A method and system for managing production quality information of transportation equipment

Abstract

The present invention discloses a method and system for managing traffic equipment production quality information, relating to the technical field of data processing. The method includes: setting up an RPA data robot to verify the acquired user information to determine the user role; extracting debugging test files and generating debugging inspection information; aggregating and processing NCR problem report information to obtain an NCR summary report for trend analysis to obtain an NCR trend report; acquiring open item list information, measuring tool list information, train project information, logistics data information, and process inspection information, and integrating all the information to obtain integrated management information; performing data analysis on the integrated management information based on big data algorithms, and performing visualization processing on the data analysis results and the integrated management information. The present invention forms a comprehensive information management of production management, so as to achieve a more ideal effect in the management of traffic equipment production quality information.

Description

Technical Field

[0001] The present invention mainly relates to the technical field of data processing, and particularly relates to a method and system for managing traffic equipment production quality information. Background Art

[0002] With the continuous improvement of the control over traffic equipment production, more and more technical means have been continuously introduced into the production management of traffic equipment. Therefore, the management of production quality is a very important task. At present, the management of traffic equipment production quality information is still mainly carried out through manual operations, manually counting relevant data, making reports and then submitting them for review. However, this method has insufficient management refinement, low efficiency and cumbersome manual operations, and is prone to calculation errors. In response to this, some enterprises have begun to adopt big data information management technology to manage production quality data. However, it has not formed a complete quality data management process, and at the same time, the degree of automation of data management is also insufficient, resulting in the inability to form a comprehensive information management of production management, and thus unable to provide a comprehensive and accurate data basis for relevant production links, making the management of traffic equipment production quality information fail to achieve the expected effect. Summary of the Invention

[0003] The purpose of the present invention is to overcome the deficiencies of the prior art. The present invention provides a method and system for managing traffic equipment production quality information, forming a comprehensive information management of production management, thereby providing a more comprehensive and accurate data basis for relevant production links, and making the management of traffic equipment production quality information achieve a more ideal effect.

[0004] To solve the above technical problems, the present invention provides a method for managing traffic equipment production quality information, the method comprising:

[0005] Setting up an RPA data robot, obtaining user information based on the RPA data robot, verifying the user information, obtaining corresponding role permissions, and determining the user role based on the corresponding role permissions;

[0006] Extracting commissioning test files based on the RPA data robot, and generating commissioning inspection information based on the commissioning test tasks generated by the corresponding user role approving the commissioning test files;

[0007] Extracting NCR problem report information, performing summary processing on the NCR problem report information to obtain an NCR summary report, and performing trend analysis based on the NCR summary report to obtain an NCR trend report;

[0008] Obtain the open item list information, measuring tool list information, train project information, logistics data information, and process inspection information, and integrate the commissioning inspection information, NCR summary report, NCR trend report, open item list information, measuring tool list information, train project information, logistics data information, and process inspection information to obtain integrated management information;

[0009] Perform data analysis on the integrated management information based on big data algorithms to obtain data analysis results, and perform visualization processing on the data analysis results and the integrated management information.

[0010] Optionally, the setting of the RPA data robot includes:

[0011] Create a custom extension component based on the NodeJS architecture and define an automated process based on the custom extension component;

[0012] Select the target components of the automated process based on the RPA tool and set the process rules and process logic sequence;

[0013] Configure the RPA designer and RPA runner based on the automated process, target components, process rules, and process logic sequence, and build an RPA data robot based on the RPA designer and RPA runner.

[0014] Optionally, the verification of the user information to obtain the corresponding role permissions and determine the user role based on the corresponding role permissions includes:

[0015] Obtain the first information verification identifier based on the user information and match the first information verification identifier with the stored second information verification identifier to obtain a matching result;

[0016] Divide the corresponding role permissions based on the matching result and assign user roles based on the corresponding role permissions.

[0017] Optionally, the extraction of the commissioning test file based on the RPA data robot includes:

[0018] Import the commissioning test table and schedule the data extraction process based on the RPA data robot;

[0019] The RPA data robot scans the information of the commissioning test table based on the data extraction process to obtain commissioning test data;

[0020] Perform data cleaning processing on the commissioning test data to obtain the commissioning test data after data cleaning processing, and generate a commissioning test file based on the commissioning test data after data cleaning processing.

[0021] Optionally, generating commissioning inspection information based on the commissioning test tasks generated by approving the commissioning test documents by the corresponding user roles, including:

[0022] Conducting wire alignment commissioning based on the commissioning test tasks to obtain wire alignment commissioning information;

[0023] Conducting wiring test based on the commissioning test tasks to obtain wiring test information;

[0024] Conducting functional commissioning test based on the commissioning test tasks to obtain functional commissioning test information;

[0025] Conducting wire tracing and drawing verification test based on the commissioning test tasks to obtain wire tracing and drawing verification test information;

[0026] Marking the wire alignment commissioning information, wiring test information, functional commissioning test information, and wire tracing and drawing verification test information as unqualified respectively to obtain wire alignment unqualified information, wiring unqualified information, functional commissioning unqualified information, and wire tracing and drawing verification unqualified information;

[0027] Obtaining several corresponding fault handling information based on the wire alignment unqualified information, wiring unqualified information, functional commissioning unqualified information, and wire tracing and drawing verification unqualified information;

[0028] Obtaining secondary test information, where the secondary test information is generated by executing corresponding tasks based on several corresponding fault handling information;

[0029] Generating commissioning inspection information based on the secondary test information, wire alignment commissioning information, wiring test information, functional commissioning test information, and wire tracing and drawing verification test information.

[0030] Optionally, extracting NCR problem report information and performing summary processing on the NCR problem report information to obtain an NCR summary report, including:

[0031] Obtaining unqualified material information generated during quality inspection and generating a material disposal plan based on the unqualified material information;

[0032] Obtaining rework report information generated by performing rework processing based on the material disposal plan, and performing summary processing on the rework report information, material disposal plan, and unqualified material information to obtain an NCR summary report.

[0033] Optionally, performing trend analysis based on the NCR summary report to obtain an NCR trend report, including:

[0034] Performing analysis on the responsibility distribution situation based on the NCR summary report to obtain the analysis result of the responsibility distribution situation;

[0035] Based on the non-conforming material information in the NCR summary report and combined with the analysis results of the responsibility distribution, a trend analysis is carried out to obtain the trend analysis results, and an NCR trend report is generated based on the trend analysis results and the analysis of the responsibility distribution situation.

[0036] Optionally, integrating the debugging inspection information, NCR summary report, NCR trend report, open item list information, measuring tool list information, train project information, logistics data information, and process inspection information to obtain integrated management information, including:

[0037] Set a data quantification form, and quantify the correlation of the debugging inspection information, NCR summary report, NCR trend report, open item list information, measuring tool list information, train project information, logistics data information, and process inspection information based on the data quantification form;

[0038] Set custom constraint items, and integrate the debugging inspection information, NCR summary report, NCR trend report, open item list information, measuring tool list information, train project information, logistics data information, and process inspection information based on the custom constraint items combined with the correlation to obtain integrated management information.

[0039] Optionally, perform data analysis on the integrated management information based on big data algorithms to obtain data analysis results, and perform visualization processing on the data analysis results and the integrated management information, including:

[0040] Perform data comparison based on the integrated management information to obtain a data comparison result;

[0041] Perform data statistics based on the integrated management information to obtain a data statistics result;

[0042] Perform comprehensive analysis based on the data comparison result and the data statistics result to obtain a comprehensive analysis result;

[0043] Perform visual marking processing on the integrated management information, comprehensive analysis result, data comparison result, and data statistics result to obtain the integrated management information, comprehensive analysis result, data comparison result, and data statistics result after visual marking processing;

[0044] Perform visual mapping on the integrated management information, comprehensive analysis result, data comparison result, and data statistics result after visual marking processing to obtain visual integrated management information, visual comprehensive analysis result, visual data comparison result, and visual data statistics result.

[0045] In addition, the present invention also provides a traffic equipment production quality information management system, and the system includes:

[0046] Robot setting and role determination module: used to set up RPA data robots, obtain user information based on the RPA data robots, verify the user information, obtain corresponding role permissions, and determine user roles based on the corresponding role permissions;

[0047] Debugging test module: used to extract debugging test files based on the RPA data robots, and generate debugging inspection information based on the debugging test tasks generated by the approval of the debugging test files by the corresponding user roles;

[0048] NCR report analysis module: used to extract NCR problem report information, perform summary processing on the NCR problem report information, obtain an NCR summary report, and perform trend analysis based on the NCR summary report to obtain an NCR trend report;

[0049] Information integration module: used to obtain open item list information, measuring tool list information, train project information, logistics data information, and process inspection information, and integrate the debugging inspection information, NCR summary report, NCR trend report, open item list information, measuring tool list information, train project information, logistics data information, and process inspection information to obtain integrated management information;

[0050] Data analysis and visualization module: used to perform data analysis on the integrated management information based on big data algorithms to obtain data analysis results, and perform visualization processing on the data analysis results and the integrated management information.

[0051] In the embodiment of the present invention, RPA data robots are set up to realize the automated process of production quality data management, reduce the data processing errors caused by manual intervention, and improve the efficiency and accuracy of the execution of each process in production quality management. By extracting debugging test files through RPA data robots and generating debugging test information based on the debugging test files, more comprehensive and specific debugging inspection information can be obtained from multiple debugging links. At the same time, by extracting the NCR problem report information to generate an NCR summary report and an NCR trend report, the comprehensive information management of quality inspection is realized. Integrating various types of information data, and then performing data analysis on the integrated management information through big data algorithms, the core data of relevant links can be directly and clearly understood, further optimizing the production quality management process, forming a comprehensive information management of production management, thereby providing a more comprehensive and accurate data basis for relevant production links, and making the production quality information management of transportation equipment reach a more ideal effect. Description of the Drawings

[0052] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0053] Figure 1 is a schematic flowchart of a traffic equipment production quality information management method in an embodiment of the present invention;

[0054] Figure 2 is a schematic structural composition diagram of a traffic equipment production quality information management system in an embodiment of the present invention. Detailed implementation manners

[0055] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0056] Embodiment 1

[0057] Please refer to Figure 1 , Figure 1 which is a schematic flowchart of a traffic equipment production quality information management method in an embodiment of the present invention.

[0058] As Figure 1 shown, a traffic equipment production quality information management method, the method includes:

[0059] S11: Set up an RPA data robot, obtain user information based on the RPA data robot, verify the user information, obtain the corresponding role permissions, and determine the user role based on the corresponding role permissions;

[0060] In the specific implementation process of the present invention, the setting of the RPA data robot includes: creating a custom extension component based on the NodeJS architecture, and defining an automated process based on the custom extension component; selecting the target component of the automated process based on the RPA tool, and setting the process rules and the process logic sequence; configuring the RPA designer and the RPA runner based on the automated process, the target component, the process rules and the process logic sequence, and constructing the RPA data robot based on the RPA designer and the RPA runner.

[0061] Further, verifying the user information to obtain corresponding role permissions and determining a user role based on the corresponding role permissions includes: obtaining a first information verification identifier based on the user information, and matching the first information verification identifier with a stored second information verification identifier to obtain a matching result. Dividing corresponding role permissions based on the matching result, and assigning a user role based on the corresponding role permissions.

[0062] Specifically, create custom extension components based on the NodeJS architecture. The NodeJS architecture is a progressive framework for building efficient and scalable server-side applications. It is based on a single-threaded event-driven architecture with a non-blocking I / O model. Create custom components through the Component constructor in the NodeJS architecture, obtain the extension component instance object through the Component constructor, set listening events and trigger events for the extension component instance object, define the life cycle of listening events and trigger events based on the Component constructor combined with the behaviors function. The behaviors function is a function for sharing event or component life cycles. Build a Document Object Model (DOM) tree structure through listening events, trigger events, and the life cycle. Create custom extension components according to the DOM tree structure combined with the definitionFilter definition section. The definitionFilter definition section supports custom component extension, can inject parameters into component extension, and improve the processing efficiency of component extension. The custom extension components can include mouse operation components, system operation components, text recognition components, and keyboard operation simulation components. Define an automated process based on the custom extension components, build a task scheduling architecture using the multi-tenant isolation method based on the custom extension components, set up a multi-threaded execution framework, and define an automated process based on the task scheduling architecture and the multi-threaded execution framework combined with an asynchronous execution mechanism. Select the target components of the automated process based on the Robotic Process Automation (RPA) tool. The target components include process flow transfer components, exception handling components, task parallel processing components, and collaborative task components, and set process rules and process logic sequences.Configure the RPA Designer and RPA Runner based on the automated process, target component, process rules, and process logic sequence. Orchestrate components according to the automated process, target component, process rules, and process logic sequence to obtain several sub-components. Perform visual orchestration on each sub-component, encapsulate each visually orchestrated sub-component to obtain the target component, and set the Domain Specific Language (DSL) for the target component. DSL can focus on a specified domain and has more powerful expressiveness and adaptability. Build the RPA Designer and RPA Runner through DSL and the target component. The RPA Designer supports diverse components, covering common automation scenarios. Its workflow flowchart can perform business modeling and process mining, and it has intelligent AI components for industry scenarios, supporting exception retry and exception notification for processes to improve runtime stability. The RPA Runner can perform local operations, store privacy data locally, has a function for setting scheduled tasks, can provide real-time feedback and view task execution status, and supports parallel processing and exception retry of tasks. Based on the RPA Designer and RPA Runner, form an RPA data robot. The RPA data robot can simulate manual operations and run continuously for 24 hours. The RPA data robot can directly obtain system-related components, open files, open folders, simulate file copying, etc., and at the same time provide a complete set of mouse operation components for mouse operations, such as moving the mouse, clicking the mouse, double-clicking the left mouse button to drag, right-clicking and dragging, etc. It can also simulate keyboard operations, such as simulating keyboard keys, pasting and copying. Process screen images, such as finding images on the screen, finding text on the screen, finding object contours, color judgment, screen resolution processing, taking screenshots, etc., and can perform text recognition through optical character recognition. Obtain user information based on the RPA data robot, publish a user information authentication process by the RPA data robot, extract the user information sent by the user, obtain a first information verification identifier based on the user information, and match the first information verification identifier with the stored second information verification identifier to obtain a matching result. If the first information verification identifier matches the second information verification identifier successfully, divide the corresponding role permissions, which may include ordinary users and administrators, and assign user roles based on the corresponding role permissions. If the first information verification identifier does not match the second information verification identifier successfully, send a prompt message to the user to re-enter the user information.

[0063] S12: Extract a debugging test file based on the RPA data robot, and generate debugging inspection information based on a debugging test task generated by approving the debugging test file by the corresponding user role;

[0064] In the specific implementation process of the present invention, the extraction of the debugging test file based on the RPA data robot includes: importing a debugging test table and scheduling a data extraction process based on the RPA data robot; the RPA data robot scans the information of the debugging test table based on the data extraction process to obtain debugging test data; performing data cleaning processing on the debugging test data to obtain the debugging test data after data cleaning processing, and generating a debugging test file based on the debugging test data after data cleaning processing.

[0065] Further, the generation of debugging inspection information based on the debugging test task generated by the corresponding user role approving the debugging test file includes: performing line checking and debugging based on the debugging test task to obtain line checking and debugging information; performing wiring test based on the debugging test task to obtain wiring test information; performing function debugging test based on the debugging test task to obtain function debugging test information; performing line checking and drawing verification test based on the debugging test task to obtain line checking and drawing verification test information; respectively performing non-conformance marking on the line checking and debugging information, wiring test information, function debugging test information, and line checking and drawing verification test information to obtain line checking non-conformance information, wiring non-conformance information, function debugging non-conformance information, and line checking and drawing verification non-conformance information; obtaining a corresponding number of fault handling information based on the line checking non-conformance information, wiring non-conformance information, function debugging non-conformance information, and line checking and drawing verification non-conformance information; obtaining secondary test information, where the secondary test information is generated by executing corresponding tasks based on the corresponding number of fault handling information; generating debugging inspection information based on the secondary test information, line checking and debugging information, wiring test information, function debugging test information, and line checking and drawing verification test information.

[0066] Specifically, the process engineer imports the debugging test form, and the RPA data robot schedules the data extraction process based on the RPA data. The RPA data robot scans the information of the debugging test form based on the data extraction process, obtains the target image of the debugging test form, corrects the inclination of the target image, that is, judges the image angle of the target image, and corrects the target image by combining the Hough transform algorithm according to the judged image angle to obtain the corrected target image. The corrected target image is flattened using the perspective correction algorithm to obtain the target image after inclination correction. Text detection is performed on the target image after inclination correction, and the text is regionally limited through a text box to obtain the text target area. Table line detection is performed on the target image to obtain text horizontal lines and text vertical lines. The text target area is segmented into field areas and text recognition is performed based on the text horizontal lines and text vertical lines to obtain the debugging test data. Data cleaning processing is performed on the debugging test data. Data cleaning processing includes selecting subsets, renaming column names, deleting duplicate values, handling missing values, applying data value functions, and handling outliers. Selecting subsets: Selecting the data columns that need to be analyzed and hiding or deleting irrelevant data columns. Renaming column names: Modifying the names of data columns to avoid duplicate or unclear column names. Deleting duplicate values: Deleting duplicate records in the data and keeping only one piece of data. Handling missing values: Filling or ignoring the missing values in the data. The filling methods include manual filling, using constants, using averages, medians, maximum and minimum values, using regression, Bayesian or decision tree methods, etc. Uniformity processing: Splitting, merging or standardizing inconsistent data values in the data to make the data conform to a unified format and rules. Applying data value functions: Performing operations such as filtering, sorting, grouping, and calculating on the data to extract useful information. Handling outliers: Detecting and processing error values or outliers in the data using statistical analysis methods, rule bases, attribute constraints or external data, etc., to obtain the debugging test data after data cleaning processing. A debugging test file is generated based on the debugging test data after data cleaning processing, and the corresponding user role approves the debugging test file to obtain the debugging test task. The corresponding user role is the manager. Line calibration debugging is performed based on the debugging test task. The line calibration task is debugged according to the project number, column number, vehicle number, and line calibration type. Then, the line calibration task is filtered by information such as connectors, line numbers, and wire harness numbers, and the changed line calibration content is supplemented to obtain the line calibration debugging information. Wiring test is performed based on the debugging test task. Information is entered based on the data generated by the wiring test performed according to the debugging test task and the supplemented wiring operation data to obtain the wiring test information. Function debugging test is performed based on the debugging test task. Information is entered based on the data generated by the function debugging test performed according to the debugging test task and the supplemented function debugging operation data to obtain the function debugging test information.Perform wire tracing and drawing verification tests based on the debugging test tasks. Enter information based on the data generated by the wire tracing and drawing verification tests performed according to the debugging test tasks and the supplemented wire tracing and drawing operation data to obtain wire tracing and drawing verification test information. Mark the wiring debugging information, wiring test information, function debugging test information, and wire tracing and drawing verification test information as unqualified respectively to obtain wiring unqualified information, wiring unqualified information, function debugging unqualified information, and wire tracing and drawing verification unqualified information. Obtain a number of corresponding fault handling information based on the wiring unqualified information, wiring unqualified information, function debugging unqualified information, and wire tracing and drawing verification unqualified information. Search for and view fault information according to the corresponding unqualified information to obtain information such as the location and wire number related to the fault. Obtain secondary test information, where the secondary test information is generated by performing corresponding tasks based on a number of corresponding fault handling information. The debugging inspection information is constituted based on the secondary test information, wiring debugging information, wiring test information, function debugging test information, and wire tracing and drawing verification test information.

[0067] S13: Extract NCR problem report information, perform summary processing on the NCR problem report information to obtain an NCR summary report, and perform trend analysis based on the NCR summary report to obtain an NCR trend report;

[0068] In the specific implementation process of the present invention, the extraction of NCR problem report information, the summary processing of the NCR problem report information to obtain an NCR summary report includes: obtaining unqualified material information generated during quality inspection, generating a material disposal plan based on the unqualified material information; obtaining repair report information generated by performing repair processing based on the material disposal plan, and performing summary processing on the repair report information, material disposal plan, and unqualified material information to obtain an NCR summary report.

[0069] Further, the performing trend analysis based on the NCR summary report to obtain an NCR trend report includes: performing analysis on the responsibility distribution situation based on the NCR summary report to obtain a responsibility distribution situation analysis result; performing trend analysis based on the unqualified material information in the NCR summary report combined with the responsibility distribution situation analysis result to obtain a trend analysis result, and generating an NCR trend report based on the trend analysis result and the responsibility distribution situation analysis.

[0070] Specifically, extract the problem report information of the Non-Conformance Report (NCR), obtain the information of non-conforming materials generated during the quality inspection process, generate a material disposal plan based on the non-conforming material information, conduct screening and processing according to the non-conforming material information to obtain screening information, formulate a disposal method based on the screening information, and obtain a material disposal plan. Obtain the rework report information generated during the rework process based on the material disposal plan, and conduct summary processing on the rework report information, the material disposal plan, and the non-conforming material information to obtain an NCR summary report. The NCR summary report records details such as the non-conformance name, occurrence date, and disposal method. Conduct an analysis of the responsibility distribution based on the NCR summary report, divide the responsibilities of each responsible unit and person in charge according to the NCR summary report, and obtain the analysis result of the responsibility distribution. Conduct a trend analysis based on the non-conforming material information in the NCR summary report combined with the analysis result of the responsibility distribution. According to the non-conforming material information combined with the responsibility statistics of the responsible units and persons divided in the analysis result of the responsibility distribution, calculate the average value and standard deviation of the non-conforming materials, as well as the ranking of the responsible units and persons responsible for the non-conforming materials, so as to obtain the trend analysis result, and generate an NCR trend report based on the trend analysis result and the responsibility distribution. Then, automatically generate open items based on the NCR trend report. The open items include inspection standard documents, open item problem handling information, key open items, key quality node open items, and industry association change information.

[0071] S14: Obtain the open item list information, measuring tool list information, train project information, logistics data information, and process inspection information, and integrate the commissioning inspection information, NCR summary report, NCR trend report, open item list information, measuring tool list information, train project information, logistics data information, and process inspection information to obtain integrated management information;

[0072] In the specific implementation process of the present invention, the integration of the commissioning inspection information, NCR summary report, NCR trend report, open item list information, measuring tool list information, train project information, logistics data information, and process inspection information to obtain integrated management information includes: setting a data quantification form, and quantifying the correlation of the commissioning inspection information, NCR summary report, NCR trend report, open item list information, measuring tool list information, train project information, logistics data information, and process inspection information based on the data quantification form; setting custom constraint items, and integrating the commissioning inspection information, NCR summary report, NCR trend report, open item list information, measuring tool list information, train project information, logistics data information, and process inspection information based on the custom constraint items combined with the correlation to obtain integrated management information.

[0073] Specifically, obtain the open item list information, measuring tool list information, train project information, logistics data information, and process inspection information on the production assistance system. The measuring tool list information includes the number of measuring tools and the measuring tool return records. The process inspection information includes process discipline inspection information, technical change information, and process documents. For process discipline inspection, technical changes, and process documents, operations such as addition, modification, deletion, and query can be performed on the system. Set the data quantification form, which can be an integer quantification form or a floating-point quantification form. Quantify the correlation of the debugging and inspection information, NCR summary report, NCR trend report, open item list information, measuring tool list information, train project information, logistics data information, and process inspection information based on the data quantification form. Use the correlation quantification model to quantify the debugging and inspection information, NCR summary report, NCR trend report, open item list information, measuring tool list information, train project information, logistics data information, and process inspection information according to the quantity quantification form to obtain the corresponding correlation. Set custom constraint items, and integrate the debugging and inspection information, NCR summary report, NCR trend report, open item list information, measuring tool list information, train project information, logistics data information, and process inspection information based on the custom constraint items combined with the correlation to obtain integrated management information. The custom constraint items can avoid large deviations in information integration, and integrating information based on the correlation can integrate the overall information corresponding to each category.

[0074] S15: Perform data analysis on the integrated management information based on big data algorithms to obtain data analysis results, and perform visualization processing on the data analysis results and the integrated management information.

[0075] In the specific implementation process of the present invention, the performing data analysis on the integrated management information based on big data algorithms to obtain data analysis results, and performing visualization processing on the data analysis results and the integrated management information includes: performing data comparison based on the integrated management information to obtain a data comparison result; performing data statistics based on the integrated management information to obtain a data statistics result; performing comprehensive analysis based on the data comparison result and the data statistics result to obtain a comprehensive analysis result; performing visual marking processing on the integrated management information, comprehensive analysis result, data comparison result, and data statistics result to obtain the integrated management information, comprehensive analysis result, data comparison result, and data statistics result after visual marking processing; performing visual mapping on the integrated management information, comprehensive analysis result, data comparison result, and data statistics result after visual marking processing to obtain visual integrated management information, visual comprehensive analysis result, visual data comparison result, and visual data statistics result.

[0076] Specifically, data comparison is performed based on the integrated management information. For example, the number of effective line checks is statistically counted according to a time interval and recorded. The year-on-year and month-on-month data in the past are compared to obtain the data comparison result. Data statistics are performed based on the integrated management information. The monthly line check pass rate is statistically counted, and a line chart is made according to the data distribution to reflect the dispersion degree of the line check quality. The monthly line check failure rate is statistically counted, and a histogram is made according to the data distribution, and the situation of the line chart is marked to reflect the discrete situation of the line check failure. The first-pass line check pass rate is calculated to obtain the data statistics result. Based on the data comparison result and the data statistics result, comprehensive analysis is carried out. The completion rate, the number of faults, etc. are displayed arranged according to different train numbers, different vehicle types, and different faults to obtain the comprehensive analysis result. Visual marking processing is performed on the integrated management information, the comprehensive analysis result, the data comparison result, and the data statistics result. Performing visual marking processing is the mapping from data attributes to visual set elements, and the integrated management information, the comprehensive analysis result, the data comparison result, and the data statistics result after visual marking processing are obtained. Visual mapping is performed on the integrated management information, the comprehensive analysis result, the data comparison result, and the data statistics result after visual marking processing. The mapping from visual set elements to visual presentation parameters is performed to obtain the visual integrated management information, the visual comprehensive analysis result, the visual data comparison result, and the visual data statistics result. At the same time, the integrated management information, the comprehensive analysis result, the data comparison result, and the data statistics result are sorted into a report, and the integrated management information, the comprehensive analysis result, the data comparison result, and the data statistics result are stored in a secure database to ensure data confidentiality and prevent data loss.

[0077] In the embodiment of the present invention, an RPA data robot is set up to realize the automated process of production quality data management, reduce the data processing errors caused by manual intervention, and improve the efficiency and accuracy of the execution of each process in production quality management. The RPA data robot extracts the commissioning test files and generates commissioning test information according to the commissioning test files, and can obtain more comprehensive and specific commissioning inspection information from multiple commissioning links. At the same time, an NCR summary report and an NCR trend report are generated through the extracted NCR problem report information, realizing the comprehensive informatization management of quality inspection. Various types of information data are integrated, and then data analysis is performed on the integrated management information through big data algorithms, and the core data of relevant links can be directly and clearly understood, further optimizing the production quality management process, forming a comprehensive informatization management of production management, thereby providing a more comprehensive and accurate data basis for relevant production links, and making the production quality information management of transportation equipment reach a more ideal effect.

[0078] Embodiment 2

[0079] Please refer to Figure 2 , Figure 2It is a schematic diagram of the structural composition of the traffic equipment production quality information management system in the embodiments of the present invention.

[0080] As Figure 2 shown, a traffic equipment production quality information management system, the system includes:

[0081] Robot setting and role determination module 21: used to set up RPA data robots, obtain user information based on the RPA data robots, verify the user information, obtain corresponding role permissions, and determine user roles based on the corresponding role permissions;

[0082] In the specific implementation process of the present invention, the setting of the RPA data robot includes: creating a custom extension component based on the NodeJS architecture, and defining an automated process based on the custom extension component; selecting the target component of the automated process based on the RPA tool, and setting the process rules and process logic sequence; configuring the RPA designer and RPA runner based on the automated process, target component, process rules and process logic sequence, and building the RPA data robot based on the RPA designer and RPA runner.

[0083] Further, the verification of the user information, obtaining corresponding role permissions, and determining user roles based on the corresponding role permissions includes: obtaining a first information verification identifier based on the user information, and matching the first information verification identifier with the stored second information verification identifier to obtain a matching result. Dividing the corresponding role permissions based on the matching result, and assigning user roles based on the corresponding role permissions.

[0084] Specifically, a custom extension component is created based on the NodeJS architecture. The NodeJS architecture is a progressive framework for building efficient and scalable server-side applications. It is based on a single-threaded event-driven architecture with a non-blocking I / O model. A custom component is created through the Component constructor in the NodeJS architecture, and an instance object of the extension component is obtained through the Component constructor. Listen events and trigger events are set for the extension component instance object. The lifecycle of the listen event and the trigger event is defined based on the Component constructor combined with the behaviors function. The behaviors function is a function for sharing event or component lifecycles. The Document Object Model (DOM) tree structure is built through the listen event, the trigger event, and the lifecycle. A custom extension component is created according to the DOM tree structure combined with the definitionFilter definition segment. The definitionFilter definition segment supports custom component extension, can inject parameters into component extension, and improves the processing efficiency of component extension. The custom extension component can include a mouse operation component, a system operation component, an optical character recognition component, and a keyboard operation simulation component. An automated process is defined based on the custom extension component. A task scheduling architecture is built based on the custom extension component using a multi-tenant isolation method, and a multi-threaded execution framework is set. An automated process is defined based on the task scheduling architecture and the multi-threaded execution framework combined with an asynchronous execution mechanism. The target components of the automated process are selected based on the Robotic Process Automation (RPA) tool. The target components include a process flow transfer component, an exception handling component, a task parallel processing component, and a collaborative task component, and process rules and process logic sequences are set.Configure the RPA Designer and RPA Runner based on the automated process, target components, process rules, and process logic sequence. Arrange components according to the automated process, target components, process rules, and process logic sequence to obtain a number of sub-components. Visually arrange each sub-component, encapsulate each visually arranged sub-component to obtain the target component, and set the Domain Specific Language (DSL) of the target component. DSL can focus on a specific domain, with more powerful expressiveness and adaptability. Build the RPA Designer and RPA Runner through DSL and the target component. The RPA Designer supports diverse components, covering common automation scenarios. Its workflow flowchart can perform business modeling and process mining, and has intelligent AI components for industry scenarios, supporting exception retry and exception notification during the process to improve runtime stability. The RPA Runner can execute local operations, save privacy data locally, has a function for setting scheduled tasks, can provide real-time feedback and view task execution status, and supports parallel processing and exception retry of tasks. Based on the RPA Designer and RPA Runner, form the RPA data robot. The RPA data robot can simulate manual operations and run continuously for 24 hours. The RPA data robot can directly obtain system-related components, open files, open folders, simulate file copying, etc., and at the same time provide a complete set of mouse operation components for mouse operations, such as moving the mouse, clicking the mouse, double-clicking the left mouse button to drag, right-clicking and dragging, etc. It can also simulate keyboard operations, such as simulating keyboard key presses, pasting and copying. Process screen images, such as finding images on the screen, finding text on the screen, finding object contours, color judgment, screen resolution processing, screenshot, etc., and can perform text recognition through optical character recognition. Obtain user information based on the RPA data robot, publish a user information authentication process by the RPA data robot, extract the user information sent by the user, obtain the first information verification identifier based on the user information, and match the first information verification identifier with the stored second information verification identifier to obtain a matching result. If the first information verification identifier matches the second information verification identifier successfully, divide the corresponding role permissions. The role permissions can include ordinary users and administrators, and assign user roles based on the corresponding role permissions. If the first information verification identifier does not match the second information verification identifier successfully, send a prompt message to the user to re-enter the user information.

[0085] Debugging test module 22: used to extract debugging test files based on the RPA data robot and generate debugging inspection information based on the debugging test tasks generated by the corresponding user roles for approving the debugging test files;

[0086] In the specific implementation process of the present invention, the extraction of the debugging test file based on the RPA data robot includes: importing a debugging test table and scheduling a data extraction process based on the RPA data robot; the RPA data robot scans the information of the debugging test table based on the data extraction process to obtain debugging test data; performing data cleaning processing on the debugging test data to obtain the debugging test data after data cleaning processing, and generating a debugging test file based on the debugging test data after data cleaning processing.

[0087] Further, the generation of debugging inspection information based on the debugging test task generated by the corresponding user role approving the debugging test file includes: performing line checking and debugging based on the debugging test task to obtain line checking and debugging information; performing wiring test based on the debugging test task to obtain wiring test information; performing function debugging test based on the debugging test task to obtain function debugging test information; performing line checking and drawing verification test based on the debugging test task to obtain line checking and drawing verification test information; respectively marking the line checking and debugging information, wiring test information, function debugging test information, and line checking and drawing verification test information as unqualified to obtain line checking unqualified information, wiring unqualified information, function debugging unqualified information, and line checking and drawing verification unqualified information; obtaining corresponding several fault handling information based on the line checking unqualified information, wiring unqualified information, function debugging unqualified information, and line checking and drawing verification unqualified information; obtaining secondary test information, where the secondary test information is generated by executing corresponding tasks based on the corresponding several fault handling information; generating debugging inspection information based on the secondary test information, line checking and debugging information, wiring test information, function debugging test information, and line checking and drawing verification test information.

[0088] Specifically, the process engineer imports the commissioning test form, and the RPA data robot schedules the data extraction process based on the RPA data. The RPA data robot scans the information of the commissioning test form based on the data extraction process, obtains the target image of the commissioning test form, corrects the inclination of the target image, that is, judges the image angle of the target image, and corrects the target image by combining the Hough transform algorithm according to the judged image angle to obtain the corrected target image. The corrected target image is flattened by using the perspective correction algorithm to obtain the inclined-corrected target image. The text in the inclined-corrected target image is detected, and the text is regionally limited by a text box to obtain the text target area. The table lines of the target image are detected to obtain the text horizontal line and the text vertical line. The text target area is segmented into field areas and text recognition is performed according to the text horizontal line and the text vertical line to obtain the commissioning test data. The commissioning test data is subjected to data cleaning processing, which includes selecting subsets, renaming column names, deleting duplicate values, handling missing values, applying data value functions, and handling outliers. Selecting subsets: Selecting the data columns that need to be analyzed and hiding or deleting the irrelevant data columns. Renaming column names: Modifying the names of the data columns to avoid duplicate or unclear column names. Deleting duplicate values: Deleting the duplicate records in the data and keeping only one piece of data. Handling missing values: Filling or ignoring the missing values in the data. The filling methods include manual filling, using constants, using averages, medians, maximum and minimum values, using regression, Bayesian or decision tree methods, etc. Uniformity processing: Splitting, merging or standardizing the inconsistent data values in the data to make the data conform to a unified format and rules. Applying data value functions: Performing operations such as filtering, sorting, grouping, and calculating on the data to extract useful information. Handling outliers: Detecting and processing the error values or outlier values in the data by using statistical analysis methods, rule bases, attribute constraints or external data, etc., to obtain the commissioning test data after data cleaning processing. A commissioning test file is generated based on the commissioning test data after data cleaning processing, and the corresponding user role approves the commissioning test file to obtain the commissioning test task. The corresponding user role is the manager. The line alignment commissioning is performed based on the commissioning test task, and the line alignment task is debugged according to the project number, column number, vehicle number and line alignment type. Then, the line alignment task is filtered by information such as connectors, line numbers and wire harness numbers, and the line alignment content is supplemented and changed to obtain the line alignment commissioning information. The wiring test is performed based on the commissioning test task, and information is entered according to the data generated by the wiring test performed according to the commissioning test task and the supplemented wiring operation data to obtain the wiring test information. The function commissioning test is performed based on the commissioning test task, and information is entered according to the data generated by the function commissioning test performed according to the commissioning test task and the supplemented function commissioning operation data to obtain the function commissioning test information.Conduct wire tracing and diagram verification tests based on the debugging test tasks, and enter information according to the data generated by the wire tracing and diagram verification tests conducted based on the debugging test tasks and the supplemented wire tracing and diagram operation data to obtain wire tracing and diagram verification test information. Mark the wire alignment debugging information, wiring test information, function debugging test information, and wire tracing and diagram verification test information as unqualified respectively to obtain wire alignment unqualified information, wiring unqualified information, function debugging unqualified information, and wire tracing and diagram verification unqualified information. Obtain several corresponding fault handling information based on the wire alignment unqualified information, wiring unqualified information, function debugging unqualified information, and wire tracing and diagram verification unqualified information, and search for and view fault information according to the corresponding unqualified information to obtain information such as the location and wire number related to the fault. Obtain secondary test information, where the secondary test information is generated by performing corresponding tasks based on several corresponding fault handling information. The debugging inspection information is composed of the secondary test information, wire alignment debugging information, wiring test information, function debugging test information, and wire tracing and diagram verification test information.

[0089] NCR Report Analysis Module 23: Used to extract NCR problem report information, summarize and process the NCR problem report information to obtain an NCR summary report, and conduct trend analysis based on the NCR summary report to obtain an NCR trend report;

[0090] In the specific implementation process of the present invention, the extraction of NCR problem report information, the summary and processing of the NCR problem report information to obtain an NCR summary report include: obtaining unqualified material information generated during the quality inspection process, and generating a material disposal plan based on the unqualified material information; obtaining repair report information generated by performing repair processing based on the material disposal plan, and summarizing and processing the repair report information, material disposal plan, and unqualified material information to obtain an NCR summary report.

[0091] Furthermore, the conduct of trend analysis based on the NCR summary report to obtain an NCR trend report includes: conducting an analysis of the responsibility distribution based on the NCR summary report to obtain the analysis result of the responsibility distribution; conducting trend analysis based on the unqualified material information in the NCR summary report in combination with the analysis result of the responsibility distribution to obtain the trend analysis result, and generating an NCR trend report based on the trend analysis result and the analysis of the responsibility distribution.

[0092] Specifically, extract the problem report information of the Non-Conformance Report (NCR), obtain the information of non-conforming materials generated during the quality inspection process, generate a material disposal plan based on the non-conforming material information, conduct screening and processing according to the non-conforming material information to obtain screening information, formulate a disposal method based on the screening information to obtain a material disposal plan. Obtain the rework report information generated by the rework processing based on the material disposal plan, and conduct summary processing on the rework report information, the material disposal plan, and the non-conforming material information to obtain an NCR summary report. The NCR summary report records details such as the non-conformance name, occurrence date, and disposal method. Conduct an analysis of the responsibility distribution based on the NCR summary report, and divide the responsibilities of each responsible unit and person in charge according to the NCR summary report to obtain the analysis result of the responsibility distribution. Conduct a trend analysis based on the non-conforming material information in the NCR summary report combined with the analysis result of the responsibility distribution. According to the non-conforming material information combined with the responsibility distribution analysis result, statistically calculate the average value, standard deviation of the non-conforming materials, and the ranking of the responsible units and persons responsible for the non-conforming materials, so as to obtain the trend analysis result, and generate an NCR trend report based on the trend analysis result and the responsibility distribution situation analysis. Then, automatically generate open items according to the NCR trend report. The open items include inspection standard documents, open item problem handling information, key open items, key quality node open items, and business link change information.

[0093] Information integration module 24: used to obtain open item list information, measuring tool list information, train project information, logistics data information, and process inspection information, and integrate the commissioning inspection information, NCR summary report, NCR trend report, open item list information, measuring tool list information, train project information, logistics data information, and process inspection information to obtain integrated management information;

[0094] In the specific implementation process of the present invention, the integration of the commissioning inspection information, NCR summary report, NCR trend report, open item list information, measuring tool list information, train project information, logistics data information, and process inspection information to obtain integrated management information includes: setting a data quantification form, and quantifying the correlation of the commissioning inspection information, NCR summary report, NCR trend report, open item list information, measuring tool list information, train project information, logistics data information, and process inspection information based on the data quantification form; setting custom constraint items, and integrating the commissioning inspection information, NCR summary report, NCR trend report, open item list information, measuring tool list information, train project information, logistics data information, and process inspection information based on the custom constraint items combined with the correlation to obtain integrated management information.

[0095] Specifically, obtain the open item list information, measuring tool list information, train project information, logistics data information, and process inspection information on the production assistance system. The measuring tool list information includes the number of measuring tools and the measuring tool return records. The process inspection information includes process discipline inspection information, technical change information, and process documents. For process discipline inspection, technical change, and process documents, operations such as addition, modification, deletion, and query can be performed on the system. Set the data quantization form, which can be an integer quantization form or a floating-point quantization form. Quantify the correlation of the debugging and inspection information, NCR summary report, NCR trend report, open item list information, measuring tool list information, train project information, logistics data information, and process inspection information based on the data quantization form. Use the correlation quantization model to perform correlation quantization on the debugging and inspection information, NCR summary report, NCR trend report, open item list information, measuring tool list information, train project information, logistics data information, and process inspection information according to the quantity quantization form to obtain the corresponding correlation. Set custom constraint items, and integrate the debugging and inspection information, NCR summary report, NCR trend report, open item list information, measuring tool list information, train project information, logistics data information, and process inspection information based on the custom constraint items combined with the correlation to obtain integrated management information. The custom constraint items can avoid large deviations in information integration, and integrating information based on the correlation can integrate the overall information corresponding to each category.

[0096] Data analysis and visualization module 25: Used to perform data analysis on the integrated management information based on big data algorithms to obtain data analysis results, and perform visualization processing on the data analysis results and the integrated management information.

[0097] In the specific implementation process of the present invention, the data analysis on the integrated management information based on big data algorithms to obtain data analysis results, and the visualization processing on the data analysis results and the integrated management information include: performing data comparison based on the integrated management information to obtain a data comparison result; performing data statistics based on the integrated management information to obtain a data statistics result; performing comprehensive analysis based on the data comparison result and the data statistics result to obtain a comprehensive analysis result; performing visualization marking processing on the integrated management information, comprehensive analysis result, data comparison result, and data statistics result to obtain the integrated management information, comprehensive analysis result, data comparison result, and data statistics result after visualization marking processing; performing visualization mapping on the integrated management information, comprehensive analysis result, data comparison result, and data statistics result after visualization marking processing to obtain visual integrated management information, visual comprehensive analysis result, visual data comparison result, and visual data statistics result.

[0098] Specifically, data comparison is performed based on the integrated management information. For example, the number of valid line checks is statistically counted according to a time interval and recorded. The year-on-year and month-on-month data in the past are compared to obtain the data comparison result. Data statistics are performed based on the integrated management information. The monthly line check pass rate is statistically counted, and a line chart is made according to the data distribution to reflect the dispersion degree of the line check quality. The monthly line check failure rate is statistically counted, and a histogram is made according to the data distribution, and the situation of the line chart is marked to reflect the line check failure dispersion. The first-pass line check pass rate is calculated to obtain the data statistics result. Based on the data comparison result and the data statistics result, comprehensive analysis is performed. The completion rate, the number of faults, etc. are displayed arranged according to different train numbers, different vehicle types, and different faults to obtain the comprehensive analysis result. Visual marking processing is performed on the integrated management information, the comprehensive analysis result, the data comparison result, and the data statistics result. Performing visual marking processing is the mapping from data attributes to visual set elements, and the integrated management information, the comprehensive analysis result, the data comparison result, and the data statistics result after visual marking processing are obtained. Visual mapping is performed on the integrated management information, the comprehensive analysis result, the data comparison result, and the data statistics result after visual marking processing. The mapping from visual set elements to visual presentation parameters is performed to obtain the visual integrated management information, the visual comprehensive analysis result, the visual data comparison result, and the visual data statistics result. At the same time, the integrated management information, the comprehensive analysis result, the data comparison result, and the data statistics result are sorted into a report, and the integrated management information, the comprehensive analysis result, the data comparison result, and the data statistics result are stored in a secure database to ensure the confidentiality of the data and prevent data loss.

[0099] In the embodiment of the present invention, an RPA data robot is set up to realize the automated process of production quality data management, reduce the data processing errors caused by manual intervention, and improve the efficiency and accuracy of the execution of each process in production quality management. The RPA data robot extracts the commissioning test files and generates commissioning test information according to the commissioning test files, and can obtain more comprehensive and specific commissioning inspection information from multiple commissioning links. At the same time, the NCR summary report and the NCR trend report are generated through the extracted NCR problem report information, realizing the comprehensive informatization management of quality inspection. Various types of information data are integrated, and then data analysis is performed on the integrated management information through big data algorithms, and the core data of the relevant links can be directly and clearly understood, further optimizing the production quality management process, forming a comprehensive informatization management of production management, thereby providing a more comprehensive and accurate data basis for the relevant production links, and making the production quality information management of transportation equipment reach a more ideal effect.

[0100] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium, which can include: read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk, etc.

[0101] In addition, the above has introduced in detail a traffic equipment production quality information management method and system provided by the embodiments of the present invention. Specific examples should have been used in this article to elaborate on the principles and implementation manners of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A method for managing production quality information of transportation equipment, characterized in that: The method comprises: An RPA data robot is set up, user information is obtained based on the RPA data robot, the user information is verified, corresponding role permissions are obtained, and the user role is determined based on the corresponding role permissions; Extracting a debugging test file based on the RPA data robot, and generating debugging inspection information based on a debugging test task generated by a corresponding user role approving the debugging test file; Extracting NCR problem report information, summarizing the NCR problem report information to obtain an NCR summary report, and performing trend analysis based on the NCR summary report to obtain an NCR trend report; Obtaining the open item list information, the gauge list information, the train project information, the logistics data information and the process inspection information, and integrating the commissioning inspection information, the NCR summary report, the NCR trend report, the open item list information, the gauge list information, the train project information, the logistics data information and the process inspection information to obtain the integrated management information; The integrated management information is analyzed based on a big data algorithm to obtain data analysis results, and the data analysis results and the integrated management information are visualized.

2. The method for managing production quality information of transportation equipment according to claim 1, characterized in that: The setting of the RPA data robot includes: Create custom extension components based on NodeJS architecture, and define automation processes based on the custom extension components; Select the target components of the automation process based on the RPA tool, and set the process rules and process logic sequence; An RPA designer and an RPA runner are configured based on the automation process, target components, process rules, and process logic sequence, and an RPA data robot is built based on the RPA designer and the RPA runner.

3. The method for managing production quality information of transportation equipment according to claim 1, characterized in that: The verifying the user information, obtaining corresponding role permissions, and determining the user role based on the corresponding role permissions includes: Acquire a first information verification identifier based on the user information, and match the first information verification identifier with a stored second information verification identifier to obtain a matching result; Corresponding role permissions are divided based on the matching results, and user roles are assigned based on the corresponding role permissions.

4. The method for managing production quality information of transportation equipment according to claim 1, characterized in that: The extracting the debugging test file based on the RPA data robot includes: Import the debugging test table and schedule the data extraction process based on the RPA data robot; The RPA data robot scans the debugging test table based on the data extraction process to obtain debugging test data; The debugging test data is cleaned to obtain the debugging test data after the data cleaning process, and a debugging test file is generated based on the debugging test data after the data cleaning process.

5. The method for managing production quality information of transportation equipment according to claim 1, characterized in that: The generating of debugging inspection information based on the debugging test task generated by the corresponding user role approving the debugging test file includes: Perform line calibration and debugging based on the debugging test task to obtain line calibration and debugging information; Perform a wiring test based on the debugging test task to obtain wiring test information; Perform a functional debugging test based on the debugging test task to obtain functional debugging test information; Perform a line checking and diagram verification test based on the debugging test task to obtain line checking and diagram verification test information; Respectively marking the calibration and debugging information, wiring test information, function debugging test information and line checking and verification diagram test information as unqualified, and obtaining calibration failure information, wiring failure information, function debugging failure information and line checking and verification diagram failure information; Based on the line calibration failure information, wiring failure information, function debugging failure information and line checking and diagram failure information, corresponding fault handling information is obtained; Acquire secondary test information, where the secondary test information is generated by executing a corresponding task based on corresponding pieces of fault handling information; The debugging and inspection information is generated based on the secondary test information, the line calibration and debugging information, the wiring test information, the functional debugging test information and the line checking and diagram verification test information.

6. The method for managing production quality information of transportation equipment according to claim 1, characterized in that: The extracting of NCR problem report information, summarizing the NCR problem report information, and obtaining an NCR summary report includes: Obtaining unqualified material information generated during the quality inspection process, and generating a material disposal plan based on the unqualified material information; Obtain the repair report information generated by the repair process based on the material disposal plan, and summarize the repair report information, the material disposal plan and the unqualified material information to obtain an NCR summary report.

7. The method for managing production quality information of transportation equipment according to claim 1, characterized in that: The performing trend analysis based on the NCR summary report to obtain the NCR trend report includes: Perform responsibility distribution analysis based on the NCR summary report to obtain responsibility distribution analysis results; A trend analysis is performed based on the unqualified material information in the NCR summary report in combination with the responsibility distribution analysis result to obtain a trend analysis result, and an NCR trend report is generated based on the trend analysis result and the responsibility distribution analysis.

8. The method for managing production quality information of transportation equipment according to claim 1, characterized in that: The debugging inspection information, NCR summary report, NCR trend report, open item list information, gauge list information, train project information, logistics data information and process inspection information are integrated to obtain integrated management information, including: Setting a data quantification form, and quantifying the correlation between the debugging inspection information, NCR summary report, NCR trend report, open item list information, gauge list information, train project information, logistics data information and process inspection information based on the data quantification form; Set custom constraint items, and integrate the debugging inspection information, NCR summary report, NCR trend report, open item list information, gauge list information, train project information, logistics data information and process inspection information based on the custom constraint items and the correlation to obtain integrated management information.

9. The method for managing production quality information of transportation equipment according to claim 1, characterized in that: The step of performing data analysis on the integrated management information based on the big data algorithm to obtain data analysis results, and visualizing the data analysis results and the integrated management information includes: Perform data comparison based on the integrated management information to obtain a data comparison result; Performing data statistics based on the integrated management information to obtain data statistical results; Perform a comprehensive analysis based on the data comparison results and the data statistical results to obtain a comprehensive analysis result; Perform visual marking processing on the integrated management information, comprehensive analysis results, data comparison results and data statistical results to obtain the integrated management information, comprehensive analysis results, data comparison results and data statistical results after visual marking processing; The integrated management information, comprehensive analysis results, data comparison results and data statistical results after visual marking processing are visually mapped to obtain visual integrated management information, visual comprehensive analysis results, visual data comparison results and visual data statistical results.

10. A transportation equipment production quality information management system, characterized in that: The system comprises: Robot setting and role determination module: used to set up the RPA data robot, obtain user information based on the RPA data robot, verify the user information, obtain corresponding role permissions, and determine the user role based on the corresponding role permissions; A debugging test module: used to extract debugging test files based on the RPA data robot, and generate debugging inspection information based on the debugging test tasks generated by the corresponding user role approving the debugging test files; NCR report analysis module: used for extracting NCR problem report information, summarizing the NCR problem report information to obtain an NCR summary report, and performing trend analysis based on the NCR summary report to obtain an NCR trend report; Information integration module: used to obtain the open item list information, gauge list information, train project information, logistics data information and process inspection information, and integrate the commissioning inspection information, NCR summary report, NCR trend report, open item list information, gauge list information, train project information, logistics data information and process inspection information to obtain integrated management information; Data analysis and visualization module: used to perform data analysis on the integrated management information based on big data algorithms, obtain data analysis results, and visualize the data analysis results and integrated management information.

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