Multi-process collaborative digital power distribution cabinet quality inspection management method and system

Abstract

The invention discloses a multi-process collaborative digital power distribution cabinet quality inspection management method and system, and aims to solve the problems of low-efficiency quality inspection, poor process collaboration, lagged discovery of quality problems and the like in the traditional power distribution cabinet production process. Through real-time data acquisition, historical data is utilized to analyze and predict potential quality problems, early warning is carried out in advance, task priorities of subsequent processes are dynamically adjusted, and intelligent quality control in the production process is realized. And all quality inspection data are recorded and stored through a digital platform, so that the traceability and transparency of the data are ensured. And through systematic data analysis and quality feedback, the production process is continuously optimized, and the efficiency and accuracy of quality management are greatly improved. According to the invention, the production process of the power distribution cabinet is optimized, the rework cost is reduced, the market competitiveness and customer satisfaction of enterprises are enhanced, and the intelligent development of the power distribution cabinet manufacturing industry is promoted.

Description

Technical Field

[0001] The present invention belongs to the technical field of quality inspection management of distribution cabinets, and specifically relates to a digital quality inspection management method and system for collaborative multi-process distribution cabinets. Background Art

[0002] In the traditional production process of distribution cabinets, quality control usually relies on manual inspection and manual recording, which results in low inspection efficiency, easy data errors, lack of real-time nature and consistency. The dispersion of quality inspection standards and methods may lead to inconsistent quality inspection standards for different processes, thus affecting the quality of the final product. The quality data and progress management among multiple processes are often isolated. The quality results of the previous process may not be transmitted to the subsequent process in a timely manner, resulting in the lagged discovery of quality problems and affecting the subsequent production links. Poor collaboration among processes, task scheduling and priority adjustment usually rely on manual intervention, and flexible response and dynamic optimization cannot be achieved. There may be potential quality problems (such as equipment failures, process deviations, etc.) in the production process, and these problems are often not discovered until subsequent links, resulting in production delays, rework and other problems. The lack of an intelligent early warning mechanism makes it impossible to analyze potential defects based on real-time data, thus hindering the ability to discover and correct quality problems in advance. In traditional quality inspection management, data recording is often done manually, which is easy to omit and lacks a clear traceability path. Once a quality problem occurs, it may be time-consuming and laborious to trace back to the specific cause and responsible person. Summary of the Invention

[0003] A digital quality inspection management method for collaborative multi-process distribution cabinets includes the following steps; S1. Quality inspection planning and task assignment: According to the design and production process of the distribution cabinet, the production of the distribution cabinet is disassembled into key processes: sheet metal processing, component installation, wiring assembly, insulation testing, and function testing. Analyze the quality inspection standards for each process, including the quality requirements, detection methods, and standard values for each process, and determine the data types to be collected for each process: dimensional accuracy, torque value, insulation resistance, and energization parameters. According to the work flow of the production line, reasonably allocate quality inspection tasks to different process links, including various inspection points before, during, and after production. The task assignment is carried out through a digital platform to ensure collaborative operation of each process; S2. Data collection: The quality inspector manually records various data (such as dimensions, appearance inspection results, performance test data, etc.) according to the inspection requirements of each process and inputs them into the digital system. For the process of numerical control machine tool processing, the machine automatically records the processing data (such as dimensions, processing accuracy, processing time, etc.) and directly imports the data into the quality inspection management platform through the system interface, reducing the workload of manual input; S3. Multi-process information sharing: Establish a platform for information sharing among processes. Operators of each process can view the quality data and inspection results of the previous process in real time, reducing information lag or errors. Dynamically adjust the task priorities of subsequent processes according to the quality inspection results of the previous process. When an unqualified result is detected in a certain process, the system automatically generates a prompt message to remind the personnel of subsequent processes to conduct re-inspection or adjustment operations to ensure that unqualified products do not flow into the next process; S4. Real-time quality inspection and analysis: Operators of each process conduct manual inspections according to the specified quality standards and enter the results into the system in a timely manner. Conduct real-time analysis based on the recorded data, and combine historical big data analysis to predict potential defects and discover potential quality problems, including the stability of electrical connections and insulation performance, and integrate to obtain the formula , where is the quality control index, measuring the comprehensive level of electrical connection stability and insulation performance, R c is the contact resistance, R ins is the insulation resistance, T is the terminal torque. The stability of electrical connections depends on the tightening torque of the terminals, is the intercept term, , are the regression coefficients, indicating the influence of each independent variable on the dependent variable, is the error term, and reverse data flow is carried out. When the quality problem of a certain process is discovered through the early warning system, trace back to the previous two processes and evaluate the implementation of their quality control. When the insulation resistance value is lower than the standard, by analyzing the torque value of the terminal and the contact resistance parameters, judge whether there are quality defects in the previous process (such as component installation or circuit assembly), and reverse to this process for early warning processing. When a certain process has 3 consecutive out-of-tolerance situations, the system automatically triggers an early warning and notifies the responsible person. For example, when the size data exceeds the tolerance range or abnormalities occur during the processing, the system will automatically prompt; S5. Quality inspection result recording and traceability: All quality inspection data and results are recorded and stored in the digital system in real time to ensure that the production process data of each distribution cabinet can be traced. For unqualified distribution cabinets, the system will generate a special unqualified report, record the specific defects and treatment measures, and guide them to the rework or scrapping process to ensure quality control; S6. Quality feedback and continuous improvement: Through the summary and analysis of quality inspection data, identify common quality problems in the production process, propose process improvement measures, and optimize the production process. Operators can timely feedback the problems and suggestions encountered during the quality inspection process, and the system will conduct continuous process and quality improvement according to the feedback; S7. Intelligent Quality Report Generation: The system automatically generates a detailed quality inspection report based on the data input for each process. The report includes the inspection data, pass / fail status, defect description, and handling measures for each link. All quality reports are electronically archived for easy query and management, ensuring data security and traceability.

[0004] Furthermore, a digital power distribution cabinet quality inspection management method for multi-process collaboration In step S3, the task priority of the subsequent process is dynamically adjusted according to the quality inspection results of the previous process. The specific steps are as follows; S31. Real-time Data Collection and Quality Feedback: After the quality inspection of each process is completed, the operator enters the inspection results (such as dimensions, appearance, performance tests, etc.) into the system. The system automatically judges whether the quality inspection results meet the standards. The quality inspection data is uploaded in real time through the digital platform and synchronized to the scheduling systems of other relevant processes; S32. Priority Adjustment Rules: Set rules in the system to define the impact of different types of quality inspection results on the priority of subsequent processes. Qualified Items: When the quality inspection result of the previous process is qualified, the subsequent process continues to proceed according to the original plan; Unqualified Items: When the previous process is unqualified, the tasks of the subsequent process need to be adjusted, and may be given priority to resume work or re-inspect; Minor Deviations: When there are minor deviations in the previous process, the start of the subsequent process will be postponed until the adjustment is completed; Use a rule engine to analyze the quality data of the previous process, identify which quality problems affect the progress and quality of the subsequent process, and adjust the task priority based on this; S33. Automatic Priority Adjustment: According to the quality inspection results of the previous process, the system automatically adjusts the task priority of the subsequent process. When a process is unqualified, the task will be marked as high priority to ensure re-inspection and rework, and avoid affecting the overall production progress; S34. Automatic Reminder: When the quality inspection of the previous process is unqualified or the deviation is too large, an automatic reminder notice will be sent to the operator of the subsequent process, informing them that they need to adjust the production process; S35. Feedback Mechanism and Adjustment Confirmation: After the system adjusts the task priority, the quality inspector and production operator must confirm that all adjustment measures have been implemented before the subsequent operation, including rework, correction, or additional inspection work. Before the subsequent process starts, the operator needs to confirm whether all improvement and adjustment measures have been implemented and provide necessary quality feedback; S36. Execution and progress tracking: According to the adjusted priorities, the corrective tasks are preferentially executed and the progress is monitored. After the corrective tasks are completed, the system will automatically return to the regular production task arrangement and track the execution progress of each task in real time to ensure that the subsequent processes after the task adjustment can be timely followed up and avoid any lag in the processes from affecting the overall production cycle.

[0005] Furthermore, a digital power distribution cabinet quality inspection management method for multi-process collaboration In step S4, real-time analysis is performed based on the recorded data, and combined with historical big data analysis to predict potential defects, discover potential quality problems, and perform early warning processing. The specific steps are as follows; S41. Data collection and preprocessing: Quality-related data is collected in real time from multiple sources such as production lines, quality inspection links, and equipment monitoring systems. The data includes dimensions, torque values, resistance values, power-on parameters, appearance inspection results, and function test results. The collected raw data is cleaned to remove outliers, missing values, or noise, and the data with different dimensions is standardized and normalized. Quality prediction features are extracted from the raw data, for example, key influencing factors are obtained through historical data analysis; S42. Real-time prediction: Big data analysis is performed using historical data and integrated to obtain the formula , where is the quality control index, which measures the comprehensive level of electrical connection stability and insulation performance, R c is the contact resistance, R ins is the insulation resistance, T is the terminal torque. The stability of the electrical connection depends on the tightening torque of the terminal, is the intercept term, , are the regression coefficients, indicating the influence of each independent variable on the dependent variable, is the error term, and the potential laws of quality problems are learned according to the model, and the parameters are adjusted to obtain the best prediction effect. During the production process, the quality data collected in real time is input into the formula for real-time prediction, and the potential quality problems of each process or product are predicted according to the input data, and it is predicted whether there are defects in a certain part of a certain power distribution cabinet; S43. Real-time early warning and dynamic response: According to historical data and business requirements, set thresholds for the prediction results of the model; when the defect prediction probability exceeds a certain value (such as 90%), an early warning is triggered. When a serious defect is predicted, rework and production suspension inspections are preferentially scheduled. When the probability or severity of predicting potential defects exceeds the set threshold, the system automatically triggers an early warning and notifies relevant personnel; S44. Feedback mechanism and model adjustment: After the warning is triggered, the quality inspector will conduct manual verification to confirm whether there are actual defects. If actual defects exist, they will be processed according to the repair or rework process. If no defects are found, the information will be fed back to the model to help it make more accurate predictions in the future; S45. Quality improvement and optimization: Through continuous data analysis, automatically identify the processes and production links prone to quality problems, and provide data support for process improvement. Through the quality reports and analysis results generated by the system, production managers can identify the bottlenecks and optimization spaces in the production process, and further improve the production process and flow; S46. Data recording and traceability: Record the situation of each warning trigger, including the type of warning, predicted defects, and handling measures. These data provide a complete record for later quality management and problem tracing. After a problem is discovered, quickly query relevant production records, quality inspection data, and equipment status to help managers analyze the root cause of the failure.

[0006] Furthermore, a multi-process collaborative digital power distribution cabinet quality inspection management system, where the multi-process collaborative digital power distribution cabinet quality inspection management system is used to implement any multi-process collaborative digital power distribution cabinet quality inspection management method; the multi-process collaborative digital power distribution cabinet quality inspection management system includes: a quality inspection planning and task allocation module, a data collection and recording module, a multi-process information sharing and collaboration module, a quality inspection and analysis module, a warning and fault handling module, and a quality inspection result recording and traceability module; Among them, the quality inspection planning and task allocation module: According to the design and production process of the power distribution cabinet, disassemble the production process into multiple processes, determine the quality inspection standards, detection methods, standard values, and the types of data to be collected for each process, and perform intelligent scheduling based on the production plan, production line load, and historical data to optimize resource utilization and task allocation; The data collection and recording module: Manually and automatically collect data, and the types of data collected include dimensional accuracy, torque value, insulation resistance, and power-on parameters. The data is directly imported into the quality inspection management platform through the system interface; The multi-process information sharing and collaboration module: A real-time information sharing platform between processes to ensure that operators of each process can view the quality data and inspection results of the previous process in real time, and dynamically adjust the task priorities of subsequent processes according to the quality inspection results of the previous process; The quality inspection and analysis module: Analyze the data of each process in real time, and discover potential quality problems in the production process according to historical data and models, and perform warning processing; Early warning and fault handling module: When potential defects are shown by real-time data and prediction results, the system automatically triggers an early warning to alert relevant personnel, identifies potential problems through big data analysis and makes predictions, and takes preventive measures in advance to prevent production delays or quality losses; Quality inspection result recording and traceability module: All quality inspection data, test results and handling measures are recorded in real time and stored in a digital system to ensure data security and traceability. For unqualified distribution cabinets, the system will generate a special non-conformance report and guide it to the rework or scrapping process.

[0007] Advantages of the present invention: Operators of each process can view the quality data and inspection results of the previous process in real time, ensuring that information flow is not lagged, and reducing production errors caused by information lag or misunderstanding. Based on the quality results of the previous process, the system can automatically adjust the task priorities of subsequent processes, reducing the transmission and accumulation of quality problems, and ensuring that problems are discovered and solved as early as possible. Based on the analysis of historical data by a machine learning model, the system can predict potential quality problems and issue early warnings in advance, thus avoiding the spread of quality problems and reducing rework and repair costs. Through real-time early warnings, production managers can take timely measures to avoid production stagnation and delays caused by equipment failures or process problems. The production process and quality inspection data of each distribution cabinet can be traced, and any defect can be quickly located to the responsible person and specific link, improving the transparency of quality control. Description of the Drawings

[0008] Figure 1 It is a flowchart of a digital quality inspection management method for multi-process collaborative distribution cabinets; Detailed Embodiments

[0009] A digital quality inspection management method for multi-process collaborative distribution cabinets, the process is as Figure 1 shown, including the following steps; S1. Quality inspection planning and task allocation: According to the design and production process of the distribution cabinet, the production of the distribution cabinet is disassembled into key processes: sheet metal processing, component installation, wiring assembly, insulation testing, and function testing. Analyze the quality inspection standards of each process, including the quality requirements, inspection methods, and standard values of each process, and determine the types of data to be collected for each process: dimensional accuracy, torque value, insulation resistance, and energization parameters. According to the work process of the production line, reasonably allocate quality inspection tasks to different process links, including various inspection points before, during, and after production. The task allocation is carried out through a digital platform to ensure the collaborative operation of each process; S2. Data collection: Quality inspectors manually record various data (such as dimensions, appearance inspection results, performance test data, etc.) according to the inspection requirements of each process and input them into the digital system. In the process of CNC machine tool processing, the machine automatically records the processing data (such as dimensions, machining accuracy, processing time, etc.) and directly imports the data into the quality inspection management platform through the system interface, reducing the workload of manual input; S3. Multi-process information sharing: Establish a platform for information sharing between processes. Operators of each process can view the quality data and inspection results of the previous process in real time, reducing information lag or errors. Dynamically adjust the task priority of the subsequent process according to the quality inspection results of the previous process. When an unqualified result is detected in a certain process, the system automatically generates a prompt message to remind the personnel of the subsequent process to conduct a re-inspection or adjustment operation to ensure that unqualified products do not flow into the next process; S4. Real-time quality inspection and analysis: Operators of each process conduct manual inspections according to the specified quality standards and enter the results into the system in a timely manner. Conduct real-time analysis based on the recorded data, and combine historical big data analysis to predict potential defects and discover potential quality problems, including electrical connection stability and insulation performance, and integrate to obtain the formula , where is the quality control index, measuring the comprehensive level of electrical connection stability and insulation performance, R c is the contact resistance, R ins is the insulation resistance, T is the terminal torque. The stability of the electrical connection depends on the tightening torque of the terminal, is the intercept term, 、 are the regression coefficients, indicating the influence of each independent variable on the dependent variable, is the error term, and reverse data flow is carried out. When the quality problem of a certain process is discovered through the early warning system, trace back to the previous two processes to evaluate the implementation of their quality control. When the insulation resistance value is lower than the standard, by analyzing the torque value of the terminal and the contact resistance parameter, judge whether there are quality defects in the previous process (such as component installation or circuit assembly), and backtrack to this process for early warning processing. When a certain process has three consecutive out-of-tolerance situations, the system automatically triggers an early warning and notifies the responsible person. For example, when the dimension data exceeds the tolerance range or there are abnormalities during the processing, the system will automatically prompt; S5. Quality inspection result recording and traceability: All quality inspection data and results are recorded and stored in the digital system in real time to ensure that the production process data of each distribution cabinet can be traced. For unqualified distribution cabinets, the system will generate a special unqualified report, record the specific defects and treatment measures, and guide them to the rework or scrapping process to ensure quality control; S6. Quality feedback and continuous improvement: By summarizing and analyzing the quality inspection data, identify common quality problems in the production process, propose process improvement measures, optimize the production process. Operators can timely feedback the problems and suggestions encountered during the quality inspection process, and the system will carry out continuous process and quality improvement according to the feedback; S7. Intelligent quality report generation: The system automatically generates detailed quality inspection reports based on the data input in each process. The reports include the inspection data, qualification status, defect description and treatment measures of each link. All quality reports are electronically archived for easy query and management at any time, ensuring the security and traceability of the data.

[0010] Furthermore, a digital power distribution cabinet quality inspection management method for multi-process collaboration, In step S3, the task priorities of subsequent processes are dynamically adjusted according to the quality inspection results of the previous process. The specific steps are as follows; S31. Real-time data collection and quality feedback: After the quality inspection of each process is completed, the operator enters the inspection results (such as dimensions, appearance, performance tests, etc.) into the system. The system automatically judges whether the quality inspection results meet the standards. The quality inspection data is uploaded in real time through the digital platform and synchronized to the scheduling systems of other relevant processes; S32. Priority adjustment rules: Set rules in the system to define the impact of different types of quality inspection results on the priorities of subsequent processes, Qualified items: When the quality inspection result of the previous process is qualified, the subsequent process continues to proceed according to the original plan; Unqualified items: When the previous process is unqualified, the tasks of subsequent processes need to be adjusted, and may be given priority to resume work or reinspect; Minor deviations: When minor deviations occur in the previous process, the start of subsequent processes will be postponed until the adjustment is completed; Use a rule engine to analyze the quality data of the previous process, identify which quality problems affect the progress and quality of subsequent processes, and adjust the task priorities based on this; S33. Automatic priority adjustment: According to the quality inspection results of the previous process, the system automatically adjusts the task priorities of subsequent processes. When a process is unqualified, the task will be marked as high priority to ensure reinspection and rework to avoid affecting the overall production progress; S34. Automatic reminder: When the quality inspection of the previous process is unqualified or the deviation is too large, an automatic reminder notice will be sent to the operators of subsequent processes, informing them that they need to adjust the production process; S35. Feedback Mechanism and Adjustment Confirmation: After the system adjusts the task priorities, the quality inspectors and production operators must confirm that all adjustment measures have been implemented before subsequent operations, including rework, correction, or additional inspection work. Before the start of the subsequent process, the operator needs to confirm whether all improvement and adjustment measures have been implemented and provide necessary quality feedback; S36. Execution and Progress Tracking: According to the adjusted priorities, prioritize the execution of correction tasks and monitor the progress. After the correction tasks are completed, the system will automatically return to the regular production task arrangement and track the execution progress of each task in real time to ensure that the subsequent processes after the task adjustment can be promptly followed up and avoid any lag in the processes from affecting the overall production cycle.

[0011] Furthermore, a digital power distribution cabinet quality inspection management method for multi-process collaboration, In step S4, real-time analysis is performed based on the recorded data, and combined with historical big data analysis to predict potential defects, discover potential quality problems, and perform early warning processing. The specific steps are as follows; S41. Data Collection and Preprocessing: Real-time collect quality-related data from multiple sources such as the production line, quality inspection link, and equipment monitoring system. The data includes dimensions, torque values, resistance values, power-on parameters, appearance inspection results, and function test results. Clean the collected raw data to remove outliers, missing values, or noise, and standardize and normalize data with different dimensions. Extract quality prediction features from the raw data, such as obtaining key influencing factors through historical data analysis; S42. Real-time Prediction: Use historical data for big data analysis and integrate to obtain the formula , where is the quality control index, measuring the comprehensive level of electrical connection stability and insulation performance, R c is the contact resistance, R ins is the insulation resistance, T is the terminal torque. The stability of the electrical connection depends on the tightening torque of the terminal, is the intercept term, , are the regression coefficients, indicating the influence of each independent variable on the dependent variable, is the error term, and learn the potential laws of quality problems according to the model and adjust the parameters to obtain the best prediction effect. During the production process, input the real-time collected quality data into the formula for real-time prediction, predict the potential quality problems of each process or product according to the input data, and predict whether there are defects in a certain part of a certain power distribution cabinet; S43. Real - time Warning and Dynamic Response: Set thresholds for the prediction results of the model according to historical data and business requirements; when the defect prediction probability exceeds a certain value (such as 90%), trigger a warning. When a serious defect is predicted, prioritize scheduling rework and production suspension inspections. When the probability or severity of a potential defect prediction exceeds the set threshold, the system automatically triggers a warning and notifies relevant personnel; S44. Feedback Mechanism and Model Adjustment: After the warning is triggered, the quality inspector will conduct manual verification to confirm whether there are actual defects. If actual defects exist, handle them according to the repair or rework process. If no defects are found, feedback this information to the model to help it make more accurate predictions in the future; S45. Quality Improvement and Optimization: Through continuous data analysis, automatically identify processes and production links that are prone to quality problems, and provide data support for process improvement. Through the quality reports and analysis results generated by the system, production managers can identify bottlenecks and optimization spaces in the production process, and further improve production processes and procedures; S46. Data Recording and Traceability: Record the situation of each warning trigger, including the type of warning, predicted defects, and handling measures. These data provide a complete record for later quality management and problem traceability. After a problem is discovered, quickly query relevant production records, quality inspection data, and equipment status to help managers analyze the root cause of the failure.

[0012] Furthermore, a digital power distribution cabinet quality inspection management system for multi - process collaboration, the digital power distribution cabinet quality inspection management system for multi - process collaboration is used to implement any digital power distribution cabinet quality inspection management method for multi - process collaboration; the digital power distribution cabinet quality inspection management system for multi - process collaboration includes: a quality inspection planning and task assignment module, a data collection and recording module, a multi - process information sharing and collaboration module, a quality inspection and analysis module, a warning and fault handling module, and a quality inspection result recording and traceability module; Among them, the quality inspection planning and task assignment module: According to the design and production process of the power distribution cabinet, disassemble the production process into multiple processes, determine the quality inspection standards, detection methods, standard values, and the types of data to be collected for each process, and perform intelligent scheduling based on the production plan, production line load, and historical data to optimize resource utilization and task assignment; The data collection and recording module: Manually and automatically collect data. The types of data collected include dimensional accuracy, torque value, insulation resistance, and power - on parameters. The data is directly imported into the quality inspection management platform through the system interface; The multi - process information sharing and collaboration module: A real - time information sharing platform between processes to ensure that operators of each process can view the quality data and inspection results of the previous process in real - time, and dynamically adjust the task priority of the subsequent process according to the quality inspection results of the previous process; Quality inspection and analysis module: Analyze the data of each process in real time, discover potential quality problems in the production process according to historical data and models, and carry out early warning and processing; Early warning and fault handling module: When the real-time data and prediction results show potential defects, the system automatically triggers an early warning to remind relevant personnel, identify potential problems through big data analysis and make predictions, and take preventive measures in advance to prevent production delays or quality losses; Quality inspection result recording and traceability module: All quality inspection data, test results and handling measures are recorded in real time and stored in the digital system to ensure data security and traceability. For unqualified distribution cabinets, the system will generate a special non-conformance report and guide them to the rework or scrapping process.

Claims

1. A multi-process collaborative digital distribution cabinet quality inspection management method, characterized in that: The steps include: S1. Quality inspection planning and task allocation: According to the design and production process of the distribution cabinet, the production of the distribution cabinet is disassembled into key processes: sheet metal processing, component installation, line assembly, insulation test, and functional test. The quality inspection standards of each process are analyzed, including the quality requirements, detection methods, and standard values ​​of each process. The data types to be collected in each process are determined: dimensional accuracy, torque value, insulation resistance, and power-on parameters. According to the workflow of the production line, quality inspection tasks are assigned to different process links, including various detection points before, during, and after production. Task allocation is carried out through a digital platform; S2. Data collection: Quality inspectors manually record various data according to the inspection requirements of each process: dimensions, appearance inspection results, performance test data, and input them into the digital system. For CNC machine tool processing, the machine automatically records the processing data: dimensions, processing accuracy, processing time, and directly imports the data into the quality inspection management platform through the system interface; S3. Multi-process information sharing: Establish a platform for information sharing between processes, so that operators of each process can view the quality data and test results of the previous process in real time, reduce information lag and errors, and dynamically adjust the task priority of subsequent processes according to the quality inspection results of the previous process. When an unqualified result is detected in a process, the system automatically generates a prompt message to remind the operators of the subsequent processes to review and adjust the operation; S4. Real-time quality inspection and analysis: Operators of each process conduct manual inspection according to the specified quality standards and enter the results into the system in a timely manner. Real-time analysis is performed based on the recorded data, and potential defects are predicted in combination with historical big data analysis to discover potential quality problems, including electrical connection stability and insulation performance, and integrate them to obtain formulas ,in It is a quality control indicator that measures the comprehensive level of electrical connection stability and insulation performance. R c is the contact resistance, R ins is the insulation resistance, T The stability of the electrical connection depends on the tightening torque of the terminal. is the intercept term, , is the regression coefficient, which indicates the effect of each independent variable on the dependent variable. It is an error term, and the data flows in the reverse direction. When a quality problem in a process is discovered through the early warning system, it is traced back to the first two processes, and the execution of its quality control is evaluated, and early warning processing is carried out. When a process exceeds the tolerance for three consecutive times, the system automatically triggers an early warning and notifies the responsible person; S5. Recording and tracing of quality inspection results: All quality inspection data and results will be recorded and stored in the digital system in real time. The production process data of each distribution cabinet can be traced. For unqualified distribution cabinets, the system will generate a special unqualified report, record the specific defects and treatment measures, and guide them to the rework and scrapping process; S6. Quality feedback and continuous improvement: By summarizing and analyzing quality inspection data, we can identify quality problems in the production process, propose process improvement measures, and optimize the production process. Operators will promptly provide feedback on problems and suggestions encountered during the quality inspection process, and the system will make continuous process and quality improvements based on the feedback. S7. Intelligent quality report generation: The system automatically generates a detailed quality inspection report based on the data input for each process. The report includes the inspection data, qualification status, defect description and treatment measures for each link. The quality reports are archived electronically for easy query and management at any time to ensure the security and traceability of the data.

2. A multi-process collaborative digital distribution cabinet quality inspection management method as described in claim 1, characterized in that: In step S3, the priority of subsequent process tasks is dynamically adjusted according to the quality inspection results of the previous process. The specific steps are as follows; S31. Real-time data collection and quality feedback: After completing the quality inspection of each process, the operator enters the inspection results into the system, and the system automatically determines whether the quality inspection results meet the standards. The quality inspection data is uploaded in real time through the digital platform and synchronized to the scheduling system of other related processes; S32. Priority adjustment rules: Set rules in the system to define the impact of different types of quality inspection results on the priority of subsequent processes. Qualified items: The quality inspection result of the current process is qualified, and the subsequent processes continue to proceed as planned; Unqualified items: The current process is unqualified, and the subsequent process tasks need to be adjusted, and priority is given to resuming work and re-inspection; Minor deviation: If there is a slight deviation in the current process, the start of the subsequent process will be postponed until the adjustment is completed; Use the rule engine to analyze the quality data of the previous process, identify which quality issues affect the progress and quality of the subsequent processes, and adjust the task priority based on this; S33.Automatic priority adjustment: Based on the quality inspection results of the previous process, the system automatically adjusts the task priority of the subsequent process. When a process fails, the task will be marked as high priority to ensure re-inspection and rework to avoid affecting the overall production progress; S34. Automatic reminder: When the quality inspection of the current process fails or the deviation is too large, a reminder notification will be automatically sent to the operator of the subsequent process to inform him that the production process needs to be adjusted; S35. Feedback mechanism and adjustment confirmation: When the system adjusts the task priority, the quality inspector and production operator must confirm that all adjustment measures have been implemented before subsequent operations, including rework, correction and additional inspection work. Before the subsequent process starts, the operator needs to confirm whether all improvements and adjustment measures have been implemented and provide necessary quality feedback; S36. Execution and progress tracking: Based on the adjusted priority, the correction task will be executed first and the progress will be monitored. After the correction task is completed, the system will automatically return to the regular production task schedule and track the execution progress of each task in real time to avoid any process delay affecting the overall production cycle.

3. A multi-process collaborative digital distribution cabinet quality inspection management method as claimed in claim 1, characterized in that: In step S4, real-time analysis is performed based on the recorded data, and potential defects are predicted in combination with historical big data analysis, potential quality problems are discovered, and early warning processing is performed. The specific steps are as follows; S41. Data collection and preprocessing: Real-time collection of quality-related data from multiple sources including production lines, quality inspection links, and equipment monitoring systems. The data includes dimensions, torque values, resistance values, power-on parameters, appearance inspection results, and functional test results. The collected raw data is cleaned to remove outliers, missing values, and noise, and data of different dimensions are standardized and normalized. Quality prediction features are extracted from the raw data, and key influencing factors are obtained through historical data analysis. S42. Real-time prediction: Use historical data to perform big data analysis and integrate the formula ,in It is a quality control indicator that measures the comprehensive level of electrical connection stability and insulation performance. R c is the contact resistance, R ins is the insulation resistance, T The stability of the electrical connection depends on the tightening torque of the terminal. is the intercept term, , is the regression coefficient, which indicates the effect of each independent variable on the dependent variable. It is an error term, and the model learns the potential laws of quality problems and adjusts the parameters to obtain the best prediction effect. In the production process, the quality data collected in real time is input into the formula for real-time prediction. Based on the input data, the potential quality problems of each process and product are predicted, and whether a part of a distribution cabinet is defective is predicted; S43. Real-time warning and dynamic response: Set thresholds for the model's prediction results based on historical data and business needs; when the defect prediction probability exceeds a certain value, trigger a warning; when a serious defect is predicted, prioritize rework and production stoppage for inspection; when the probability and severity of a potential defect are predicted to exceed the set threshold, the system automatically triggers a warning and notifies relevant personnel; S44. Feedback mechanism and model adjustment: After the warning is triggered, the quality inspector will conduct manual verification to confirm whether there are actual defects. If there are actual defects, they will be handled according to the repair and rework process. If no defects are found, the information will be fed back to the model to help it make more accurate predictions in the future; S45. Quality improvement and optimization: Through continuous data analysis, the system can automatically identify processes and production links that are prone to quality problems, and provide data support for process improvement. Through the quality reports and analysis results generated by the system, production managers can identify bottlenecks and optimization space in the production process, and further improve production processes and procedures. S46. Data recording and traceability: Record each time an early warning is triggered, including the type of warning, predicted defects, and treatment measures. These data provide a complete record for subsequent quality management and problem tracing. After a problem is discovered, relevant production records, quality inspection data, and equipment status can be quickly queried to help managers analyze the root cause of the failure.

4. A multi-process collaborative digital distribution cabinet quality inspection management system, characterized in that: The multi-process collaborative digital distribution cabinet quality inspection management system is used to implement any multi-process collaborative digital distribution cabinet quality inspection management method as claimed in claims 1-3; the multi-process collaborative digital distribution cabinet quality inspection management system includes: quality inspection planning and task allocation module, data acquisition and recording module, multi-process information sharing and collaboration module, quality inspection and analysis module, early warning and fault handling module, quality inspection result recording and tracing module; The quality inspection planning and task allocation module: according to the design and production process of the power distribution cabinet, the production process is broken down into multiple processes, and the quality inspection standards, detection methods, standard values, and data types required to be collected for each process are determined. Intelligent scheduling is performed based on production plans, production line loads, and historical data to optimize resource utilization and task allocation. Data collection and recording module: manual and automatic data collection. The collected data types include dimensional accuracy, torque value, insulation resistance, and power-on parameters. The data is directly imported into the quality inspection management platform through the system interface; Multi-process information sharing and collaboration module: A real-time information sharing platform between processes ensures that operators of each process can view the quality data and test results of the previous process in real time, and dynamically adjust the task priority of the subsequent process based on the quality inspection results of the previous process; Quality inspection and analysis module: Real-time analysis of data from each process, discovery of potential quality problems in the production process based on historical data and models, and early warning processing; Early warning and fault handling module: When real-time data and prediction results show potential defects, the system automatically triggers an early warning to remind relevant personnel, identify potential problems and make predictions through big data analysis, and take handling measures in advance to prevent production delays and quality losses; Quality inspection result recording and traceability module: All quality inspection data, test results and treatment measures are recorded in real time and stored in the digital system to ensure data security and traceability. For unqualified distribution cabinets, the system will generate a special unqualified report and guide them to the rework and scrapping process.

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