A numerical control boring and milling machine human risk identification control method, system, device and medium

Abstract

This invention discloses a method, system, equipment, and medium for identifying and controlling human-caused risks in CNC boring and milling machines. The method first acquires process data from the machine tool's manufacturing and operation, constructs a risk structure decomposition framework, and generates a structured list of human-caused risks by calculating the quantified coupling degree. Next, it acquires Pythagorean fuzzy set evaluation data for the risk list and uses the WASPAS method for data fusion and calculation, outputting risk ranking and extracting key human-caused risks. Then, it constructs a causal logic tree based on FTA to extract the root cause element set, applies DEMATEL to quantify the mutual influence relationships between elements, and calculates centrality and causality. Based on centrality and causality, it determines control priorities and outputs corresponding CNC boring and milling machine control strategy instructions. This invention achieves a fully automated closed loop from comprehensive quantitative identification and precise tracing of human-caused risks to automatic issuance of hardware / software control strategies by the computer, improving the proactive defense capabilities and quality reliability of complex electromechanical systems.

Description

Technical Field

[0001] This invention relates to the field of risk control and data processing technology for complex electromechanical systems, and in particular to a method, system, equipment and medium for identifying and controlling human-caused risks in CNC boring and milling machines. Background Technology

[0002] Human-caused risk control is crucial for ensuring the reliability and stability of complex electromechanical systems (such as CNC machine tools and automated production lines). However, these systems are complex and highly coupled, with numerous human-caused risk factors affecting their reliability and unclear interactions. Existing methods for controlling human-caused risks largely rely on expert experience or single statistical analyses, making it difficult to systematically and quantitatively pinpoint the core factors from a vast array of human-caused risk factors.

[0003] Currently, there are some data-driven methods for controlling the root causes of human-related risks, but they often have the following shortcomings: 1. Existing methods lack a systematic approach in the risk identification phase, often relying on experience-based listings, which makes it difficult to ensure comprehensive coverage of human-caused risks throughout the entire lifecycle and all elements of a complex system, potentially leading to omissions in the extraction of human-caused risks.

[0004] 2. In the process of human factor risk assessment, it is difficult to effectively handle the inherent ambiguity, hesitation and extreme uncertainty in expert judgment, and there is a lack of decision-making mechanism that can stably aggregate multiple fuzzy evaluations and achieve scientific ranking.

[0005] 3. Existing methods stop at identifying key human factors risks and lack a systematic conversion mechanism from risk prioritization to control measure formulation, making it difficult to effectively translate the analysis results into concrete actions to prevent quality incidents. Summary of the Invention

[0006] In view of the shortcomings of the prior art, the technical problem to be solved by the present invention is: how to provide a method, system, equipment and medium for identifying and controlling human factors risks in CNC boring and milling machines, so as to realize the systematic identification of all elements of human factors risks in complex electromechanical systems, the accurate quantitative integration of evaluation data, and the automatic output of targeted hardware or software control strategies based on the root cause tracing results.

[0007] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: A method for identifying and controlling human-caused risks in CNC boring and milling machines, executed by computer equipment, includes the following steps: S1: Obtain process data of CNC boring and milling machine manufacturing and operation process, decompose the process data into basic work unit set based on the Work Structure Decomposition (WBS) method; construct risk structure decomposition (RBS) framework based on skill-rule-knowledge (SRK) cognitive model; calculate the coupling degree data between the basic work unit set and each risk factor in the RBS framework, and generate a structured human factor risk list based on the coupling degree data. S2: Obtain Pythagorean fuzzy set evaluation data for the human factors risk list, calculate the comprehensive weight of experts; fuse the evaluation data using the Pythagorean fuzzy Hamacher operator to construct a comprehensive evaluation matrix; calculate the comprehensive evaluation matrix based on the weighted aggregation and product evaluation method WASPAS, output the risk ranking result containing the WASPAS comprehensive utility value, and extract the key human factors risks. S3: Construct the causal logic tree of the key human factors risk based on the fault tree analysis (FTA) method, and analyze and extract the set of root cause elements that lead to the key human factors risk. S4: Obtain the direct impact evaluation data for the root element set, apply the DEMATEL decision laboratory analysis method to quantify the mutual influence relationship between each root element, and calculate the influence degree, affected degree, centrality and causal degree of each root element. S5: Determine the control priority classification of each root element based on the centrality and causality, and output the control strategy instructions for the CNC boring and milling machine according to the control priority classification. The control strategy instructions include equipment sensor layout parameter update instructions or digital equipment management system reconfiguration instructions.

[0008] Furthermore, the process of generating a structured human-cause risk list in step S1 includes: S11. The quality control work of CNC boring and milling machines is broken down into the key component assembly stage and the whole machine debugging stage, and further refined into multiple basic work units. S12. Based on the SRK cognitive model, personnel cognitive risks are divided into skill-level risks, rule-level risks, and knowledge-level risks, generating a standardized human factors risk classification framework. S13. Quantitatively couple and map the basic working unit with the human factors risk classification framework, filter out risk items with coupling scores higher than a preset threshold, and generate the human factors risk list.

[0009] Furthermore, the process of calculating the comprehensive weights of experts and constructing the comprehensive evaluation matrix in step S2 includes: S21. Obtain the Pythagorean fuzzy number for each expert on multiple evaluation indicators for each risk factor in the human risk list, wherein the Pythagorean fuzzy number includes membership degree and non-membership degree. S22. Calculate the correlation between expert evaluation opinions using the cosine correlation formula, and obtain objective weights based on the correlation. S23. Linearly weight the objective weights and the preset subjective weights to obtain the expert's comprehensive weights; S24. Using the comprehensive weights, the Pythagorean fuzzy numbers of all experts are aggregated using the Pythagorean fuzzy Hamach weighted average operator to generate the comprehensive evaluation matrix.

[0010] Furthermore, the calculation of the comprehensive evaluation matrix based on the weighted aggregate sum-product evaluation method (WASPAS) includes: S25. The comprehensive evaluation matrix is ​​normalized. S26. Calculate the VAM utility value and VPM utility value for each individual due to risk using the weighted additive model and the weighted multiplicative model, respectively. S27. Based on preset linear aggregation parameters, combine the VAM utility value and the VPM utility value to calculate and obtain the WASPAS comprehensive utility value for each individual cause risk.

[0011] Further, step S4, which calculates the influence, affectedness, centrality, and causation of each root element, includes: S41. Construct a direct influence matrix among the root cause elements based on the direct influence evaluation data. S42. The direct influence matrix is ​​normalized to obtain the comprehensive influence matrix. The elements of the comprehensive influence matrix represent the sum of all direct and indirect influences between the root elements. S43. Calculate the row sum of the comprehensive influence matrix as the influence degree, and calculate the column sum of the comprehensive influence matrix as the degree of influence. S44. Add the influence degree to the influenced degree to obtain the centrality degree, and subtract the influence degree from the influenced degree to obtain the causation degree.

[0012] Further, in step S5, determining the control priority classification of each root cause element based on the centrality and causality includes: S51. Construct a causal quadrant diagram with the centrality as the horizontal axis and the causality as the vertical axis, and map the root element to the causal quadrant diagram. S52. The root element located in the first quadrant is classified as the first priority, serving as the core driving element, and a device sensor layout parameter update instruction containing supplementary sensor node information is generated accordingly. S53. The root element located in the second quadrant is classified as the second priority, as a sensitive element, and a digital equipment management system reconstruction instruction containing process optimization algorithm call instructions or fault case knowledge base entry instructions is generated in a targeted manner.

[0013] A key human factor risk identification and risk root cause control system for CNC boring and milling machines includes: The risk list generation module is used to acquire process data of CNC boring and milling machines, build an RBS framework based on WBS and SRK, and generate a structured human factors risk list based on the coupling degree data of basic work unit set and risk factors. The key risk extraction module is used to acquire Pythagorean fuzzy set evaluation data, calculate expert comprehensive weights and construct a comprehensive evaluation matrix, and use the WASPAS method to calculate the comprehensive utility value and extract key human factors risks. The causal tracing module is used to construct a causal logic tree based on the FTA method and extract the root source element set. The DEMATEL method is used to calculate the influence, affectedness, centrality and causality of each root source element. The control strategy output module is used to determine the control priority based on the centrality and causality, and output a control strategy that includes device sensor layout parameter update instructions or digital device management system reconfiguration instructions.

[0014] A computer device includes a memory and a processor, the memory storing a computer program, the processor executing the program to perform the steps of the method as described in any of the preceding claims.

[0015] A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the method as described in any of the preceding claims.

[0016] In summary, the present invention has the following beneficial effects: 1. Systematic identification and modeling: By systematically coupling WBS with SRK cognitive model, human factor risk data is extracted in a structured manner from massive and chaotic manufacturing processes, ensuring the comprehensiveness and accuracy of risk identification throughout the entire life cycle of complex electromechanical systems from the data source.

[0017] 2. Evaluation of the scientific nature of data processing: The WASPAS data fusion method based on Pythagorean fuzzy sets and Hamacher operators is adopted, which effectively overcomes the problem of the traditional method's weak ability to handle extreme fuzzy evaluations and constructs a scientific and robust decision-making calculation process.

[0018] 3. Closed-loop implementation of risk tracing and control: The innovative introduction of the FTA-DEMATEL hybrid computing model quantifies the causal relationships of each root cause element based on the extraction of key human-cause risks. Qualitative risk tracing is directly transformed into "equipment sensor layout update instructions" and "control system reconfiguration instructions" that can be issued by the computer system. This achieves a fully automated and digital closed loop from "system identification → scientific integration → cause calculation → control issuance", which greatly improves the proactive defense capability and quality reliability of electromechanical systems. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the main mechanical structure of a machining center in this embodiment.

[0020] Figure 2 This is a breakdown diagram of the quality control process for the CNC boring and milling machine in this embodiment.

[0021] Figure 3 This is a schematic diagram illustrating the risk decomposition of quality control for CNC boring and milling machines in this embodiment.

[0022] Figure 4 This is a schematic diagram of the fault tree for tracing the key human factors risks in this embodiment.

[0023] Figure 5 This is a scatter plot of centrality-causality in this embodiment. Detailed Implementation

[0024] The present invention will be further described in detail below with reference to the embodiments.

[0025] In the actual production and overall debugging of a certain model of high-end CNC boring and milling machine, there has long been a technical challenge of complex human-caused risks and an over-reliance on personal experience and post-event investigation for control. For example, when debugging the precision of machined curved surfaces, the lack of a structured traceability calculation method makes it impossible to pinpoint the core risk, resulting in limited effectiveness of conventional control measures. To address this, this embodiment proposes a method for identifying and controlling key human-caused risks in CNC boring and milling machines, aiming to systematically address the above challenges through a fully automated quantitative model and digital command output.

[0026] like Figure 1 For the main mechanical structure of a certain machining center, in this embodiment, a human factors risk list is first constructed based on the WBS-SRKRBS model, which includes the following steps: constructing the Work Breakdown Structure (WBS) of a CNC boring and milling machine, constructing the Risk Breakdown Structure (RBS) based on the SRK model, WBS-SRKRBS coupling mapping and risk source identification, and forming the final human factors risk list.

[0027] Constructing the Working Structure Breakdown (WBS) of CNC boring and milling machine: Based on the manufacturing process and quality control requirements of CNC machine tools, the quality control work of CNC boring and milling machine is broken down into two core stages: (1) assembly stage of key components (W1) and (2) debugging stage of the whole machine (W2).

[0028] (1) Assembly stage of key components (W1): A certain vertical machining center is mainly assembled from components such as bed, slide rail, worktable, electrical control cabinet, spindle box, column and motor box. Its overall internal structure is as follows: Figure 1 As shown. Assembly is a crucial step in machine tool manufacturing, and the final accuracy and performance of the machine tool largely depend on the quality of assembly. To systematically manage and optimize this complex process, the overall assembly process can be decomposed into multiple logically clear work stages. This decomposition method naturally maps to corresponding Work Breakdown Structure (WBS) units, laying the foundation for subsequent schedule, cost, and quality control. Specifically: ① Basic large component placement and fixing (W) 11 ): Fixing of large structural components such as bed and column.

[0029] ② Assembly and adjustment of core moving parts (W) 12 ): Installation and adjustment of precision motion units such as spindles, lead screws, and guide rails.

[0030] ③ Functional accessory integration and debugging (W) 13 ): Installation and functional debugging of automation accessories such as tool magazines and rotary tables.

[0031] ④ CNC system installation and parameter setting (W 14 ): CNC system and driver hardware installation and basic parameter settings.

[0032] Based on this physical location, WBS is further applied to decompose the quality control of the machine tool debugging stage.

[0033] (2) Overall Machine Debugging Stage (W2): After the components are assembled, the overall machine debugging stage begins. This stage mainly involves CNC connection, parameter setting, and functional testing to ensure the coordinated operation of all systems. The focus is on testing and calibrating various precision aspects of the machine tool (such as positioning accuracy and repeatability), and verifying machining performance through trial cutting. This stage is the final inspection stage to ensure the final quality and performance of the machine tool meet the standards. The specific breakdown is as follows: ① Geometric and positioning accuracy inspection (W) 21 ): Using equipment such as laser interferometers to detect static and motion accuracy.

[0034] ② Cutting performance test (W) 22 ): The dynamic rigidity, vibration and surface quality of the machined material are tested through actual test cuts.

[0035] ③ Numerical control function and multi-axis linkage test (W) 23 ): Verify the accuracy and smoothness of all CNC functions and multi-axis linkage.

[0036] ④ Reliability testing (W) 24 ): Conduct temperature rise, vibration and continuous operation tests to evaluate long-term stability.

[0037] The specific structure of the quality control work breakdown for CNC boring and milling machines is as follows: Figure 2 As shown.

[0038] Constructing a Human Factor Risk Structure Decomposition (RBS) Based on the SRK Model: To systematically identify human cognitive and behavioral risks (R) in the quality control of CNC boring and milling machines, this invention constructs a risk structure decomposition based on the SRK cognitive behavior model as its core framework. Human cognitive and behavioral risks are divided into three different directions from three perspectives: skill-level risk (R1), rule-level risk (R2), and knowledge-level risk (R3). Further subdivisions are made in each direction, providing a standardized classification basis for accurately identifying human factor risk factors.

[0039] ①Skill-level risk (R1): Insufficient operational proficiency (R 11 Repetitive motion inaccuracy (R) 12 ) ② Rule-level risk (R2): Errors in rule understanding and application (R 21 ), Inspection standard execution deviation (R) 22 ) ③ Knowledge-level risk (R3): Insufficient ability to diagnose complex faults (R 31 ), Process system optimization decision-making errors (R) 32 ) The specific risk classification levels for quality control risk decomposition of CNC boring and milling machines are as follows: Figure 3 As shown.

[0040] WBS-SRKRBS Coupling Mapping and Risk Source Identification: Based on the work structure decomposition and risk structure decomposition of CNC boring and milling machines, eight processes and six types of risks are coupled and paired to form the WBS-SRKRBS matrix of the CNC boring and milling machine. The coupling strength is quantified using a 0-5 score system, where: 0 points indicate no obvious correlation; 1-3 points indicate indirect or moderate coupling; and 4-5 points indicate strong coupling, meaning that the risk is very likely to cause quality deviations in this process. The specific results are shown in Table 1.

[0041] Table 1. WBS-SRKRBS Coupling Mapping for CNC Boring and Milling Machines Based on the table above, by obtaining the risk events and adverse consequences of the coupling factors with a score of 5 during the production of CNC boring and milling machines, this risk factor is named, and a new list of human-caused risks is generated. The specific analysis process is shown in Table 2.

[0042] Table 2 Human Factors Risk List Forming the final list of human factors risks: Through the above systematic coupling analysis, we selected and refined the six most representative human factors risks, and constructed a structured human factors risk indicator system, as shown in Table 3.

[0043] Table 3 Human Factors Risk Based on SRK Model The process involves expert evaluation and weight determination, including expert language evaluation and expert weight calculation.

[0044] Expert Language Evaluation: Four experts (E1, E2, E3, E4) were invited to evaluate the above six risks on two evaluation indicators: J1 (frequency of occurrence) and J2 (severity of impact). The experts used the Pythagorean fuzzy terminology set shown in Table 4 for qualitative evaluation.

[0045] Table 4. Pythagorean Fuzzy Language Terminology Set The language evaluation matrix of expert E1 and the corresponding Pythagorean fuzzy number (PFN) are shown in Table 5, and the PFN matrices of the other three experts are given in Table 6.

[0046] Table 5. PFN matrix of expert E1 Table 6 Expert PFN Matrix Expert weight calculation: Based on the expert evaluation matrix in Table 5, the expert weights are determined using a comprehensive subjective-objective method based on correlation. Taking the evaluation of element HF1 on index J1 as an example, the relevant steps are as follows: (1) Calculation of the correlation matrix: using the cosine correlation formula Calculate the correlation between experts, where and Expert K represents the element HF i In indicator J j Membership and non-membership of the evaluation value; and Representative expert l on element HF i In indicator J jThe membership degree and non-membership degree of the evaluation value.

[0047] The correlation matrix of element HF1 on index J1 is calculated as follows: (2) Calculate the average correlation of each expert. Initial weights of experts The relevant formulas are as follows: The results are as follows: (3) Calculate the overall weight Set the subjective expert weight as Using the formula (in The expert's overall weight can be calculated: The process of prioritizing key human risk factors includes the following steps: obtaining a comprehensive assessment matrix and prioritizing the risk using the Weighted Aggregated Sum Product Assessment (WASPAS) method.

[0048] Obtaining the comprehensive evaluation matrix: The initial matrix proposed by the experts is synthesized using the Hamacher weighted operator. The relevant formula is as follows: in and Expert K represents the element HF i The membership and non-membership of the evaluation value on index j The parameters represent the overall weight of expert k. The comprehensive evaluation matrix can be obtained as shown in Table 7.

[0049] Table 7 Comprehensive Evaluation Matrix The weighted average sum-of-products assessment (WASPAS) method was used for ranking: Based on the data in Table 7, the proposed human-caused risks were ranked using the WASPAS method to select the most critical aspects of quality control for CNC boring and milling machines, providing a basis for subsequent control. The specific steps are as follows.

[0050] 1) Calculate the weighted addition (VAM) score, weighted multiplication (VPM) score, and WASPAS composite score using the formula. The formula for calculating the relevant score is as follows: in It is element HF i The comprehensive evaluation value of indicator j, r j The weights of evaluation metrics J1 (frequency of occurrence) and J2 (severity of impact) A value of 0.5 is typically used to indicate that the two parts have balanced weights.

[0051] Based on the comprehensive evaluation matrix in Table 7, the scores can be calculated using the formula, as shown in Table 8.

[0052] Table 8. Scores related to the WASPAS method (2) Based on the WASPAS comprehensive score, the ranking of human factors risk can be obtained, as shown in Table 9.

[0053] Table 9 Ranking of Key Risks According to the ranking in Table 9, the result of the risk importance sorted from largest to smallest is HF6>HF5>HF3>HF2>HF1>HF4. This indicates that the three human factors risks of "multi-axis linkage accuracy compensation and optimization decision-making ability; accuracy of boring chatter / cutting noise diagnosis; and completeness of maintenance procedures for key components of spindle / lead screw" are relatively important. This conclusion will be used as the key human factors risk input for the next step of analysis.

[0054] This study conducts source tracing and control of key human factors risks based on a hybrid FTA-DEMATEL model, using these risks as control examples for subsequent analysis. The following only presents the root cause element analysis and control process for the key human factors risks: "Multi-axis linkage accuracy compensation and optimization decision-making capability (HF6), boring chatter / cutting noise diagnosis accuracy (HF5), and completeness of maintenance procedures for key spindle / leadscrew components (HF3)". This includes Fault Tree Analysis (FTA) and Decision-making Trial and Evaluation Laboratory (DEMATEL) analysis of the key human factors risks.

[0055] Fault Tree Analysis (FTA) of Critical Human Factor Risks: Based on the fault tree procedure, the critical human factor risk attribution event is first identified as the top event U, and a fault tree diagram is drawn as follows: Figure 4The fault tree is shown below. The event descriptions for each stage of the fault tree are shown in Table 10.

[0056] Table 10 Events at each stage of the fault tree Based on the basic events in the decision tree above, we can derive the set of root causes leading to key human factor risks: Dematel Analysis (DEMATL): This involves performing a Dematel analysis on the root cause element set. A direct impact matrix is ​​constructed using expert evaluations, and the centrality and causality of each root cause element are calculated to identify the key root cause elements.

[0057] (1) Expert evaluation and construction of direct influence matrix Experts were asked to evaluate the direct impact of 18 factors pairwise (0-4 points; 0: no direct impact, 1: weak impact, 2: moderate impact, 3: strong impact, 4: decisive impact). The results were aggregated to obtain the direct impact matrix Z of 18 factors, as shown in Table 11. Table 11 Direct Influence Matrix (2) Calculate the comprehensive influence matrix T and related parameters Using formula The comprehensive influence matrix is ​​calculated, where I is the identity matrix of the same order as Z. The results are given in Table 12.

[0058] Table 12 Comprehensive Impact Matrix (3) Analyze the comprehensive influence matrix According to the DEMATEL model analysis, the impact (D i =The sum of the m-th row of the matrix reflects the degree of influence of a certain element on other elements; the degree of influence (R) j =The sum of the nth column of the matrix reflects the degree to which an element is influenced by other elements. Centrality and causality are important indicators for defining whether an element is a result element or a cause element, and their relevant calculation formulas are as follows: Based on the above formulas, all parameters of the DEMATEL model can be calculated and listed in Table 13.

[0059] Table 13 DEMATEL Related Parameters Based on the above calculation results, the following can be plotted: Figure 5The centrality-causality scatter plot shown can be used for the final source analysis of the DEMATEL model to propose corresponding control measures.

[0060] (4) Develop an integrated control strategy As shown in the centrality-causality scatter plot, the elements in the first quadrant (D1, G1, I1) have both high influence and high causality, and belong to the core driving root elements that cause human-caused risks. They play a decisive role in the reliability level of the entire CNC system, so these elements are listed as the first priority control objects. The elements in the second quadrant (A1, A2, C2, E1) have the characteristics of low influence and high causality, and belong to the sensitive elements that cause human-caused risks. They are easily affected by the core root elements, and thus indirectly affect the overall reliability of the CNC system. Therefore, they are listed as the second priority control objects.

[0061] Based on the above priority classification and combined with reliability knowledge, we can construct a specific layered risk control logic: For the core driving root elements of the first priority, we focus on root causes such as data source, management source, and lack of supervision, and implement precise prevention and control from the starting point of risk generation; for the sensitive elements of the second priority, we build a dynamic response mechanism for risk transmission around derivative issues such as data quality, knowledge accumulation, decision-making tools, and system integration.

[0062] First, from the perspective of risk causes, the core driving elements correspond to three fundamental issues: ① Data source issues: These issues stem from layout defects and can directly lead to distortion or loss of status monitoring data, which is the starting point for subsequent risks.

[0063] ②Management issues at the source: These issues stem from design oversights, which can lead to a lack of scientific basis for critical activities such as equipment maintenance, increasing the probability of human error.

[0064] ③ Lack of supervision: This type of problem stems from insufficient constraints, which can lead to the failure to identify and intervene in risks and hidden dangers in a timely manner, causing small deviations to gradually evolve into systemic failures.

[0065] Based on this, sensitive elements correspond to four types of problems: ① Data quality issues: The transmission effect of core root elements may lead to shortcomings such as insufficient algorithm support.

[0066] ② Knowledge accumulation problem: The imperfect process data verification and management mechanism will result in the inability to collect experience and fault information into reusable knowledge assets.

[0067] ③ Decision-making tool issues: Insufficient ability to integrate multi-source information will result in a lack of comprehensive and accurate data support for management and control decisions.

[0068] ④ System integration issues: The lack of supporting systems such as case libraries will prevent the rapid reuse of risk response experience, thus weakening the system's resilience.

[0069] This forms a layered and progressive risk control system that covers the root causes, providing systematic support for the reliability of CNC systems. The current status and information of relevant factors related to the quality control strategy based on the DEMATEL model are given in Table 14.

[0070] Table 14 Quality Control Strategies Based on Table 14, it can be concluded that this method can systematically identify the root causes of critical human-related risks in CNC boring and milling machines, classify them according to their nature and impact, and then automatically generate hierarchical and implementable hardware control and software reconfiguration instructions—implementing "equipment sensor layout parameter update instructions" for data source-level problems; and implementing "digital equipment management system reconfiguration instructions" for supporting and derivative problems to call optimization algorithms or execute knowledge base entries. Thus, through automated control at the computer's underlying level, a digital closed-loop risk control system covering data flow, logical algorithms, and system interfaces is constructed.

[0071] In summary, this invention constructs a list of key human factor risks using a WBS-SRKRBS coupled framework and extracts these risks using a Pythagorean fuzzy WASPAS ranking method. Furthermore, it innovatively introduces an FTA-DEMATEL hybrid model for in-depth root cause analysis of these risks, accurately identifying the core driving factors that cause them. Based on this, a hierarchical control strategy is formulated, ultimately forming a complete decision-making loop for key human factor risks: "systematic identification - quantitative ranking - root cause analysis - precise control." Therefore, this method significantly improves the systematicness, predictability, and accuracy of quality risk management in complex electromechanical systems, providing a reliable theoretical and practical tool for implementing proactive preventative quality control.

[0072] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for identifying and controlling human-caused risks in CNC boring and milling machines, characterized in that, Performed by a computer device, including the following steps: S1. Obtain process data of CNC boring and milling machine manufacturing and operation process, decompose the process data into basic work unit set based on the Work Structure Decomposition (WBS) method; construct risk structure decomposition (RBS) framework based on skill-rule-knowledge (SRK) cognitive model; calculate the coupling degree data between the basic work unit set and each risk factor in the RBS framework, and generate a structured human factor risk list based on the coupling degree data. S2. Obtain Pythagorean fuzzy set evaluation data for the human factors risk list, calculate the comprehensive weight of experts; fuse the evaluation data using the Pythagorean fuzzy Hamacher operator to construct a comprehensive evaluation matrix; calculate the comprehensive evaluation matrix based on the weighted aggregation and product evaluation method WASPAS, output the risk ranking result containing the WASPAS comprehensive utility value, and extract the key human factors risks. S3. Construct the causal logic tree of the key human factor risk based on the fault tree analysis method (FTA), and analyze and extract the root cause element set that leads to the key human factor risk. S4. Obtain the direct impact evaluation data for the root element set, apply the DEMATEL decision laboratory analysis method to quantify the mutual influence relationship between each root element, and calculate the influence degree, affected degree, centrality and causal degree of each root element. S5. Determine the control priority classification of each root element based on the centrality and causality, and output the corresponding CNC boring and milling machine control strategy instructions according to the control priority classification.

2. The human factor risk identification and control method for CNC boring and milling machines as described in claim 1, characterized in that, The process of generating a structured list of human-caused risks in step S1 includes: S11. The quality control work of CNC boring and milling machines is broken down into the key component assembly stage and the whole machine debugging stage, and further refined into multiple basic work units. S12. Based on the SRK cognitive model, personnel cognitive risks are divided into skill-level risks, rule-level risks, and knowledge-level risks, generating a standardized human factors risk classification framework. S13. Quantitatively couple and map the basic working unit with the human factors risk classification framework, filter out risk items with coupling scores higher than a preset threshold, and generate the human factors risk list.

3. The human factor risk identification and control method for CNC boring and milling machines as described in claim 1, characterized in that, The process of calculating the comprehensive weights of experts and constructing the comprehensive evaluation matrix in step S2 includes: S21. Obtain the Pythagorean fuzzy number for each expert on multiple evaluation indicators for each risk factor in the human risk list, wherein the Pythagorean fuzzy number includes membership degree and non-membership degree. S22. Calculate the correlation between expert evaluation opinions using the cosine correlation formula, and obtain objective weights based on the correlation. S23. Linearly weight the objective weights and the preset subjective weights to obtain the expert's comprehensive weights; S24. Using the comprehensive weights, the Pythagorean fuzzy numbers of all experts are aggregated using the Pythagorean fuzzy Hamach weighted average operator to generate the comprehensive evaluation matrix.

4. The human factor risk identification and control method for CNC boring and milling machines as described in claim 1, characterized in that, The calculation of the comprehensive evaluation matrix based on the weighted aggregation sum-product evaluation method (WASPAS) includes: S25. The comprehensive evaluation matrix is ​​normalized. S26. Calculate the VAM utility value and VPM utility value for each individual due to risk using the weighted additive model and the weighted multiplicative model, respectively. S27. Based on preset linear aggregation parameters, combine the VAM utility value and the VPM utility value to calculate and obtain the WASPAS comprehensive utility value for each individual cause risk.

5. The human factor risk identification and control method for CNC boring and milling machines as described in claim 1, characterized in that, Step S4 involves calculating the influence, affectedness, centrality, and causation of each root element, including: S41. Construct a direct influence matrix among the root cause elements based on the direct influence evaluation data. S42. The direct influence matrix is ​​normalized to obtain the comprehensive influence matrix. The elements of the comprehensive influence matrix represent the sum of all direct and indirect influences between the root elements. S43. Calculate the row sum of the comprehensive influence matrix as the influence degree, and calculate the column sum of the comprehensive influence matrix as the degree of influence. S44. Add the influence degree to the influenced degree to obtain the centrality degree, and subtract the influence degree from the influenced degree to obtain the causation degree.

6. The human factor risk identification and control method for CNC boring and milling machines as described in claim 1, characterized in that, Step S5, which determines the control priority classification of each root cause element based on the centrality and causality, includes: S51. Construct a causal quadrant diagram with the centrality as the horizontal axis and the causality as the vertical axis, and map the root element to the causal quadrant diagram. S52. The root element located in the first quadrant is classified as the first priority, serving as the core driving element, and a device sensor layout parameter update instruction containing supplementary sensor node information is generated accordingly. S53. The root element located in the second quadrant is classified as the second priority, as a sensitive element, and a digital equipment management system reconstruction instruction containing process optimization algorithm call instructions or fault case knowledge base entry instructions is generated in a targeted manner.

7. A human factor risk identification and control system for CNC boring and milling machines, characterized in that, include: The risk list generation module is used to acquire process data of CNC boring and milling machines, build an RBS framework based on WBS and SRK, and generate a structured human factors risk list based on the coupling degree data of basic work unit set and risk factors. The key risk extraction module is used to acquire Pythagorean fuzzy set evaluation data, calculate expert comprehensive weights and construct a comprehensive evaluation matrix, and use the WASPAS method to calculate the comprehensive utility value and extract key human factors risks. The causal tracing module is used to construct a causal logic tree based on the FTA method and extract the root source element set. The DEMATEL method is used to calculate the influence, affectedness, centrality and causality of each root source element. The control strategy output module is used to determine the control priority based on the centrality and causality, and output the corresponding control strategy instructions.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the program, it implements the steps of the human factor risk identification and control method for CNC boring and milling machines as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the human factor risk identification and control method for CNC boring and milling machines as described in any one of claims 1 to 6.

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