Medical instrument production line and medical instrument production method

Abstract

The invention relates to the field of medical instrument production, in particular to a medical instrument production line and a medical instrument production method. The technical problems that although part of existing medical instrument production lines achieve automatic control, the intelligent management aspect still needs to be improved, the real-time collection and analysis capacity of production data is insufficient, and problems in the production process are difficult to find and solve in time are solved. According to the technical scheme, the medical instrument production line comprises a material conveying module, a processing module, an acquisition module, an analysis module, a scheduling module and a monitoring module; according to the invention, the production data is collected in real time, the production process is monitored and analyzed in real time, problems in the production process are identified and classified, and different production scheduling management is carried out according to different problem types, so that the intelligent medical instrument production management system is constructed, real-time collection, analysis and processing of the production data are realized, and the production efficiency is improved. And problems in the production process can be found and solved in time.

Description

Technical Field

[0001] The present invention relates to the field of medical device production, and in particular to a medical device production line and a medical device production method. Background Art

[0002] The medical device production line is a highly integrated and automated production process, covering multiple links from raw material processing, precision machining, assembly to final product packaging and sterilization. These production lines are designed to produce various medical equipment and consumables, such as hemodialysis tubing, infusion tubes, oxygen concentrators, ventilators, etc., to meet the wide needs of the medical industry. Medical device production lines usually use advanced production equipment and technologies, such as plastic extruders, injection molding machines, clean room assembly lines, ethylene oxide sterilization equipment, etc., to ensure product quality and safety. These equipment can accurately control various parameters in the production process, such as temperature, pressure, speed, etc., so as to produce precision products that meet medical standards. However, although some existing medical device production lines have achieved automated control, there is still room for improvement in intelligent management. The real-time collection and analysis capabilities of production data are insufficient, and it is difficult to promptly discover and solve problems in the production process. Summary of the invention

[0003] In order to overcome the existing medical device production lines, although some production lines have achieved automated control, there is still room for improvement in intelligent management. The real-time collection and analysis capabilities of production data are insufficient, making it difficult to promptly discover and solve problems in the production process.

[0004] The technical solution of the present invention is: a medical device production line, comprising: Material conveying module, used to efficiently and accurately transfer raw materials, semi-finished products or finished products between various stations on the production line, ensuring the continuity and timeliness of materials during processing, reducing waiting time and labor costs; Processing module, used to use various processing equipment to perform cutting, forming, assembling, welding, injection molding and other processing operations on materials, and transform them into semi-finished products or finished products that meet the requirements; The acquisition module is used to collect various data during the production process using multiple sets of sensor equipment, such as process parameters such as temperature, pressure, flow, speed, and quality information such as product size, weight, and appearance; The analysis module is used to analyze and process the collected data to evaluate the stability of the production process and the consistency of product quality, discover potential problems in the production process through analysis, and provide data support for optimizing production; The scheduling module is used to schedule and optimize the production line according to the analysis results of the analysis module, ensure the reasonable allocation and efficient execution of production tasks, and dynamically adjust the operation status of the production line according to the production plan and resource status to cope with various changes in the production process; The monitoring module is used to monitor the operating status of the entire production line in real time and conduct all-round monitoring of the production line to ensure the safety and order of the production site.

[0005] Preferably, the material conveying module includes a conveyor belt, an automatic guided vehicle AGV and a robot arm; the conveyor belt, the automatic guided vehicle AGV and the robot arm are used to transport materials from one workstation to another according to a preset path and speed, and to efficiently and accurately transfer raw materials, semi-finished products or finished products between various stations on the production line.

[0006] Preferably, the processing module includes CNC machine tools, injection molding machines and welding machines; CNC machine tools are used for high-precision processing of complex parts, and various processing operations such as cutting, drilling, milling and turning are performed on materials by programming tool paths and cutting parameters; injection molding machines are used for the manufacture of plastic parts, by injecting molten plastic materials into pre-designed molds, and forming the required plastic parts after cooling and solidification; welding machines are used to connect metal or non-metal parts to form a complete medical device structure.

[0007] Preferably, the acquisition module includes a parameter detection unit and a sensor device; the sensor device includes a temperature sensor, a pressure sensor, a flow sensor, a speed sensor, a weighing sensor and a visual detection unit, and the sensor device is used to collect relevant data in the production process when the production line is running; the parameter detection unit is used to collect relevant parameters of the production equipment in the production line during operation.

[0008] Preferably, when the acquisition module is working, it includes the following steps: S101: Install sensors at key locations of the production line according to production line requirements to ensure accurate capture of various parameters during the production process; S102: Configuring the hardware and software of the acquisition module, including setting the frequency, accuracy, and communication protocol of data acquisition; S103: Preliminary processing of the data collected by the sensor, data format conversion and filtering to reduce noise and interference; S104: Clean and organize the collected data to remove invalid data and outliers; S105: Perform data conversion and normalization processing to standardize the data.

[0009] Preferably, the analysis module uses descriptive statistical analysis and inferential statistical analysis to analyze the data collected by the acquisition module, uses indicators such as skewness coefficient and peak coefficient to describe the shape of the data distribution, infers the overall parameters based on the sample data, and performs variance analysis by analyzing the degree of influence of different factors on the dependent variable; establishes a regression model, analyzes the relationship between variables and performs prediction and control, and constructs a model to automatically identify data anomalies in the production process.

[0010] Preferably, when the analysis module is working, it includes the following steps: S201: Calculate basic statistics for the standardized data; calculate the average value of all data to reflect the central position of the data; calculate the median, mode, standard deviation and variance; S202: Draw a frequency distribution diagram of the data and analyze the form of data distribution; S203: An indicator to measure the direction and degree of skewness of data distribution. A positive value indicates right skewness and a negative value indicates left skewness. S204: An indicator that measures the sharpness or flatness of the data distribution. Compared with the normal distribution, a value greater than 1 indicates sharpness, and a value less than 1 indicates flatness; S205: Use sample statistics to directly estimate population parameters; based on sample data, construct intervals that contain a certain confidence level for the population parameters; S206: Identify and test whether one or more independent variables have a significant effect on the dependent variable; S207: Calculate the variance between groups and the variance within groups, and use the F statistic to determine whether the difference is significant; S208: Establish a linear relationship model between independent variables and dependent variables, evaluate the model goodness of fit R² and variable significance; use regression models for prediction and control, and adjust independent variables to optimize dependent variables; S209: Analyze data trends, patterns and outliers to extract key information; S210: extracting feature variables for anomaly detection; S211: Using the Z-score statistical method, set a threshold to identify outliers; S212: Automatically learn data distribution and identify outliers through the One-Class SVM model; train the model and adjust parameters to optimize the detection effect; S213: inputting data into the model to identify outliers; S214: Outputting a list of abnormal values ​​and their characteristic descriptions; S215: Select an X-bar control chart based on the data type and analysis purpose; collect process data and calculate control limits, plot data points on the control chart, and mark outliers; S216: Monitor the production process in real time to see if the data points are within the control limits; S217: When a data point exceeds the control limit, it is considered abnormal and the cause of the abnormality is analyzed; S218: Output the abnormality analysis result.

[0011] Preferably, the scheduling module classifies the identified problems according to the analysis results of the analysis module, schedules and optimizes the production line according to different problem types, prioritizes the problems, allocates resources and formulates scheduling plans, and issues scheduling instructions to relevant personnel and equipment on the production line to ensure the smooth implementation of the scheduling plan.

[0012] Preferably, the scheduling module comprises the following steps when working: S301: Collect data from the analysis module, including abnormal information, equipment status, and material supply status during the production process; S302: Identify problems and classify the identified problems according to types; S303: Prioritize production tasks according to the urgency and importance of the issues; S304: Allocate resources such as equipment, manpower and materials according to production plan and resource status; S305: Develop a detailed scheduling plan based on problem classification and priority sorting; S306: Monitor the operation status and various production indicators of the production line in real time through the MES system or other monitoring tools; S307: According to the real-time monitoring results, the scheduling plan is dynamically adjusted to cope with emergencies in the production process; S308: issuing the adjusted scheduling instructions to relevant personnel and equipment on the production line; S309: Evaluate the execution effect of the scheduling plan, feed back the evaluation results to the analysis module and other relevant departments, and continuously optimize and improve the scheduling module based on the evaluation results and feedback information.

[0013] A method for producing a medical device, comprising the following steps: S401: The production materials are transported to the processing module through the material transport module; the production operation of the production line is safely monitored through the monitoring module; S402: Perform different types of processing on the production materials through the processing modules, and transfer the workpieces between different production equipment in the processing modules by conveying the materials; S403: The data collection module collects various data on the production line during material transmission and production processing through sensor equipment and parameter detection equipment, and collects data related to medical machinery production; S404: preprocessing the collected data and standardizing the data; S405: input the pre-processed data into the analysis module; S406: Detect and analyze the collected data through the analysis module, and identify abnormal data of the production line according to the data characteristics; S407: The control module classifies the identified problems, schedules and optimizes the production line according to different problem types, and formulates relevant scheduling strategies; S408: issuing scheduling instructions to relevant personnel and equipment on the production line; S409: Relevant personnel and equipment receive and execute the dispatch instruction.

[0014] Beneficial effects of the present invention: 1. Compared with existing medical device production lines, although some production lines have achieved automated control, there is still room for improvement in intelligent management. The real-time collection and analysis capabilities of production data are insufficient, making it difficult to timely discover and solve problems in the production process. This invention collects production data in real time, monitors and analyzes the production process in real time, identifies and classifies problems in the production process, and performs different production scheduling management according to different problem types, thereby building an intelligent medical device production management system, realizing real-time collection, analysis and processing of production data, and timely discovering and solving problems in the production process. 2. Analyze the data collected by the acquisition module by using descriptive statistical analysis and inferential statistical analysis, use indicators such as skewness coefficient and kurtosis coefficient to describe the shape of data distribution, infer overall parameters based on sample data, and perform variance analysis by analyzing the degree of influence of different factors on dependent variables; establish a regression model, analyze the relationship between variables and make predictions and controls, and automatically identify data anomalies in the production process by building a model; classify the identified problems according to the analysis results of the analysis module through the scheduling module, schedule and optimize the production line according to different problem types, prioritize problems, allocate resources and formulate scheduling plans, and issue scheduling instructions to relevant personnel and equipment on the production line to ensure the smooth implementation of the scheduling plan, thereby building an intelligent medical device production management system, realizing real-time collection, analysis and processing of production data, and timely discovering and solving problems in the production process. 3. The monitoring module can monitor the operation status of the entire production line in real time and conduct all-round monitoring of the production line to ensure the safety and order of the production site. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 What is shown is a schematic diagram of the workflow of the medical device production line of the present invention; Figure 2 Shown is a schematic diagram of the steps of the medical device production method of the present invention. DETAILED DESCRIPTION

[0016] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0017] See also Figure 1-2 The present invention provides an embodiment: a medical device production line, comprising: Material conveying module, used to efficiently and accurately transfer raw materials, semi-finished products or finished products between various stations on the production line, ensuring the continuity and timeliness of materials during processing, reducing waiting time and labor costs; Processing module, used to use various processing equipment to perform cutting, forming, assembling, welding, injection molding and other processing operations on materials, and transform them into semi-finished products or finished products that meet the requirements; The acquisition module is used to collect various data during the production process using multiple sets of sensor equipment, such as process parameters such as temperature, pressure, flow, speed, and quality information such as product size, weight, and appearance; The analysis module is used to analyze and process the collected data to evaluate the stability of the production process and the consistency of product quality, discover potential problems in the production process through analysis, and provide data support for optimizing production; The scheduling module is used to schedule and optimize the production line according to the analysis results of the analysis module, ensure the reasonable allocation and efficient execution of production tasks, and dynamically adjust the operation status of the production line according to the production plan and resource status to cope with various changes in the production process; The monitoring module is used to monitor the operating status of the entire production line in real time and conduct all-round monitoring of the production line to ensure the safety and order of the production site.

[0018] Preferably, the material conveying module includes a conveyor belt, an automatic guided vehicle AGV and a robot arm; the conveyor belt, the automatic guided vehicle AGV and the robot arm are used to transport materials from one workstation to another according to a preset path and speed, and to efficiently and accurately transfer raw materials, semi-finished products or finished products between various stations on the production line.

[0019] Preferably, the processing module includes CNC machine tools, injection molding machines and welding machines; CNC machine tools are used for high-precision processing of complex parts, and various processing operations such as cutting, drilling, milling and turning are performed on materials by programming tool paths and cutting parameters; injection molding machines are used for the manufacture of plastic parts, by injecting molten plastic materials into pre-designed molds, and forming the required plastic parts after cooling and solidification; welding machines are used to connect metal or non-metal parts to form a complete medical device structure.

[0020] Preferably, the acquisition module includes a parameter detection unit and a sensor device; the sensor device includes a temperature sensor, a pressure sensor, a flow sensor, a speed sensor, a weighing sensor and a visual detection unit, and the sensor device is used to collect relevant data in the production process when the production line is running; the parameter detection unit is used to collect relevant parameters of the production equipment in the production line during operation.

[0021] Preferably, when the acquisition module is working, it includes the following steps: S101: Install sensors at key locations of the production line according to production line requirements to ensure accurate capture of various parameters during the production process; S102: Configuring the hardware and software of the acquisition module, including setting the frequency, accuracy, and communication protocol of data acquisition; S103: Preliminary processing of the data collected by the sensor, data format conversion and filtering to reduce noise and interference; S104: Clean and organize the collected data to remove invalid data and outliers; S105: Perform data conversion and normalization processing to standardize the data.

[0022] Preferably, the analysis module uses descriptive statistical analysis and inferential statistical analysis to analyze the data collected by the acquisition module, uses indicators such as skewness coefficient and peak coefficient to describe the shape of the data distribution, infers the overall parameters based on the sample data, and performs variance analysis by analyzing the degree of influence of different factors on the dependent variable; establishes a regression model, analyzes the relationship between variables and performs prediction and control, and constructs a model to automatically identify data anomalies in the production process.

[0023] Preferably, when the analysis module is working, it includes the following steps: S201: Calculate basic statistics for the standardized data; calculate the average value of all data to reflect the central position of the data; calculate the median, mode, standard deviation and variance; S202: Draw a frequency distribution diagram of the data and analyze the form of data distribution; S203: An indicator to measure the direction and degree of skewness of data distribution. A positive value indicates right skewness and a negative value indicates left skewness. S204: An indicator that measures the sharpness or flatness of the data distribution. Compared with the normal distribution, a value greater than 1 indicates sharpness, and a value less than 1 indicates flatness; S205: Use sample statistics to directly estimate population parameters; based on sample data, construct intervals that contain a certain confidence level for the population parameters; S206: Identify and test whether one or more independent variables have a significant effect on the dependent variable; S207: Calculate the variance between groups and the variance within groups, and use the F statistic to determine whether the difference is significant; S208: Establish a linear relationship model between independent variables and dependent variables, evaluate the model goodness of fit R² and variable significance; use regression models for prediction and control, and adjust independent variables to optimize dependent variables; S209: Analyze data trends, patterns and outliers to extract key information; S210: extracting feature variables for anomaly detection; S211: Using the Z-score statistical method, set a threshold to identify outliers; S212: Automatically learn data distribution and identify outliers through the One-Class SVM model; train the model and adjust parameters to optimize the detection effect; S213: inputting data into the model to identify outliers; S214: Outputting a list of abnormal values ​​and their characteristic descriptions; S215: Select an X-bar control chart based on the data type and analysis purpose; collect process data and calculate control limits, plot data points on the control chart, and mark outliers; S216: Monitor the production process in real time to see if the data points are within the control limits; S217: When a data point exceeds the control limit, it is considered abnormal and the cause of the abnormality is analyzed; S218: Output the abnormality analysis result.

[0024] Preferably, the scheduling module classifies the identified problems according to the analysis results of the analysis module, schedules and optimizes the production line according to different problem types, prioritizes the problems, allocates resources and formulates scheduling plans, and issues scheduling instructions to relevant personnel and equipment on the production line to ensure the smooth implementation of the scheduling plan.

[0025] Preferably, the scheduling module comprises the following steps when working: S301: Collect data from the analysis module, including abnormal information, equipment status, and material supply status during the production process; S302: Identify problems and classify the identified problems according to types; S303: Prioritize production tasks according to the urgency and importance of the issues; S304: Allocate resources such as equipment, manpower and materials according to production plan and resource status; S305: Develop a detailed scheduling plan based on problem classification and priority sorting; S306: Monitor the operation status and various production indicators of the production line in real time through the MES system or other monitoring tools; S307: According to the real-time monitoring results, the scheduling plan is dynamically adjusted to cope with emergencies in the production process; S308: issuing the adjusted scheduling instructions to relevant personnel and equipment on the production line; S309: Evaluate the execution effect of the scheduling plan, feed back the evaluation results to the analysis module and other relevant departments, and continuously optimize and improve the scheduling module based on the evaluation results and feedback information.

[0026] A method for producing a medical device, comprising the following steps: S401: The production materials are transported to the processing module through the material transport module; the production operation of the production line is safely monitored through the monitoring module; S402: Perform different types of processing on the production materials through the processing modules, and transfer the workpieces between different production equipment in the processing modules by conveying the materials; S403: The data collection module collects various data on the production line during material transmission and production processing through sensor equipment and parameter detection equipment, and collects data related to medical machinery production; S404: preprocessing the collected data and standardizing the data; S405: input the pre-processed data into the analysis module; S406: Detect and analyze the collected data through the analysis module, and identify abnormal data of the production line according to the data characteristics; S407: The control module classifies the identified problems, schedules and optimizes the production line according to different problem types, and formulates relevant scheduling strategies; S408: issuing scheduling instructions to relevant personnel and equipment on the production line; S409: Relevant personnel and equipment receive and execute the dispatch instruction.

[0027] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge scope of those skilled in the art without departing from the spirit of the present invention.

Claims

1. Medical device production line; its characteristics are: Included are: Material conveying module, used to efficiently and accurately transfer raw materials, semi-finished products or finished products between various stations on the production line, ensuring the continuity and timeliness of materials during processing, reducing waiting time and labor costs; Processing module, used to use various processing equipment to perform cutting, forming, assembling, welding, injection molding and other processing operations on materials, and transform them into semi-finished products or finished products that meet the requirements; The acquisition module is used to collect various data during the production process using multiple sets of sensor equipment, such as process parameters such as temperature, pressure, flow, speed, and quality information such as product size, weight, and appearance; The analysis module is used to analyze and process the collected data to evaluate the stability of the production process and the consistency of product quality, discover potential problems in the production process through analysis, and provide data support for optimizing production; The scheduling module is used to schedule and optimize the production line according to the analysis results of the analysis module, ensure the reasonable allocation and efficient execution of production tasks, and dynamically adjust the operation status of the production line according to the production plan and resource status to cope with various changes in the production process; The monitoring module is used to monitor the operating status of the entire production line in real time and conduct all-round monitoring of the production line to ensure the safety and order of the production site.

2. The medical device production line according to claim 1, characterized in that: The material conveying module includes conveyor belts, automatic guided vehicles (AGVs) and robot arms; conveyor belts, automatic guided vehicles (AGVs) and robot arms are used to transport materials from one workstation to another according to preset paths and speeds, and to efficiently and accurately transfer raw materials, semi-finished products or finished products between various stations on the production line.

3. The medical device production line according to claim 2, characterized in that: The processing module includes CNC machine tools, injection molding machines and welding machines; CNC machine tools are used for high-precision processing of complex parts. By programming tool paths and cutting parameters, they can perform various processing operations such as cutting, drilling, milling and turning on materials; injection molding machines are used to manufacture plastic parts. They inject molten plastic materials into pre-designed molds, and form the required plastic parts after cooling and solidification; welding machines are used to connect metal or non-metal parts to form a complete medical device structure.

4. The medical device production line according to claim 3, characterized in that: The acquisition module includes a parameter detection unit and a sensor device; the sensor device includes a temperature sensor, a pressure sensor, a flow sensor, a speed sensor, a weighing sensor and a visual detection unit. The sensor device is used to collect relevant data in the production process when the production line is running; the parameter detection unit is used to collect relevant parameters of the production equipment in the production line during operation.

5. The medical device production line according to claim 4, characterized in that: When the acquisition module is working, it includes the following steps: S101: Install sensors at key locations of the production line according to production line requirements to ensure accurate capture of various parameters during the production process; S102: Configuring the hardware and software of the acquisition module, including setting the frequency, accuracy, and communication protocol of data acquisition; S103: Preliminary processing of the data collected by the sensor, data format conversion and filtering to reduce noise and interference; S104: Clean and organize the collected data to remove invalid data and outliers; S105: Perform data conversion and normalization processing to standardize the data.

6. The medical device production line according to claim 5, characterized in that: The analysis module uses descriptive statistics and inferential statistics to analyze the data collected by the acquisition module. It uses indicators such as skewness coefficient and kurtosis coefficient to describe the shape of the data distribution, infers the overall parameters based on the sample data, and performs variance analysis by analyzing the influence of different factors on the dependent variables. It establishes a regression model, analyzes the relationship between variables and performs prediction and control, and automatically identifies data anomalies in the production process by building a model.

7. The medical device production line according to claim 6, characterized in that: When the analysis module is working, it includes the following steps: S201: Calculate basic statistics for the standardized data; calculate the average value of all data to reflect the central position of the data; calculate the median, mode, standard deviation and variance; S202: Draw a frequency distribution diagram of the data and analyze the form of data distribution; S203: An indicator to measure the direction and degree of skewness of data distribution. A positive value indicates right skewness and a negative value indicates left skewness. S204: An indicator that measures the sharpness or flatness of the data distribution. Compared with the normal distribution, a value greater than 1 indicates sharpness, and a value less than 1 indicates flatness; S205: Use sample statistics to directly estimate population parameters; based on sample data, construct intervals that contain a certain confidence level for the population parameters; S206: Identify and test whether one or more independent variables have a significant effect on the dependent variable; S207: Calculate the variance between groups and the variance within groups, and use the F statistic to determine whether the difference is significant; S208: Establish a linear relationship model between independent variables and dependent variables, evaluate the model goodness of fit R² and variable significance; use regression models for prediction and control, and adjust independent variables to optimize dependent variables; S209: Analyze data trends, patterns and outliers to extract key information; S210: extracting feature variables for anomaly detection; S211: Using the Z-score statistical method, set a threshold to identify outliers; S212: Automatically learn data distribution and identify outliers through the One-Class SVM model; train the model and adjust parameters to optimize the detection effect; S213: inputting data into the model to identify outliers; S214: Outputting a list of abnormal values ​​and their characteristic descriptions; S215: Select an X-bar control chart based on the data type and analysis purpose; collect process data and calculate control limits, plot data points on the control chart, and mark outliers; S216: Monitor the production process in real time to see if the data points are within the control limits; S217: When a data point exceeds the control limit, it is considered abnormal and the cause of the abnormality is analyzed; S218: Output the abnormality analysis result.

8. The medical device production line according to claim 7, characterized in that: The scheduling module classifies the identified problems based on the analysis results of the analysis module, schedules and optimizes the production line according to different problem types, prioritizes the problems, allocates resources and formulates scheduling plans, and issues scheduling instructions to relevant personnel and equipment on the production line to ensure the smooth implementation of the scheduling plan.

9. The medical device production line according to claim 8, characterized in that: When the scheduling module is working, it includes the following steps: S301: Collect data from the analysis module, including abnormal information, equipment status, and material supply status during the production process; S302: Identify problems and classify the identified problems according to types; S303: Prioritize production tasks according to the urgency and importance of the issues; S304: Allocate resources such as equipment, manpower and materials according to production plan and resource status; S305: Develop a detailed scheduling plan based on problem classification and priority sorting; S306: Monitor the operation status and various production indicators of the production line in real time through the MES system or other monitoring tools; S307: According to the real-time monitoring results, the scheduling plan is dynamically adjusted to cope with emergencies in the production process; S308: issuing the adjusted scheduling instructions to relevant personnel and equipment on the production line; S309: Evaluate the execution effect of the scheduling plan, feed back the evaluation results to the analysis module and other relevant departments, and continuously optimize and improve the scheduling module based on the evaluation results and feedback information.

10. A method for producing a medical device, characterized in that: The following steps are involved: S401: The production materials are transported to the processing module through the material transport module; the production operation of the production line is safely monitored through the monitoring module; S402: Perform different types of processing on the production materials through the processing modules, and transfer the workpieces between different production equipment in the processing modules by conveying the materials; S403: The data collection module collects various data on the production line during material transmission and production processing through sensor equipment and parameter detection equipment, and collects data related to medical machinery production; S404: preprocessing the collected data and standardizing the data; S405: input the pre-processed data into the analysis module; S406: Detect and analyze the collected data through the analysis module, and identify abnormal data of the production line according to the data characteristics; S407: The control module classifies the identified problems, schedules and optimizes the production line according to different problem types, and formulates relevant scheduling strategies; S408: issuing scheduling instructions to relevant personnel and equipment on the production line; S409: Relevant personnel and equipment receive and execute the dispatch instruction.

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