Defective product analysis system based on production data statistics

Abstract

The invention relates to the field of defective product analysis in automation of the manufacturing industry, in particular to a defective product analysis system based on production data statistics, and greatly improves the efficiency and accuracy of defective product analysis. The system comprises a data processing module used for collecting and processing data from different sources; the display module is used for displaying the processed data on a user interface and providing a visual chart and a report; the Yield module is used for analyzing the yield of the product; the iFA module is used for carrying out bad diagnosis and analysis on a set workstation or process flow; the relevance module is used for analyzing relevance among different parameters; the automatic early warning module is used for monitoring the change trend of the key indexes and giving an alarm in time when an abnormal condition occurs; and the reporting module is used for automatically generating various types of reports. The method is suitable for defective product analysis in the manufacturing industry.

Description

Technical Field

[0001] The present invention relates to the field of fault analysis in manufacturing automation, and in particular to a system for analyzing defective products based on production data statistics. Background Art

[0002] In the current manufacturing environment, defective product analysis is a key link in ensuring product quality and production efficiency. Currently, the commonly used manufacturing execution system can collect and display production data in real time, but its function is limited to the display level of data and lacks in-depth analysis capabilities. This means that the root cause analysis of defective products, the diagnosis of production line problems, and the formulation of preventive measures are still highly dependent on manual operations, which is not only inefficient, but also may affect the accuracy and consistency of the analysis results due to human factors. Summary of the invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a defective product analysis system based on production data statistics, which greatly improves the efficiency and accuracy of defective product analysis.

[0004] The present invention adopts the following technical scheme to achieve the above-mentioned purpose. The present invention provides a system for analyzing defective products based on production data, including: Data processing module, used to collect and process data from different sources; The presentation module is used to display the processed data on the user interface and provide visual charts and reports; Yield module, used to analyze product yield; iFA (intelligent Failure analysis) module, used to diagnose and analyze failures of set workstations or process flows; Correlation module, used to analyze the correlation between different parameters; Automatic early warning module, used to monitor the changing trends of key indicators and issue timely alarms when abnormal situations occur; Reporting module, used to automatically generate various types of reports.

[0005] Furthermore, the data processing module includes a data acquisition submodule and a data processing submodule. The sites in the data acquisition submodule are distributed in various locations in the factory and are responsible for collecting workstation status and production data information. The data gateway in the data acquisition submodule summarizes the data collected by the sites and transmits it to the data processing submodule. The data processing submodule stores all collected data in a database and regularly executes data processing tasks, including data cleaning, preprocessing and analysis.

[0006] Furthermore, data cleaning removes invalid data, ensures that there are no duplications, missing or outliers in the input data, data preprocessing formats the data, converts the timestamps of all data into a unified format, and standardizes the data from different sources.

[0007] Furthermore, data analysis includes data grouping and aggregation calculation. Data grouping includes grouping by time period and grouping by production line. Grouping by time period specifically includes grouping data by different time periods according to business needs, including grouping by time granularity of hours, days, weeks or months. Grouping by production line specifically includes grouping data by different production lines, workstations or process sections.

[0008] Furthermore, data aggregation calculations include: Aggregate the grouped data to generate summary data, including the total output, number of qualified products, and number of unqualified products in each time period.

[0009] Furthermore, the Yield module is specifically used to calculate the yield in each time period according to the following formula: Yield rate = number of qualified products / total output × 100%; Among them, the number of qualified products refers to the number of products that have passed all quality inspection links, and the total output refers to the number of all products entering the production line.

[0010] Furthermore, the correlation module is specifically used to connect the data of multiple related tables through the JOIN operation in SQL, and then use subqueries and nested queries to extract data under specific conditions, and finally merge the data under different time periods or different conditions to generate a comprehensive analysis report, including generating complete process correlation analysis results and material correlation analysis results.

[0011] Furthermore, process correlation analysis includes risk position analysis and correlation analysis between workstations and operators. Risk position analysis includes: By analyzing the relationship between Fail Station and risk stations, the actual assembly station that caused the defective products was found. Specifically, the Fail Station was matched with the risk stations through SQL query, the number of defective products at each risk station was counted, and the station with the highest number of defective products was identified. Through recursive query or nested query, extract serial numbers of different orders and their corresponding station information, specifically including extracting the main serial number and its corresponding lower-order serial number from the table through recursive query, and analyzing the impact of different orders of processes on the occurrence of defective products.

[0012] Furthermore, the correlation analysis between workstations and operators includes: Analyze the relationship between workstations and defective product incidence rates to identify workstation problems that lead to defective products, including: using SQL queries to link workstations with defective product records, calculating the defective product incidence rate of each workstation, and analyzing its correlation with the workstations; Analyze the relationship between different operators and the defective product rate, and find out the operator problems that lead to defective products, including: using SQL query to connect the operator number with the defective product record, counting the number of defective products for each operator, and analyzing its correlation with the operator's behavior.

[0013] Furthermore, the material relevance analysis includes: Analyze the relationship between the materials provided by different material suppliers and the incidence of defective products, and find out the material quality problems that lead to defective products. Specifically, use SQL query to connect the material supplier information with the defective product records, count the number of defective products of each supplier, and analyze the correlation between it and material quality; Analyze the relationship between different batches of materials and the incidence of defective products, and find out the batch problems that lead to defective products, including: using SQL query to connect the batch information of materials with the defective product records, counting the number of defective products in each batch, and analyzing its correlation with the material batch.

[0014] The beneficial effects of the present invention are: The present invention overcomes the limitations of existing manufacturing execution systems and achieves in-depth integration and analysis of production data. The system of the present invention seamlessly integrates the data source of the manufacturing execution system, and uses the analysis logic provided by defective product analysis to automatically process the original data. Through complex algorithm calculations, it can accurately identify problems that occur in the production line, including but not limited to whether the specific cause of the error reporting during the station is a workstation failure, human operating error, or material quality problem.

[0015] The present invention can comprehensively analyze the production data every day and timely discover potential risks in the production process. More importantly, the system has a built-in early warning mechanism. When it detects that the yield rate is not up to standard or the number of consecutive errors increases abnormally, it can automatically trigger an early warning signal, providing production management personnel with timely intervention basis, thereby effectively preventing the occurrence of production accidents and improving overall production efficiency and product quality.

[0016] The system of the present invention not only makes up for the deficiencies of existing manufacturing execution systems in data analysis, but also significantly improves the efficiency and accuracy of defective product analysis in the manufacturing industry through intelligent and automated means, providing strong technical support for the company's lean production and continuous improvement. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1The present invention provides a block diagram of a system for analyzing defective products based on production data statistics. DETAILED DESCRIPTION

[0018] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0019] The present invention provides a system for analyzing defective products based on production data statistics. Figure 1 As shown, specifically including: Data processing module, used to collect and process data from different sources; The display module is used to display the processed data on the user interface and provide visual charts and reports; Yield module, used to analyze product yield; iFA module, used to diagnose and analyze faults in a set workstation or process; Correlation module, used to analyze the correlation between different parameters; Automatic early warning module, used to monitor the changing trends of key indicators and issue timely alarms when abnormal situations occur; Reporting module, used to automatically generate various types of reports.

[0020] Specifically, the data processing module includes a data acquisition submodule and a data processing submodule. The sites in the data acquisition submodule are distributed in various locations in the factory and are responsible for collecting workstation status and production data information. The data gateway in the data acquisition submodule summarizes the data collected by the sites and transmits it to the data processing submodule. The data processing submodule stores all collected data in a database (such as an Oracle database) and regularly executes data processing tasks, including data cleaning, preprocessing and analysis.

[0021] Specifically, data cleaning removes invalid data, ensures that there are no duplications, missing values, or outliers in the input data, data preprocessing formats the data, converts the timestamps of all data into a unified format, and standardizes data from different sources.

[0022] Specifically, data analysis includes data grouping and aggregation calculation. Data grouping includes grouping by time period and grouping by production line. Grouping by time period includes grouping data by different time periods according to business needs, including grouping by time granularity of hours, days, weeks or months. Grouping by production line includes grouping data by different production lines, workstations or process sections.

[0023] Specifically, data aggregation calculations include: Aggregate the grouped data to generate summary data, including the total output, number of qualified products, and number of unqualified products in each time period.

[0024] Specifically, the Yield module is used to calculate the yield in each time period according to the following formula: Yield rate = number of qualified products / total output × 100%; Among them, the number of qualified products refers to the number of products that have passed all quality inspection links, and the total output refers to the number of all products entering the production line.

[0025] Specifically, the correlation module is used to connect the data of multiple related tables through the JOIN operation in SQL, and then use subqueries and nested queries to extract data under specific conditions, and finally merge the data under different time periods or different conditions to generate a comprehensive analysis report, including generating complete process correlation analysis results and material correlation analysis results.

[0026] Specifically, process correlation analysis includes risk position analysis and correlation analysis between workstations and operators. Risk position analysis includes: By analyzing the relationship between Fail Station and risk stations, the actual assembly station that caused the defective products was found. Specifically, the Fail Station and the risk stations were matched through SQL query, the number of defective products at each risk station was counted, and the station with the highest number of defective products was identified. Through recursive query or nested query, extract serial numbers of different orders and their corresponding station information, specifically including extracting the main serial number and its corresponding lower-order serial number from the table through recursive query, and analyzing the impact of different orders of processes on the occurrence of defective products.

[0027] Specifically, the correlation analysis between workstations and operators includes: Analyze the relationship between workstations and defective product incidence rates to identify workstation problems that lead to defective products, including: using SQL queries to link workstations with defective product records, calculating the defective product incidence rate of each workstation, and analyzing its correlation with the workstations; Analyze the relationship between different operators and the defective product rate, and find out the operator problems that lead to defective products, including: using SQL query to connect the operator number with the defective product record, counting the number of defective products for each operator, and analyzing its correlation with the operator's behavior.

[0028] Specifically, material relevance analysis includes: Analyze the relationship between the materials provided by different material suppliers and the incidence of defective products, and find out the material quality problems that lead to defective products. Specifically, use SQL query to connect the material supplier information with the defective product records, count the number of defective products of each supplier, and analyze the correlation between it and material quality; Analyze the relationship between different batches of materials and the incidence of defective products, and find out the batch problems that lead to defective products, including: using SQL query to connect the batch information of materials with the defective product records, counting the number of defective products in each batch, and analyzing its correlation with the material batch.

[0029] Specifically, the present invention generates various types of reports through the FineReport reporting tool and supports dynamic data display. The present invention predefines the report style through the report template, and the user can select a suitable template as needed. The data in the database can also be bound to the report in real time to ensure the timeliness of the data.

[0030] The method for realizing large-scale data parallel processing and analysis of the present invention is as follows: Data Sharding: Shard large data sets by timestamp or specific identifier to ensure that each shard can be processed independently on different computing nodes. For example, the code uses MAIN_WORKDAY and MAIN_CONFIG to filter and group data to ensure that the data for each working day is processed in one shard.

[0031] Parallel query: Use the parallel query function of the database to process the data of each shard in parallel. Multiple subqueries are used in the code, and the results are merged through LEFT JOIN to achieve parallel processing and aggregation of data.

[0032] Result aggregation: Aggregate the processing results of each shard to generate a final statistical report. In the code, the data results of different time periods are combined through the UNION ALL operation to generate a comprehensive analysis report.

[0033] The system of the present invention can quickly capture a large amount of data and perform subsequent processing to ensure the timeliness and integrity of the data; it can collect specific data to avoid the interference of a large amount of useless data and ensure the accuracy and reliability of the data; it can also customize screening rules as needed to meet different collection requirements and flexibly respond to various business scenarios.

[0034] The above is only a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the form disclosed herein, and should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be modified within the scope of the concept described herein through the above teachings or the technology or knowledge of the relevant field. The changes and modifications made by those skilled in the art shall not deviate from the spirit and scope of the present invention, and shall be within the scope of protection of the claims attached to the present invention.

Claims

1. A system for analyzing defective products based on production data, characterized in that: include: Data processing module, used to collect and process data from different sources; The presentation module is used to display the processed data on the user interface and provide visual charts and reports; Yield module, used to analyze product yield; iFA module, used to diagnose and analyze faults in a set workstation or process; Correlation module, used to analyze the correlation between different parameters; Automatic early warning module, used to monitor the changing trends of key indicators and issue timely alarms when abnormal situations occur; Reporting module, used to automatically generate various types of reports.

2. The system for analyzing defective products based on production data statistics according to claim 1, characterized in that: The data processing module includes a data acquisition submodule and a data processing submodule. The sites in the data acquisition submodule are distributed in various locations in the factory and are responsible for collecting workstation status and production data information. The data gateway in the data acquisition submodule summarizes the data collected by the sites and transmits it to the data processing submodule. The data processing submodule stores all collected data in the database and regularly executes data processing tasks, including data cleaning, preprocessing and analysis.

3. The defective product analysis system based on production data statistics according to claim 2 is characterized in that: Data cleaning removes invalid data, ensures that there are no duplications, missing values, or outliers in the input data, formats the data through data preprocessing, converts the timestamps of all data into a unified format, and standardizes data from different sources.

4. The system for analyzing defective products based on production data statistics according to claim 2, characterized in that: Data analysis includes data grouping and aggregation calculation. Data grouping includes grouping by time period and grouping by production line. Grouping by time period includes grouping data by different time periods according to business needs, including grouping by time granularity of hours, days, weeks or months. Grouping by production line includes grouping data by different production lines, workstations or process sections.

5. The system for analyzing defective products based on production data statistics according to claim 4 is characterized in that: Data aggregation calculations include: Aggregate the grouped data to generate summary data, including the total output, number of qualified products, and number of unqualified products in each time period.

6. The defective product analysis system based on production data statistics according to claim 1 is characterized in that: The Yield module is specifically used to calculate the yield in each time period according to the following formula: Yield rate = number of qualified products / total output × 100%; Among them, the number of qualified products refers to the number of products that have passed all quality inspection links, and the total output refers to the number of all products entering the production line.

7. The system for analyzing defective products based on production data statistics according to claim 1, characterized in that: The correlation module is specifically used to connect the data of multiple related tables through the JOIN operation in SQL, and then use subqueries and nested queries to extract data under specific conditions. Finally, the data under different time periods or different conditions are merged to generate a comprehensive analysis report, including the generation of complete process correlation analysis results and material correlation analysis results.

8. The system for analyzing defective products based on production data statistics according to claim 7 is characterized in that: Process correlation analysis includes risk position analysis and correlation analysis between workstations and operators. Risk position analysis includes: By analyzing the relationship between Fail Station and risk stations, the actual assembly station that caused the defective products was found. Specifically, the Fail Station and the risk stations were matched through SQL query, the number of defective products at each risk station was counted, and the station with the highest number of defective products was identified. Through recursive query or nested query, extract serial numbers of different orders and their corresponding station information, specifically including extracting the main serial number and its corresponding lower-order serial number from the table through recursive query, and analyzing the impact of different orders of processes on the occurrence of defective products.

9. The system for analyzing defective products based on production data statistics according to claim 8, characterized in that: The correlation analysis between workstations and operators includes: Analyze the relationship between workstations and defective product incidence rates to identify workstation problems that lead to defective products, including: using SQL queries to link workstations with defective product records, calculating the defective product incidence rate of each workstation, and analyzing its correlation with the workstations; Analyze the relationship between different operators and the defective product rate, and find out the operator problems that lead to defective products, including: using SQL query to connect the operator number with the defective product record, counting the number of defective products for each operator, and analyzing its correlation with the operator's behavior.

10. The system for analyzing defective products based on production data statistics according to claim 7, characterized in that: Material relevance analysis includes: Analyze the relationship between the materials provided by different material suppliers and the incidence of defective products, and find out the material quality problems that lead to defective products. Specifically, use SQL query to connect the material supplier information with the defective product records, count the number of defective products of each supplier, and analyze the correlation between it and material quality; Analyze the relationship between different batches of materials and the incidence of defective products, and find out the batch problems that lead to defective products, including: using SQL query to connect the batch information of materials with the defective product records, counting the number of defective products in each batch, and analyzing its correlation with the material batch.

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