Computer control system for industrial automatic production

Abstract

The invention discloses a computer control system for industrial automatic production, which relates to the technical field of industrial automation and comprises a data acquisition module for acquiring production data on a production line in real time and transmitting the data to a production data analysis module; the production data analysis module is used for establishing a production plan analysis model according to the collected data; the production scheduling module predicts the production plan by using a time sequence analysis method based on the output of the production plan analysis model, adjusts and optimizes the production plan based on the prediction result of the production plan, and generates a control signal; and the equipment control module is used for adjusting the working state of each piece of production equipment according to the control signal generated by the production scheduling module. Through deep integration and optimization of the modules, the automation level of the production line is improved, the production process is more flexible and efficient, changes of production requirements can be responded in real time, and the production efficiency and the resource utilization rate of enterprises are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial automation, and in particular to a computer control system for industrial automation production. Background Art

[0002] With the continuous advancement of industrialization, the manufacturing industry is facing more and more challenges, such as the complexity of production plans, fluctuations in equipment load, and the improvement of production line efficiency. In order to improve production efficiency and optimize resource allocation, traditional production scheduling methods have been unable to meet the needs of modern industrial production. Computer control systems for industrial automated production have come into being. By integrating a variety of modern technologies, especially data acquisition and processing, intelligent scheduling and equipment control, it can achieve real-time monitoring and precise control of the production process.

[0003] At present, many industrial enterprises rely on manual labor and simple computer programs to carry out production scheduling, but these methods have great limitations. Manual scheduling has problems such as slow response, incomplete information, and inability to cope with emergencies; while traditional computer scheduling systems lack flexibility and intelligence, and have difficulty handling complex production needs and changing production environments. Therefore, there is an urgent need for a more intelligent, real-time production scheduling and equipment control system to cope with increasingly complex production needs and efficient resource scheduling. Summary of the Invention

[0004] In order to solve the above technical problems, a computer control system for industrial automated production is provided. This technical solution solves the above problems.

[0005] In order to achieve the above objects, the technical solution adopted by the present invention is:

[0006] A computer control system for industrial automated production, comprising:

[0007] Data acquisition module, production data analysis module, production scheduling module, equipment control module;

[0008] The data acquisition module is used to collect production data on the production line in real time and transmit the data to the production data analysis module;

[0009] The production data analysis module is electrically connected to the data acquisition module, and the production data analysis module is used to establish a production plan analysis model based on the collected data;

[0010] The production scheduling module is electrically connected to the production data analysis module. The production scheduling module is used to predict the production plan based on the output of the production plan analysis model using a time series analysis method, adjust and optimize the production plan based on the predicted results of the production plan, and generate a control signal;

[0011] The equipment control module is electrically connected to the production scheduling module, and the equipment control module is used to adjust the working state of each production equipment according to the control signal generated by the production scheduling module.

[0012] Preferably, the production data analysis module specifically includes:

[0013] Feature extraction unit: Extracts features from the collected data based on feature engineering methods, including production speed, equipment status, raw material usage, and production line downtime;

[0014] Production planning modeling unit: Based on feature data, use regression analysis equations to establish a production planning analysis model. Based on the production planning analysis model, use historical data for analysis to predict future production demand, production bottlenecks, and production equipment loads. Feedback the analysis and prediction results to the production scheduling module, and output the predicted production plan and production scheduling adjustment suggestions.

[0015] Preferably, the step of establishing a production plan analysis model based on the characteristic data using a regression analysis equation specifically includes:

[0016] Based on the extracted feature data, a linear regression model is selected to establish a production planning analysis model, and the production demand, bottlenecks and equipment load are predicted through the regression equation;

[0017] Among them, the production planning analysis model formula is:

[0018]

[0019] Where, is the output of the forecast, including production demand, production bottlenecks and equipment load, are features extracted from the collected data, is the intercept of the model, is the regression coefficient of the feature, which indicates the degree of influence of the feature on the prediction result. is the error term, which represents the difference between the predicted value and the actual value.

[0020] Preferably, the production scheduling module specifically includes:

[0021] Use time series analysis to forecast production plans. The production scheduling module applies time series analysis to predict future production demand and equipment load based on the output of the production plan analysis model.

[0022] Based on the output of regression analysis models and time series analysis, the production scheduling module summarizes the predicted production demand, bottlenecks and equipment load, and combines historical data to predict future production plans;

[0023] Based on the production plan forecast results, the production scheduling module adjusts and optimizes the production plan. If it predicts that the equipment load is higher than the threshold, measures to reduce the equipment load are taken. If there is a shortage of raw materials, suggestions for adjusting the production plan are made.

[0024] Preferably, the production plan is predicted using a time series analysis method, and the production scheduling module applies the time series analysis to predict future production demand and equipment load based on the output of the production plan analysis model, specifically including:

[0025] Through time series analysis, historical data is used for model training to obtain the parameters of the time series model;

[0026] Generate production demand, equipment load, and production bottleneck values ​​for a period of time in the future based on the model prediction results;

[0027] Combined with the forecast data output by the production planning analysis model, the time series analysis method is used to make long-term forecasts, and based on the long-term forecasts, the trend of the production scheduling plan is obtained.

[0028] Preferably, the production scheduling module summarizes the predicted production demand, bottlenecks and equipment load based on the output of the regression analysis model and time series analysis, and combines historical data to predict future production plans, specifically including:

[0029] The production demand, bottleneck and equipment load output by the regression analysis model are used as input data for time series analysis;

[0030] Use historical production data as training data for time series analysis to further calibrate and optimize the forecasting model;

[0031] Based on the combined output of regression analysis model and time series analysis, the forecast results of future production plans are generated and provide a decision basis for production scheduling.

[0032] Preferably, the step of using the production demand, bottleneck and equipment load output by the regression analysis model as input data for time series analysis specifically includes:

[0033] Among them, the time series analysis formula is:

[0034]

[0035] Where, Indicates time The target variable at time t, When all independent variables are zero, The baseline value, is the coefficient before each independent variable, In time Production needs at all times, For in time The bottleneck of time, In time Equipment load at the moment, is the error term;

[0036] The combined output of analytical models and time series analysis generates forecast results for future production plans and provides a basis for decision-making in production scheduling.

[0037] Preferably, the production scheduling module adjusts and optimizes the production plan according to the production plan prediction result, specifically including:

[0038] Based on the production plan forecast results, the production scheduling module dynamically adjusts the resource allocation of each production stage to ensure the smooth execution of the production plan;

[0039] If it is predicted that the equipment load is too high and the raw materials are insufficient, the production scheduling module optimizes the production plan by adjusting the production sequence and adjusting the production volume;

[0040] Optimize production plans in real time and automatically adjust production scheduling based on equipment conditions and raw material supply factors.

[0041] Preferably, the device control module specifically includes:

[0042] Control signal receiving unit: used to receive control signals output from the production scheduling module, the control signals including production scheduling, equipment working status and production task information;

[0043] Equipment status monitoring unit: used to monitor the operating status of each equipment on the production line in real time and collect equipment working status information, including temperature, pressure and operating speed parameters;

[0044] Control instruction generation unit: generates control instructions based on the received control signal and equipment status information. The control instructions include starting and stopping the control equipment, adjusting working parameters, and switching production modes;

[0045] Execution unit: transmits control instructions to the equipment and performs control operations on the equipment, such as adjusting the working speed of the equipment, changing production process parameters, and starting and stopping the equipment.

[0046] Preferably, the device control module further includes:

[0047] Abnormal processing unit: Detects abnormal conditions of equipment during the production process, including failures and abnormal shutdowns, and generates alarm signals in a timely manner according to the set fault diagnosis rules, and takes corresponding emergency treatment measures;

[0048] Feedback adjustment unit: monitors equipment performance and production results in real time during equipment operation, and optimizes and adjusts equipment control parameters based on feedback data;

[0049] Remote control interface: supports remote monitoring and control of equipment operation, allowing managers to remotely view equipment status, troubleshoot problems, and modify control instructions.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] The present invention proposes to deeply integrate and optimize modules such as data acquisition, data analysis, production scheduling, and equipment control, which not only improves the automation level of the production line, but also makes the production process more flexible and efficient, and can respond to changes in production demand in real time, thereby maximizing the company's production efficiency and resource utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a system framework diagram of the present invention;

[0053] Figure 2 This is the internal system framework diagram of the production data analysis module in the present invention. DETAILED DESCRIPTION

[0054] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0055] Reference Figure 1 As shown, a computer control system for industrial automated production includes:

[0056] Data acquisition module, production data analysis module, production scheduling module, equipment control module;

[0057] The data acquisition module is used to collect production data on the production line in real time and transmit the data to the production data analysis module;

[0058] The production data analysis module is electrically connected to the data acquisition module, and the production data analysis module is used to establish a production plan analysis model based on the collected data;

[0059] The production scheduling module is electrically connected to the production data analysis module. The production scheduling module is used to predict the production plan based on the output of the production plan analysis model using a time series analysis method, adjust and optimize the production plan based on the predicted results of the production plan, and generate a control signal.

[0060] The equipment control module is electrically connected to the production scheduling module, and the equipment control module is used to adjust the working status of each production equipment according to the control signal generated by the production scheduling module.

[0061] Reference Figure 2 As shown, the production data analysis module specifically includes:

[0062] Feature extraction unit: Extracts features from the collected data based on feature engineering methods, including production speed, equipment status, raw material usage, and production line downtime;

[0063] Production planning modeling unit: Based on feature data, a regression analysis equation is used to establish a production planning analysis model. Based on the production planning analysis model, historical data is used for analysis to predict future production demand, production bottlenecks, and production equipment load. The analysis and prediction results are fed back to the production scheduling module, which outputs the predicted production plan and production scheduling adjustment suggestions.

[0064] The production data analysis module extracts key data such as production speed, equipment status, and raw material usage through feature engineering, and combines it with regression analysis to establish a production planning analysis model. This innovation effectively combines historical data with actual production conditions, providing a basis for predictive analysis for production scheduling and improving prediction accuracy.

[0065] Based on the characteristic data, the production planning analysis model is established using the regression analysis equation, including:

[0066] Based on the extracted feature data, a linear regression model is selected to establish a production planning analysis model, and the production demand, bottlenecks and equipment load are predicted through the regression equation;

[0067] Among them, the production planning analysis model formula is:

[0068]

[0069] Where, is the output of the forecast, including production demand, production bottlenecks and equipment load, are features extracted from the collected data, is the intercept of the model, is the regression coefficient of the feature, which indicates the degree of influence of the feature on the prediction result. is the error term, which represents the difference between the predicted value and the actual value;

[0070] By combining regression analysis and time series analysis, the system can comprehensively predict future production demand, production bottlenecks and equipment load. This multi-dimensional data forecasting method is more accurate and stable than a single forecasting method, and can better cope with complex production environments and demand changes.

[0071] The production scheduling module specifically includes:

[0072] Use time series analysis to forecast production plans. The production scheduling module applies time series analysis to predict future production demand and equipment load based on the output of the production plan analysis model.

[0073] Based on the output of regression analysis models and time series analysis, the production scheduling module summarizes the predicted production demand, bottlenecks and equipment load, and combines historical data to predict future production plans;

[0074] Based on the production plan forecast results, the production scheduling module adjusts and optimizes the production plan. If it predicts that the equipment load exceeds the threshold, it will take measures to reduce the equipment load. If there is a shortage of raw materials, it will make suggestions for adjusting the production plan.

[0075] The production scheduling module dynamically adjusts the production plan based on the forecast results, including optimization of resource allocation, production sequence and production volume. This innovative adjustment can minimize resource waste and equipment overload, ensuring the smooth execution of the production plan.

[0076] Use time series analysis to forecast production plans. The production scheduling module applies time series analysis to forecast future production demand and equipment load based on the output of the production plan analysis model. Specifically, it includes:

[0077] Through time series analysis, historical data is used for model training to obtain the parameters of the time series model;

[0078] Generate production demand, equipment load, and production bottleneck values ​​for a period of time in the future based on the model prediction results;

[0079] Combined with the forecast data output by the production planning analysis model, long-term forecasting is carried out using time series analysis methods. Based on the long-term forecast, the trend of the production scheduling plan is obtained;

[0080] The equipment control module is not only responsible for receiving control signals and executing equipment operations, but also includes functions such as equipment status monitoring, exception handling and remote control. By integrating multiple functions, the equipment control module can perform control operations more promptly and accurately to ensure the smooth operation of the production line, and provide remote monitoring functions to facilitate real-time intervention and adjustments by managers.

[0081] Based on the output of regression analysis models and time series analysis, the production scheduling module summarizes the predicted production demand, bottlenecks, and equipment load, and combines historical data to predict future production plans. Specifically, it includes:

[0082] The production demand, bottleneck and equipment load output by the regression analysis model are used as input data for time series analysis;

[0083] Use historical production data as training data for time series analysis to further calibrate and optimize the forecasting model;

[0084] Based on the combined output of regression analysis model and time series analysis, the forecast results of future production plans are generated and provide a decision basis for production scheduling.

[0085] The production demand, bottleneck, and equipment load output by the regression analysis model are used as input data for time series analysis, specifically including:

[0086] Among them, the time series analysis formula is:

[0087]

[0088] Where, Indicates time The target variable at time t, When all independent variables are zero, The baseline value, is the coefficient before each independent variable, In time Production needs at all times, For in time The bottleneck of time, In time Equipment load at the moment, is the error term;

[0089] The combined output of analytical models and time series analysis generates forecast results for future production plans and provides a basis for decision-making in production scheduling.

[0090] According to the production plan forecast results, the production scheduling module adjusts and optimizes the production plan, including:

[0091] Based on the production plan forecast results, the production scheduling module dynamically adjusts the resource allocation of each production stage to ensure the smooth execution of the production plan;

[0092] If it is predicted that the equipment load is too high and the raw materials are insufficient, the production scheduling module optimizes the production plan by adjusting the production sequence and adjusting the production volume;

[0093] Optimize production plans in real time and automatically adjust production scheduling based on equipment conditions and raw material supply factors.

[0094] The device control module specifically includes:

[0095] Control signal receiving unit: used to receive control signals output from the production scheduling module, including production scheduling, equipment working status and production task information;

[0096] Equipment status monitoring unit: used to monitor the operating status of each equipment on the production line in real time and collect equipment working status information, including temperature, pressure and operating speed parameters;

[0097] Control instruction generation unit: Generates control instructions based on the received control signals and equipment status information. Control instructions include starting and stopping the control equipment, adjusting working parameters, and switching production modes;

[0098] Execution unit: transmits control instructions to the equipment and performs control operations on the equipment. Control operations include adjusting the equipment's operating speed, changing production process parameters, and starting and stopping the equipment.

[0099] The device control module also includes:

[0100] Abnormal processing unit: Detects abnormal conditions of equipment during the production process, including failures and abnormal shutdowns, and generates alarm signals in a timely manner according to the set fault diagnosis rules, and takes corresponding emergency treatment measures;

[0101] Feedback adjustment unit: monitors equipment performance and production results in real time during equipment operation, and optimizes and adjusts equipment control parameters based on feedback data;

[0102] Remote control interface: supports remote monitoring and control of equipment operation, allowing managers to remotely view equipment status, troubleshoot problems, and modify control instructions.

[0103] In summary, the advantages of the present invention are:

[0104] The system acquires production data in real time through the data acquisition module and, in combination with the production data analysis module, establishes a production planning analysis model through feature extraction and regression analysis. This model can more accurately predict production demand, production bottlenecks, and equipment load, thereby achieving precise production scheduling and plan adjustments.

[0105] The production scheduling module, based on regression analysis models and time series analysis methods, can optimize and adjust production plans. When it predicts peak equipment load or raw material shortages, it can take timely measures to adjust production plans to ensure smooth operation of the production line. In addition, it dynamically adjusts resource allocation at each production stage to avoid resource waste and improve production efficiency.

[0106] The equipment control module can monitor the status of the equipment in real time. Based on the control signal and equipment status information, it generates control instructions and performs optimized control of the equipment, automatically adjusting the equipment working status. The abnormality handling unit and feedback adjustment unit can detect equipment abnormalities in a timely manner and take measures to ensure the continuity and stability of production.

[0107] The system supports remote monitoring and control of equipment operation, allowing managers to intervene and adjust the production line anytime and anywhere. This not only improves management flexibility, but also enables timely handling of emergencies and ensures the normal operation of the production line.

[0108] The system combines regression analysis models with time series analysis, and uses intelligent algorithms to make long-term and short-term predictions of multiple indicators such as production demand, bottlenecks, and equipment load. Using these prediction results, the production scheduling module can automatically adjust the production sequence, production volume, etc. according to the changing trends of the production plan, thereby achieving the purpose of optimizing production scheduling and greatly improving production efficiency and production flexibility.

[0109] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A computer control system for industrial automated production, characterized in that: include: Data acquisition module, production data analysis module, production scheduling module, equipment control module; The data acquisition module is used to collect production data on the production line in real time and transmit the data to the production data analysis module; The production data analysis module is electrically connected to the data acquisition module, and the production data analysis module is used to establish a production plan analysis model based on the collected data; The production scheduling module is electrically connected to the production data analysis module. The production scheduling module is used to predict the production plan based on the output of the production plan analysis model using a time series analysis method, adjust and optimize the production plan based on the predicted results of the production plan, and generate a control signal; The equipment control module is electrically connected to the production scheduling module, and the equipment control module is used to adjust the working state of each production equipment according to the control signal generated by the production scheduling module.

2. The computer control system for industrial automated production according to claim 1, characterized in that: The production data analysis module specifically includes: Feature extraction unit: Extracts features from the collected data based on feature engineering methods, including production speed, equipment status, raw material usage, and production line downtime; Production planning modeling unit: Based on feature data, use regression analysis equations to establish a production planning analysis model. Based on the production planning analysis model, use historical data for analysis to predict future production demand, production bottlenecks, and production equipment loads. Feedback the analysis and prediction results to the production scheduling module, and output the predicted production plan and production scheduling adjustment suggestions.

3. The computer control system for industrial automated production according to claim 2, characterized in that: The method of establishing a production plan analysis model based on the feature data using a regression analysis equation specifically includes: Based on the extracted feature data, a linear regression model is selected to establish a production planning analysis model, and the production demand, bottlenecks and equipment load are predicted through the regression equation; Among them, the production planning analysis model formula is: Where, is the output of the forecast, including production demand, production bottlenecks and equipment load, are features extracted from the collected data, is the intercept of the model, is the regression coefficient of the feature, which indicates the degree of influence of the feature on the prediction result. is the error term, which represents the difference between the predicted value and the actual value.

4. The computer control system for industrial automated production according to claim 3, characterized in that: The production scheduling module specifically includes: Use time series analysis to forecast production plans. The production scheduling module applies time series analysis to predict future production demand and equipment load based on the output of the production plan analysis model. Based on the output of regression analysis models and time series analysis, the production scheduling module summarizes the predicted production demand, bottlenecks and equipment load, and combines historical data to predict future production plans; Based on the production plan forecast results, the production scheduling module adjusts and optimizes the production plan. If it predicts that the equipment load is higher than the threshold, measures to reduce the equipment load are taken. If there is a shortage of raw materials, suggestions for adjusting the production plan are made.

5. The computer control system for industrial automated production according to claim 4, characterized in that: The production plan is predicted using a time series analysis method. The production scheduling module applies time series analysis to predict future production demand and equipment load based on the output of the production plan analysis model. Specifically, the following steps are involved: Through time series analysis, historical data is used for model training to obtain the parameters of the time series model; Generate production demand, equipment load, and production bottleneck values ​​for a period of time in the future based on the model prediction results; Combined with the forecast data output by the production planning analysis model, the time series analysis method is used to make long-term forecasts, and based on the long-term forecasts, the trend of the production scheduling plan is obtained.

6. The computer control system for industrial automated production according to claim 4, characterized in that: Based on the output of the regression analysis model and time series analysis, the production scheduling module summarizes the predicted production demand, bottlenecks, and equipment load, and combines historical data to predict future production plans. Specifically, the following are included: The production demand, bottleneck and equipment load output by the regression analysis model are used as input data for time series analysis; Use historical production data as training data for time series analysis to further calibrate and optimize the forecasting model; Based on the combined output of regression analysis model and time series analysis, the forecast results of future production plans are generated and provide a decision basis for production scheduling.

7. The computer control system for industrial automated production according to claim 6, characterized in that: The production demand, bottleneck and equipment load output by the regression analysis model are used as input data for time series analysis. include: Among them, the time series analysis formula is: Where, Indicates time The target variable at time t, When all independent variables are zero, The baseline value, is the coefficient before each independent variable, In time Production needs at all times, For in time The bottleneck of time, In time Equipment load at the moment, is the error term; The combined output of analytical models and time series analysis generates forecast results for future production plans and provides a basis for decision-making in production scheduling.

8. The computer control system for industrial automated production according to claim 4, characterized in that: The production scheduling module adjusts and optimizes the production plan according to the production plan prediction results, specifically including: Based on the production plan forecast results, the production scheduling module dynamically adjusts the resource allocation of each production stage to ensure the smooth execution of the production plan; If it is predicted that the equipment load is too high and the raw materials are insufficient, the production scheduling module optimizes the production plan by adjusting the production sequence and adjusting the production volume; Optimize production plans in real time and automatically adjust production scheduling based on equipment conditions and raw material supply factors.

9. The computer control system for industrial automated production according to claim 1, characterized in that: The device control module specifically includes: Control signal receiving unit: used to receive control signals output from the production scheduling module, the control signals including production scheduling, equipment working status and production task information; Equipment status monitoring unit: used to monitor the operating status of each equipment on the production line in real time and collect equipment working status information, including temperature, pressure and operating speed parameters; Control instruction generation unit: generates control instructions based on the received control signal and equipment status information. The control instructions include starting and stopping the control equipment, adjusting working parameters, and switching production modes; Execution unit: transmits control instructions to the equipment and performs control operations on the equipment, such as adjusting the working speed of the equipment, changing production process parameters, and starting and stopping the equipment.

10. The computer control system for industrial automated production according to claim 9, characterized in that: The device control module also includes: Abnormal processing unit: Detects abnormal conditions of equipment during the production process, including failures and abnormal shutdowns, and generates alarm signals in a timely manner according to the set fault diagnosis rules, and takes corresponding emergency treatment measures; Feedback adjustment unit: monitors equipment performance and production results in real time during equipment operation, and optimizes and adjusts equipment control parameters based on feedback data; Remote control interface: supports remote monitoring and control of equipment operation, allowing managers to remotely view equipment status, troubleshoot problems, and modify control instructions.

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