Quality tool analysis method and system based on quality improvement activity

Abstract

The invention relates to the technical field of data analysis, in particular to a quality tool analysis method and system based on a quality improvement activity. According to the quality tool analysis method based on the quality improvement activity, firstly, a quality tool is embedded into a service system, then the quality tool is associated with a service scene, then an analysis conclusion is automatically generated by using an AI large model, and finally, the quality tool analysis result is obtained by combining analysis data, a chart generated by the quality tool and an enterprise localization large model or a descriptive summary generated by the AI large model. And calling the prefabricated analysis report template to form an analysis report. According to the quality tool analysis method and system based on the quality improvement activity, the quality tool is embedded into the service system, the quality tool is associated with the actual service scene, a large model is utilized to analyze and summarize the chart, and the analysis report is automatically generated, so that the workload of a user is greatly reduced, and the working efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and particularly to a quality tool analysis method and system based on quality improvement activities. Background Art

[0002] Currently, in industries such as manufacturing, food, and fast-moving consumer goods, for quality improvement, most improvements are carried out through quality improvement activities such as QC groups, quality control circles, Six Sigma improvements, and quality-trustworthy teams. During the implementation of improvement activities, quality tools such as flowcharts, histograms, affinity diagrams, and fishbone diagrams are often used for current situation investigation, root cause analysis, etc. However, quality tools and business systems such as quality systems are independent software systems, deployed separately, and cannot be associated with quality data and quality improvement activities in the business system. Users need to manually import analysis data into the quality tool for analysis, which is inconvenient for users to operate and has low work efficiency.

[0003] In the prior art, there are few cases where quality tools are embedded in business systems, and even fewer cases where quality tools are associated with quality improvement activities.

[0004] For example, the patent with the patent number 201810925436X discloses a method for tracing and analyzing abnormal product quality data based on manufacturing big data. For multi-source heterogeneous structured data in each production link of a manufacturing enterprise, the original quality data is reasonably integrated through targeted data preprocessing measurements to construct a structured data set convenient for large-scale parallel analysis; data features are widely extracted and accurately selected, and appropriate analysis measurements and analysis algorithms are selected to trace and analyze the abnormal data generated during the product production process, providing an accurate method for tracing and analyzing abnormal quality data. This method only classifies, summarizes, integrates, and preprocesses the quality data in each production link of the manufacturing industry, and does not use quality tools for scientific analysis.

[0005] The patent with the patent number 2023112380693 discloses an artificial intelligence-based data analysis and governance system and its method. By collecting target data from various data sources, cleaning and processing the target data to remove duplicate values and missing values, extracting effective features of the target data through machine learning and deep learning for the target data after removing duplicate values and missing values, comprehensively analyzing the target data with the extracted effective features to obtain the analyzed target data, classifying the target data, detecting outliers and abnormal events of the target data by learning the historical records and behavior patterns of the data, and formulating corresponding governance strategies and measures according to the analysis results and abnormal situations of the target data, including improving the quality of the target data and risk control. This method expands the sources of data analysis, including databases, file systems, Web APIs, LOT devices, social media, and obtaining target data from third-party data suppliers, and uses artificial intelligence technology to clean, process, classify, and summarize these data, without involving the use of quality tools.

[0006] To solve the problem of the combination of quality tools used in data analysis and business systems, the present invention proposes a quality tool analysis method and system based on quality improvement activities. Summary of the Invention

[0007] In order to make up for the defects of the prior art, the present invention provides a simple and efficient quality tool analysis method and system based on quality improvement activities.

[0008] The present invention is realized through the following technical solutions:

[0009] A quality tool analysis method based on quality improvement activities, characterized in that it includes the following steps:

[0010] Step S1: Embed the quality tool into the business system

[0011] Integrate the backend logic of the quality tool into the backend components of the existing business system, and graphically display the results of the quality tool analysis through the ECharts plugin;

[0012] In the step S1, when it is impossible to directly integrate into the backend components of the business system, custom select and use mathematical formulas, operation functions, and Python middleware to implement the calculation logic of the quality tool and the graphical display of the quality tool analysis results in the business system.

[0013] Step S2: Associate the quality tool with the business scenario

[0014] The user custom selects the quality tool according to the needs, and generates graphical analysis results by filling in data through the interface or importing data from Excel.

[0015] The user customizes and selects classic cases according to needs, reuses the selected classic cases, and generates graphical analysis results by customizing and modifying or re-importing analysis data;

[0016] Automatically record the usage records of the user and associate them with improvement activities;

[0017] In the step S2, the usage records of the user are associated with improvement activities, and the associated information includes tool name, user name, usage time, analysis data, improvement activity name, and improvement activity stage.

[0018] Step S3: Automatically generate analysis conclusions using an AI large model

[0019] Call the enterprise-localized large model or AI large model to automatically generate a descriptive summary of the graphical display of quality analysis, and use it in the document report after manual analysis and modification of the descriptive summary;

[0020] In the step S3, the AI large model adopts one or a combination of DeepSeek, Kimi, ChatGPT, and Doubao.

[0021] Step S4: Automatically generate an analysis report

[0022] Combine the analysis data, the charts generated by quality tools, and the descriptive summary generated by the enterprise-localized large model or AI large model, and call the prefabricated analysis report template to form an analysis report.

[0023] A quality tool analysis system based on quality improvement activities includes a business system embedding module, a business scenario association module, an AI large model application module, and a report generation module;

[0024] The business system embedding module is responsible for integrating the backend logic of quality tools into the backend components of the existing business system, and the results of quality tool analysis are graphically displayed through the ECharts plug-in;

[0025] When it cannot be directly integrated into the backend components of the business system, the business system embedding module customizes and selects and uses mathematical formulas, operation functions, and Python middleware to implement the calculation logic of quality tools and the graphical display of quality tool analysis results in the business system.

[0026] The business scenario association module is responsible for recording the usage records of the user and associating the usage records with the business scenarios of improvement activities;

[0027] The usage records of the user include the following:

[0028] The user customizes and selects quality tools according to requirements, fills in data through the interface or imports data from Excel to generate graphical analysis results;

[0029] The user customizes and selects classic cases according to requirements, reuses the selected classic cases, and generates graphical analysis results by customizing modifications or re-importing analysis data;

[0030] The associated information recorded by the business scenario association module includes tool name, user name, usage time, analysis data, improvement activity name, and improvement activity stage.

[0031] The AI large model application module is responsible for calling the enterprise-localized large model or AI large model to automatically generate a descriptive summary of the graphical display of quality analysis, and after manually analyzing and modifying the descriptive summary, it is used in the document report;

[0032] The AI large model adopts one or a combination of DeepSeek, Kimi, ChatGPT, and Doubao.

[0033] The report generation module is responsible for combining the analysis data, the charts generated by the quality tools, and the descriptive summary generated by the enterprise-localized large model or AI large model, and calling the pre-made analysis report template to form an analysis report.

[0034] A quality tool analysis device based on quality improvement activities, characterized in that: it includes a memory and a processor; the memory is used to store a computer program, and the processor is used to implement the above method steps when executing the computer program.

[0035] A readable storage medium, characterized in that: a computer program is stored on the readable storage medium, and the computer program implements the above method steps when executed by a processor.

[0036] The beneficial effect of the present invention is that: the quality tool analysis method and system based on quality improvement activities, by embedding quality tools into the business system, associating quality tools with actual business scenarios, using large models to analyze and summarize charts and automatically generate analysis reports, greatly reducing the workload of users and improving work efficiency. Description of the Drawings

[0037] In order 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 use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0038] AppendixFigure 1 This is a schematic diagram of the quality tool usage process of the present invention.

[0039] Appendix Figure 2 This is a schematic diagram of the approval process for classic cases of the present invention. Detailed implementation manners

[0040] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0041] The quality tool analysis method based on quality improvement activities includes the following steps:

[0042] Step S1: Embed the quality tool into the business system

[0043] Integrate the backend logic of the quality tool into the backend components of the existing business system, and the analysis results of the quality tool are graphically displayed through the ECharts plugin;

[0044] In step S1, when it is impossible to directly integrate into the backend components of the business system, custom select and use mathematical formulas, operation functions, and Python middleware to implement the calculation logic of the quality tool and the graphical display of the analysis results of the quality tool in the business system.

[0045] Step S2: Associate the quality tool with the business scenario

[0046] The user custom selects the quality tool according to the needs, and fills in the data through the interface or imports the data in Excel to generate graphical analysis results;

[0047] The user custom selects classic cases according to the needs, and reuses the selected classic cases, and generates graphical analysis results by custom modifying or re-importing the analysis data;

[0048] Automatically record the usage records of the user and associate them with the improvement activities;

[0049] In step S2, the usage records of the user are associated with the improvement activities, and the associated information includes the tool name, user name, usage time, analysis data, improvement activity name, and improvement activity stage.

[0050] Step S3: Automatically generate analysis conclusions using the AI large model

[0051] Call the enterprise local large model or AI large model to automatically generate a descriptive summary of the graphical display of quality analysis. After manual analysis and modification of the descriptive summary, it is used in the document report;

[0052] In step S3, the AI large model adopts one or a combination of DeepSeek, Kimi, ChatGPT, and Doubao.

[0053] Step S4: Automatically generate an analysis report

[0054] Combining the analysis data, the charts generated by quality tools, and the descriptive summary generated by the enterprise local large model or AI large model, call the prefabricated analysis report template to form an analysis report.

[0055] The quality tool analysis system based on quality improvement activities includes a business system embedding module, a business scenario association module, an AI large model application module, and a report generation module;

[0056] The business system embedding module is responsible for integrating the backend logic of the quality tool into the backend components of the existing business system, and the results of quality tool analysis are graphically displayed through the ECharts plugin;

[0057] When it cannot be directly integrated into the backend components of the business system, the business system embedding module custom selects and uses mathematical formulas, operation functions, and Python middleware to implement the calculation logic of the quality tool and the graphical display of the quality tool analysis results in the business system.

[0058] The business scenario association module is responsible for recording the usage records of users and associating the usage records with the business scenarios of improvement activities;

[0059] The usage records of users include the following:

[0060] Users custom select quality tools according to their needs, fill in data through the interface or import data from Excel to generate graphical analysis results;

[0061] Users custom select classic cases according to their needs, reuse the selected classic cases, and generate graphical analysis results by custom modification or re-importing analysis data;

[0062] The association information recorded by the business scenario association module includes tool name, user name, usage time, analysis data, improvement activity name, and improvement activity stage.

[0063] The AI large model application module is responsible for calling the enterprise local large model or AI large model to automatically generate a descriptive summary of the graphical display of quality analysis. After manual analysis and modification of the descriptive summary, it is used in the document report;

[0064] The AI large model adopts one or a combination of DeepSeek, Kimi, ChatGPT, and Doubao.

[0065] The report generation module is responsible for combining the analysis data, the charts generated by quality tools, and the descriptive summaries generated by the enterprise localization large model or the AI large model, and calling the prefabricated analysis report template to form an analysis report.

[0066] The quality tool analysis device based on quality improvement activities includes a memory and a processor; the memory is used to store computer programs, and the processor is used to implement the above-mentioned method steps when executing the computer programs.

[0067] For the readable storage medium, a computer program is stored on the readable storage medium, and when the computer program is executed by a processor, the above-mentioned method steps are implemented.

[0068] Embodiment

[0069] 1) The types of quality tools that the system initialization can support;

[0070] 2) When conducting root cause analysis of QC activities, quality improvers open the quality tool and select the fishbone diagram.

[0071] 3) Quality improvers edit the fishbone diagram online, or fill in Excel according to the format requirements and import it into the system to generate a fishbone diagram, which can be exported as a picture and saved to the computer side.

[0072] 4) After generating the fishbone diagram, quality improvers click the corresponding button, and the system automatically calls the large model to generate the root cause analysis conclusion.

[0073] 5) The system automatically records the fishbone diagram and analysis conclusion generated by quality improvers.

[0074] 6) The system automatically calls the prefabricated analysis report template to insert the analysis data, the fishbone diagram, and the root cause analysis conclusion generated by the large model. After quality improvers adjust the automatically generated report, the root cause analysis report is completed.

[0075] 7) Quality improvers believe that the fishbone diagram they made represents a type of problem and is typical, so they apply online for a classic case and submit it to the management for approval.

[0076] 8) After the management approves, a text message reminder is automatically sent to quality improvers.

[0077] 9) At the end of the year, the management staff counted the usage of quality tools. The reuse times of the fishbone diagram case made by this quality improver ranked first. According to the company's requirements, the quality department rewarded this quality improver.

[0078] This quality tool analysis method and system based on quality improvement activities supports the tracking management of each stage of the enterprise QC group improvement activities from topic selection, current situation investigation, design goal, cause analysis, key cause analysis to the final activity summary.

[0079] According to statistics, before the application of this method, QC improvers downloaded or collected analysis data offline from the business system, drew required flowcharts, histograms, affinity diagrams, fishbone diagrams and other graphs through Visio, Excel, PPT or online mapping tools searched on the Internet, took screenshots and put them into the activity stage tracking document, and manually analyzed and summarized in the document. It took about 3 hours to switch between tools, systems and the network many times from data sorting to report writing.

[0080] After the application of this method, QC quality improvers only need to open the business system, upload analysis data and call quality tools to generate required charts. If there are similar scenarios, directly reuse the scenarios and simply modify the data to generate new charts. Automatically summarize the charts and embed them into the analysis report document. The whole process only takes less than 10 minutes.

[0081] In summary, in the present invention, by embedding quality tools into the business system, associating quality tools with actual business scenarios, using large models to analyze and summarize charts and automatically generate analysis reports, the workload of users is greatly reduced and the work efficiency is improved.

[0082] The above-described embodiments are only one of the specific implementation manners of the present invention. The general changes and substitutions made by those skilled in the art within the scope of the technical solution of the present invention should be included in the protection scope of the present invention.

Claims

1. A quality tool analysis method based on quality improvement activities, characterized in that: It includes the following steps: Step S1: Embed the quality tool into the business system Integrate the backend logic of the quality tool into the backend components of the existing business system, and the analysis results of the quality tool are graphically displayed through the ECharts plugin; Step S2: Associate the quality tool with the business scenario The user customizes and selects the quality tool according to the requirements, and fills in the data through the interface or imports the data from Excel to generate a graphical analysis result; The user customizes and selects a classic case according to the requirements, reuses the selected classic case, and generates a graphical analysis result by customizing the modification or re-importing the analysis data; Automatically record the usage records of the user and associate them with the improvement activities; Step S3: Use the AI large model to automatically generate analysis conclusions Call the enterprise-localized large model or AI large model to automatically generate a descriptive summary of the graphical display of the quality analysis, and use it in the document report after manual analysis and modification of the descriptive summary; Step S4: Automatically generate an analysis report Combine the analysis data, the charts generated by the quality tool, and the descriptive summary generated by the enterprise-localized large model or AI large model, and call the prefabricated analysis report template to form an analysis report.

2. The quality tool analysis method based on quality improvement activities according to claim 1, wherein: In the above step S1, when it is impossible to directly integrate into the backend components of the business system, custom select and use mathematical formulas, operation functions, and Python middleware to implement the calculation logic of the quality tool and the graphical display of the quality tool analysis results in the business system.

3. The quality tool analysis method based on quality improvement activities according to claim 1, wherein: In the above step S2, the usage records of the user are associated with the improvement activities, and the associated information includes the tool name, user name, usage time, analysis data, improvement activity name, and improvement activity stage.

4. The quality tool analysis method based on quality improvement activities according to claim 1, characterized in that: In the above step S3, the AI large model adopts one or a combination of DeepSeek, Kimi, ChatGPT, and Doubao.

5. A quality tool analysis system based on quality improvement activities, characterized in that: It includes: A business system embedding module, a business scenario association module, an AI large model application module, and a report generation module; The business system embedding module is responsible for integrating the backend logic of the quality tool into the backend components of the existing business system, and the analysis results of the quality tool are graphically displayed through the ECharts plugin; The business scenario association module is responsible for recording the usage records of the user and associating the usage records with the business scenario of the improvement activities; The usage records of the user include the following: The user customizes and selects the quality tool according to the requirements, and fills in the data through the interface or imports the data from Excel to generate a graphical analysis result; The user customizes and selects a classic case according to the requirements, reuses the selected classic case, and generates a graphical analysis result by customizing the modification or re-importing the analysis data; The AI large model application module is responsible for calling the enterprise-localized large model or AI large model to automatically generate a descriptive summary of the graphical display of the quality analysis, and using it in the document report after manual analysis and modification of the descriptive summary; The report generation module is responsible for combining the analysis data, the charts generated by the quality tool, and the descriptive summary generated by the enterprise-localized large model or AI large model, and calling the prefabricated analysis report template to form an analysis report.

6. The quality tool analysis system based on quality improvement activities according to claim 5, wherein: When it is impossible to be directly integrated into the backend components of the business system, the embedded module of the business system customarily selects and utilizes mathematical formulas, operation functions, and Python middleware to implement the calculation logic of quality tools and the graphical display of the analysis results of quality tools in the business system.

7. The quality tool analysis system based on quality improvement activities according to claim 5, characterized in that: The associated information recorded by the business scenario association module includes tool name, user name, usage time, analysis data, improvement activity name, and improvement activity stage.

8. The quality tool analysis system based on quality improvement activities according to claim 7, characterized in that: The AI large model adopts one or a combination of DeepSeek, Kimi, ChatGPT, and Doubao.

9. A quality tool analysis device based on quality improvement activities, characterized in that: It includes a memory and a processor; the memory is used to store a computer program, and the processor is used to implement the method described in any one of claims 1 to 4 when executing the computer program.

10. A readable storage medium, characterized in that: A computer program is stored on the readable storage medium, and when the computer program is executed by the processor, it implements the method described in any one of claims 1 to 4.

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