calculate the semantic similarity. A threshold of <0.9 is considered inconsistent. Identify the inconsistent items and, based on the production parameters, query the historical template to insert missing points. Correction values ​​are automatically filled in for missing control points based on context rules and parameter values ​​are corrected. For example, if the process frequency is >10 times / hour, add additional detection. Inconsistency detection is achieved through an item-by-item matching algorithm, such as comparing the hash values ​​of step numbers, and prioritizing the use of historical successful control measures as completion templates during repair. For example, in electronic product assembly, it can automatically correct temperature control point deviations, ensuring that CP reports comply with IATF 16949 standards, thereby improving user engagement and report reliability. Step S403: The user adjusts and controls the preliminary control plan through interaction, and confirms the correction results. Finally, a complete CP report containing all control points and correction records is generated and stored in a distributed knowledge base. The user adjusts the plan by converting commands through the voice parsing API, such as "add detection point". The visual interface displays real-time topology changes. When generating the report, a correction log table is embedded to ensure that the storage supports version control and facilitates subsequent auditing. The complete CP report includes process description, control points, detection methods, response plan, responsible persons, records, and revision history.

[0038] Step S500: Based on the semantic association model, generate a product special characteristic list in the DFMEA module. The product characteristic list includes dimensions, materials, functions and tolerances, such as dimensional tolerance ±0.01mm and material strength >500MPa. Import the special characteristic data into the APQP module to form a dynamic special characteristic topology diagram. Transfer the special characteristic data to the MSA module and SPC module to generate measurement task and process capability analysis report. Step S501: Extract part attributes and design requirements from the semantic association model, parse the model vectors to identify design requirements, such as tolerance ±0.01mm, identify key product characteristics, analyze the relationship between product characteristics and design functions, and generate a product characteristic list; based on the design failure mode, screen characteristics that have a significant impact on product quality and safety and mark them as special characteristics; special characteristic screening is based on a threshold mechanism, such as marking a characteristic with an impact score >7 as special, and considering changes in production plans in the topology diagram adjustment, such as adding characteristic weights for new orders, associating the special characteristic list with production parameters, verifying its feasibility in actual production, and storing the verified product special characteristic list in a distributed knowledge base by simulating production parameter matching, such as strength and pressure compatibility, and setting access permissions for subsequent modules to call.

[0039] Step S502: Extract the product special characteristics list from the distributed knowledge base and import it into the APQP module. The APQP module constructs a topology relationship for the imported product special characteristics list, associates it with the process, and stores it. Based on the semantic association model, it generates the topology relationship between special characteristics, calculates similarity, and constructs nodes / edges. If the similarity is >0.7, an edge is added, and the edge weight is calculated using the similarity. The weight is dynamically adjusted using the formula W = 0.5 plan priority + 0.5 * quality requirements. Nodes represent special characteristics, and edges represent the dependencies between special characteristics. The priority weight of nodes in the topology graph is dynamically adjusted according to the production plan and quality requirements. The dynamic special characteristics topology graph is associated with the dynamic topology network of the process steps to generate a cross-module collaborative mapping, and the dynamic special characteristics topology graph is stored.

[0040] Step S503: Extract the special characteristics to be measured from the dynamic special characteristic topology graph, select nodes with weight > 0.6, and generate a measurement task list containing measurement parameters and frequencies; In the MSA module, a calibration plan for measurement equipment is set based on the measurement task list to verify the repeatability and reproducibility of the measurement system. Repeatability is checked weekly with a standard deviation of <0.05. In the SPC module, production data related to special characteristics are collected, the process capability index is calculated, CPK = min(USL-μ, μ-LSL) / (3σ), and statistical analysis charts are generated. By using hash comparison or field matching, the consistency between the measurement task list and the product special characteristics list is verified, aligning the analysis results with production goals, generating a report containing measurement results and process capability analysis, and storing it in a distributed knowledge base.

[0041] Step S600: Monitor data transmission between monitoring modules, use logs to record the transmission time and content of each packet, detect format errors or data omissions, automatically trigger an adaptive repair mechanism to complete or correct data, and provide transmission path optimization suggestions through adaptive topology network analysis. Monitoring is implemented through event triggers, checking integrity when transmitting each data packet, and prioritizing matching historical patterns during repair. For example, in a high-intensity production environment, it can detect format errors from CP to SPC and automatically correct them, ensuring path optimization to reduce latency and improve the continuity and reliability of the overall workflow. Step S601: Monitor the data transmission between modules in real time, check the data format, step number and content integrity; compare with the expected data in the semantic association model, identify format errors and data omissions, extract relevant patterns from historical data based on production data, query similar scenarios from the database, such as cases of past omission control measures, and generate remediation suggestions. Step S602: Automatically perform repair operations through the rule engine, such as completing templates, inserting missing items, completing missing steps, standardizing data formats, using shortest path algorithms, such as Dijkstra variants, and optimizing data transmission paths based on the weight analysis of the adaptive dynamic topology network. Optimization suggestions are provided, including reroute high-load paths to ensure latency <5 seconds, and improving system performance in multi-module interactions, such as APQP to MSA.

[0042] Step S700: Record each step of data transmission on the distributed trust ledger and generate a time-series encrypted signature and a multi-dimensional verification code.

[0043] Step S701: Generate a record for each data transmission step, containing a timestamp, module identifier, and data content, in the form of {timestamp: "2025-08-15 10:00", module: "APQP to PFMEA", content: "step data"}. Capture transmission metadata (such as source module APQP, target PFMEA), generate a time-series encrypted signature using an encryption algorithm, calculate the hash chain of the encrypted signature (current hash = SHA-256(previous hash + timestamp + content)), and generate a multi-dimensional checksum. The multi-dimensional checksum includes content CRC, path checksum, and consistency XOR, covering data content, transmission path, and consistency between modules, for multi-party collaborative verification. Multi-party verification refers to consensus among module nodes. Content checksum covers data hash and verifies consistency between modules. Step S702: Store the records in a distributed trust ledger. The distributed trust ledger includes transaction records, timestamps, and hash chains. It uses a multi-node consensus mechanism to prevent tampering through majority voting, thus providing security protection. For each record, the multi-node consensus requires more than 50% of the nodes to agree. Set up a dynamic traceability query interface. The query interface supports SQL-like filtering (e.g., SELECT WHERE risk > high), allowing users to query transmission records by time range, module type, and risk level, and generate a visual traceability path diagram. The path diagram is rendered using GraphViz, and the node colors indicate the risk level, ensuring that problems can be quickly located during audits. For example, the node color indicates the risk, and the transmission time is displayed. Specific Implementation Example 2:

[0044] like Figures 1 to 2 As shown, based on the content of the above specific embodiments, the following content is further disclosed: According to the method for transferring process steps in a quality tool as described in the above specific embodiment, the corresponding module includes the following: The delivery methods include Advanced Product Quality Planning (APQP), Process Failure Mode and Effects Analysis (PFMEA), Control Plan (CP), Design Failure Mode and Effects Analysis (DFMEA), Measurement System Analysis (MSA), and Statistical Process Control (SPC). These modules are derived from the core toolset of the AIAG (Automotive Industry Action Group) standard and are used to ensure product quality consistency and risk control from design to production.

[0045] APQP (Advanced Product Quality Planning) is a structured approach for planning and developing products to meet customer requirements. It comprises five phases: planning and defining procedures, product design and development, process design and development, product and process validation, production start-up, and feedback evaluation. Specifically, it includes process flowcharts, special characteristic identification, risk assessment, and resource allocation. APQP emphasizes cross-functional team collaboration to ensure quality planning from concept to production. It is applicable to industries such as automotive and aerospace, and its goal is to prevent problems rather than detect them. APQP primarily corresponds to step S200, which involves parsing the semantic model to construct an adaptive dynamic topology network, and step S500, which involves importing special characteristic data to form a dynamic special characteristic topology graph. In S200, the APQP module receives the adaptive semantic association model from S100, extracts the logical relationships between process steps, generates an initial topology network, and dynamically adjusts the weights to construct the adaptive dynamic topology network. This module acts as an entry point, passing data packets to downstream modules, such as PFMEA. In S500, APQP receives the special characteristic list generated by DFMEA, imports it, and forms a dynamic special characteristic topology graph, which is then associated with the process step network to achieve cross-module collaboration. APQP ensures the initial structuring of process steps and special characteristics, improves overall quality planning efficiency, reduces later risks, and adapts to production fluctuations (such as changes in equipment status) through dynamic adjustments, ultimately reducing product development cycles and costs, and improving customer satisfaction.

[0046] PFMEA (Process Failure Mode and Effects Analysis) is a systematic approach used to identify and assess potential failure modes, causes, and effects in manufacturing processes. Prioritization is based on the Risk Priority Number (RPN). It includes constructing functional and failure networks, calculating severity, probability of occurrence, and detection difficulty, and proposing optimization measures. Key components include a failure mode list, root cause analysis, current controls, and improvement actions. PFMEA assumes correct design and focuses on process risk, and is widely used to prevent production defects. PFMEA primarily corresponds to the functional and failure topologies of S300, calculating the RPN and generating risk control strategies. In S300, the PFMEA module receives the adaptive dynamic topology network from APQP, extracts functional attributes to generate a functional topology, analyzes potential failures to generate a failure topology, and calculates the RPN based on real-time production data, generating adaptive risk control strategies such as adjusting process parameters. Data packets are transmitted to the CP module via the topology network. PFMEA identifies process failures early in the solution, reducing the risk of production interruptions. Through RPN calculation and strategy generation, it improves process reliability, reduces scrap rates, and supports the continuity of the overall quality toolchain.

[0047] A Control Plan (CP) is a document that outlines process monitoring methods, measurement systems, and response plans to control critical characteristics. It includes process steps, control points, detection methods, responsibility assignments, and emergency responses. CPs are divided into prototype, pre-production, and production phases to ensure process stability, with a focus on maintaining and improving the process. CP primarily corresponds to the S400's function of generating preliminary control plans based on failure topologies, detecting and correcting inconsistencies, and generating complete reports. In the S400, the CP module receives the failure topology and risk strategies from the PFMEA (Problem-Based Management Analysis), extracts key patterns to generate preliminary plans, compares PFMEA data to identify inconsistencies, automatically completes / corrects the data, and supports user-interactive adjustments. CP ensures that PFMEA risks are translated into actual control, maintains process stability, reduces variability, improves product consistency, and supports continuous improvement.

[0048] DFMEA (Design Failure Mode and Effects Analysis) is used to identify potential failure modes, causes, and effects in product design, prioritizing them based on Restricted Participant Networks (RPNs). It includes functional analysis, failure network construction, and design optimization, encompassing design characteristics, failure effects, and improvement actions. It assumes correct manufacturing and focuses on design risks. DFMEA primarily corresponds to the generation of a product-specific characteristic list in S500. In S500, the DFMEA module receives the semantic model, extracts part attributes to generate a characteristic list, filters specific characteristics, and verifies their feasibility. DFMEA identifies key characteristics in the solution, ensures design robustness, reduces downstream process risks, and supports product lifecycle quality.

[0049] MSA (Measurement System Analysis) assesses the accuracy, repeatability, and reproducibility of a measurement system, including GR&R (Gage Repeatability and Reproducibility) studies. Core components include variation source analysis, equipment calibration, and acceptance criteria (GR&R < 10%). MSA corresponds to the measurement task-based verification system of S500. In S500, MSA receives measurement tasks with special characteristics, sets up calibration plans, and verifies repeatability and reproducibility. MSA ensures the reliability of measurement data, reduces erroneous decisions, supports SPC accuracy, and improves the overall quality data credibility.

[0050] SPC (Statistical Process Control) uses statistical methods to monitor process variation, including control charts, process capability indices (CPK), and trend analysis. It encompasses data acquisition, chart generation, and anomaly detection to ensure process stability. SPC corresponds to the S500 in calculating CPK and generating analysis reports. In the S500, SPC receives production data, calculates CPK, generates charts, and verifies consistency. SPC monitors process stability in the solution, detects deviations early, optimizes resource allocation, supports preventive maintenance, and improves production efficiency.

[0051] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising a reference structure" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0052] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for transferring process steps in a quality tool, characterized in that, The transmission method includes: Acquire bill of materials data, production parameters, and process flow diagrams, fuse them to form multimodal data, and generate an adaptive semantic association model; The semantic association model is parsed, and the network topology weights are dynamically adjusted based on equipment status, output fluctuations, and quality feedback to generate an adaptive dynamic topology network and data packets to be transmitted. Based on the adaptive dynamic topology network, functional topology and failure topology are constructed. Failure risk priority is calculated by combining real-time production data, and an adaptive risk control strategy is generated. High-risk step data is transmitted to the CP module through the adaptive dynamic topology network to generate a PFMEA report. Based on the failure topology and adaptive risk control strategy, an initial control plan is automatically generated, inconsistencies with PFMEA module data are detected and repaired in real time, and a complete CP report is generated. Based on the semantic association model, a list of product special characteristics is generated in the DFMEA module, the special characteristic data is imported into the APQP module to form a dynamic special characteristic topology diagram, and the special characteristic data is transferred to the MSA module and SPC module to generate measurement task and process capability analysis report. The system monitors data transmission between modules, detects format errors or data omissions, automatically triggers an adaptive repair mechanism to complete or correct data, and provides suggestions for optimizing transmission paths through adaptive topology network analysis. Each step of data transmission is recorded on a distributed trust ledger, generating time-series encrypted signatures and multi-dimensional verification codes.

2. The method for transferring process steps in a quality tool according to claim 1, characterized in that, The process of acquiring bill of materials data, production parameters, and process flow diagrams, fusing them to form multimodal data, and generating an adaptive semantic association model includes: Extract process descriptions, step numbers, and relationships from the process flow diagram to generate structured image data; Parse bill of materials data from product data management system and product lifecycle management system to extract part numbers, material attributes and hierarchical relationships; Real-time production parameters, including temperature, pressure, and speed, are collected from sensors on the production line to generate time-series datasets; Image data, bill of materials data, and production parameters are mapped to a unified semantic space. Semantic association rules are used to match the relationships between steps, parts, and parameters to form an adaptive semantic association model. The semantic association model is stored in a distributed knowledge base, and a dynamic update mechanism is set up to synchronize the changes in production parameters every minute to maintain the real-time performance of the model.

3. The method for transferring process steps in a quality tool according to claim 1, characterized in that, The process of parsing the semantic association model, dynamically adjusting network topology weights based on equipment status, output fluctuations, and quality feedback, and generating an adaptive dynamic topology network and data packets to be transmitted includes: The logical relationships between process steps are extracted from the semantic association model to generate an initial topology network, where nodes represent steps and edges represent dependencies between steps. Real-time acquisition of equipment operating status, output fluctuation data, and quality feedback data; calculation of dynamic weights for each step node; adjustment of edge connection strength of the topology network based on weight changes; and prioritization of strengthening the transmission paths of high-risk steps. High-risk steps are marked as priority delivery objects, generating data packets to be delivered that contain step priorities and data content, and the adjusted topology network is stored as an adaptive dynamic topology network.

4. The method for transferring process steps in a quality tool according to claim 1, characterized in that, Based on the adaptive dynamic topology network, a functional topology and a failure topology are constructed. Failure risk priorities are calculated using real-time production data, and an adaptive risk control strategy is generated, including: The functional attributes of the steps are extracted from the adaptive dynamic topology network to generate a functional topology that maps the association between the steps and the production target. The potential failure modes of the steps are analyzed to generate a failure topology that records the causal relationship between the failure modes and the steps. By combining real-time production data, the priority of failure risks is calculated, and the severity, probability of occurrence, and detection difficulty are comprehensively considered through a weighted formula. Based on the failure topology and failure risk priority, an adaptive risk control strategy is generated. The adaptive risk control strategy includes adjusting process parameters, adding detection points, and optimizing the step sequence. The adaptive risk control strategy and the corresponding data are encapsulated into a data packet and transmitted to the CP module through an adaptive dynamic topology network.

5. The method for transferring process steps in a quality tool according to claim 1, characterized in that, Based on the failure topology and adaptive risk control strategy, a preliminary control plan is automatically generated, inconsistencies with PFMEA module data are detected and corrected in real time, and a complete CP report is generated, including: Key failure modes and control measures are extracted from the failure topology to generate a preliminary control plan that includes control points, detection methods, and responsibility allocation. The preliminary control plan is compared with the step numbers, failure modes and control measures of the PFMEA module data to identify inconsistencies. Based on the production parameters, missing control points are automatically filled in and parameter values ​​are corrected. Users interact with the system to adjust and control the initial control plan, confirm the correction results, and finally generate a complete CP report containing all control points and correction records, which is stored in a distributed knowledge base.

6. The method for transferring process steps in a quality tool according to claim 1, characterized in that, The generation of a product special characteristics list in the DFMEA module based on the semantic association model includes: Extract the part attributes and design requirements of the bill of materials from the semantic association model, identify key product characteristics, analyze the relationship between product characteristics and design functions, and generate a product characteristic list. Based on the design failure modes, characteristics that have a significant impact on product quality and safety are screened and marked as special characteristics; The list of special characteristics is associated with production parameters to verify its feasibility in actual production. The verified list of product special characteristics is then stored in a distributed knowledge base, and access permissions are set for subsequent modules to call.

7. The method for transferring process steps in a quality tool according to claim 6, characterized in that, The process of importing special characteristic data into the APQP module to form a dynamic special characteristic topology graph includes: Extract the product's special characteristics list from the distributed knowledge base and import it into the APQP module; Based on the semantic association model, a topological relationship between special characteristics is generated, where nodes represent special characteristics and edges represent dependencies between special characteristics. The priority weights of nodes in the topological graph are dynamically adjusted according to the production plan and quality requirements. The dynamic special characteristic topology graph is associated with the dynamic topology network of process steps to generate a cross-module collaborative mapping, and the dynamic special characteristic topology graph is stored.

8. The method for transferring process steps in a quality tool according to claim 7, characterized in that, The process of transferring special characteristic data to the MSA and SPC modules to generate measurement task and process capability analysis reports includes: Extract the special characteristics that need to be measured from the dynamic special characteristic topology graph, and generate a measurement task list containing measurement parameters and frequencies; In the MSA module, a calibration plan for the measurement equipment is set based on the measurement task list to verify the repeatability and reproducibility of the measurement system; In the SPC module, production data related to special characteristics are collected, process capability index is calculated, and statistical analysis charts are generated. Verify the consistency between the measurement task list and the product special characteristics list, align the analysis results with production goals, generate a report containing measurement results and process capability analysis, and store it in a distributed knowledge base.

9. The method for transferring process steps in a quality tool according to claim 1, characterized in that, The monitoring modules detect format errors or data omissions during data transmission, automatically triggering an adaptive repair mechanism to complete or correct the data. Adaptive topology network analysis provides suggestions for optimizing the transmission path, including: Real-time monitoring of data transfer between modules, checking data format, step numbering, and content integrity; By comparing the expected data in the semantic association model, format errors and data omissions are identified, and relevant patterns are extracted from historical data based on production data to generate repair suggestions. It automatically performs repair operations, completing missing steps and standardizing data formats. Based on the weight analysis of the adaptive dynamic topology network, the data transmission path is optimized, and optimization suggestions are given.

10. A method for transferring process steps in a quality tool according to claim 1, characterized in that, The process of recording each step of data transmission on a distributed trust ledger and generating time-series encrypted signatures and multi-dimensional verification codes includes: For each step of data transmission, a record containing a timestamp, module identifier, and data content is generated. A time-series encrypted signature is generated using an encryption algorithm, and a multi-dimensional verification code is generated, covering data content, transmission path, and consistency between modules, for multi-party collaborative verification. Records are stored in a distributed trust ledger, and security is protected through a multi-node consensus mechanism. A dynamic traceability query interface is set up, allowing users to query transmission records by time range, module type, and risk level, and generate a visual traceability path diagram.

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