CN118822175A - A dynamic integrated advanced scheduling method for steelmaking and rolling - Google Patents

Abstract

A dynamic integrated advanced scheduling method for steelmaking and steel rolling belongs to the technical field of steel smelting methods. Its technical scheme is: various data in the steelmaking and steel rolling production process are collected in real time through a data acquisition module; the collected data are processed and analyzed through artificial intelligence algorithms and big data technology to obtain relevant information on production plans and scheduling; corresponding production plans are formulated according to the processed data and market demand; according to the production plan and actual conditions, the production schedule is optimized in real time through a scheduling optimization algorithm to ensure the stability and efficiency of the production process; the abnormal situation in the production process is monitored in real time through an abnormal handling module, and timely adjusted and optimized; the production plan and schedule are displayed to users in a graphical manner through a visualization module. The present invention can realize the visualization of the steelmaking and steel rolling production process, facilitate users to monitor and manage the production process, and improve production management efficiency and production process transparency.

Description

A dynamic integrated advanced scheduling method for steelmaking and rolling

Technical Field

The invention relates to a dynamic integrated advanced scheduling method for steelmaking and steel rolling, and belongs to the technical field of steel smelting methods.

Background Art

In traditional steel production, steelmaking and steel rolling are separate processes. The lack of close connection between steelmaking and steel rolling can lead to problems such as low production efficiency, waste of resources, and unstable product quality. Therefore, some steel companies have begun to explore advanced scheduling technology for dynamic integration of steelmaking and steel rolling, aiming to effectively connect and optimize the two links of steelmaking and steel rolling.

The core of this technology is to integrate the design and management of the steelmaking and steel rolling production processes through advanced production planning and scheduling systems. Specifically, this system can comprehensively analyze and optimize the steelmaking and steel rolling production processes based on factors such as order requirements, equipment conditions, and human resources, thereby formulating the most reasonable production plan and scheduling scheme. At the same time, this system can also make dynamic adjustments based on real-time data during the production process to ensure the stability and efficiency of the production process.

The application of dynamic integrated advanced scheduling technology for steelmaking and rolling can improve the production efficiency and product quality of steel enterprises and reduce production costs and resource consumption. With the development of artificial intelligence and big data technology, this technology will continue to be optimized and improved, providing stronger technical support for the sustainable development of the steel industry.

Summary of the invention

The purpose of the present invention is to provide a dynamic integrated advanced scheduling method for steelmaking and steel rolling. By adopting advanced scheduling based on advanced algorithms and artificial intelligence, it can respond to changes in the production environment in real time and adjust production plans and schedules in time; it can integrate various production information to provide a comprehensive production plan and scheduling solution; it can predict and optimize based on historical data and real-time information, so as to provide more accurate and optimized production plans and schedules; it provides a wealth of visualization tools, which can display production plans and schedules in a graphical way, facilitate user understanding and operation, improve production efficiency and reduce production costs, and effectively solve the above-mentioned problems existing in the background technology.

The technical solution of the present invention is: a dynamic integrated advanced scheduling method for steelmaking and steel rolling, comprising the following steps:

Step 1: Data collection: The data collection module collects various data in the steelmaking and rolling production process in real time, including order information, equipment status and production progress;

Step 2: Data processing: Process and analyze the collected data through artificial intelligence algorithms and big data technology to obtain relevant information on production planning and scheduling;

Step 3: Production plan formulation: formulate corresponding production plan according to the processed data and market demand;

Step 4: Production schedule optimization: According to the production plan and actual situation, the production schedule is optimized in real time through the scheduling optimization algorithm to ensure the stability and efficiency of the production process;

Step 5. Exception handling: Use the exception handling module to monitor abnormal situations in the production process in real time, and make timely adjustments and optimizations;

Step 6. Visual display: The production plan and schedule are displayed to users in a graphical manner through the visualization module to facilitate user understanding and operation.

The step 1 includes the following steps:

1.1. Determine the scope of data collection: clarify the type and source of data to be collected, including order information, equipment status and production progress;

1.2. Select data collection method: According to the data source and type, select the appropriate data collection method, including sensors, data interfaces and databases;

1.3. Develop data collection program: According to the selected data collection method and data collection scope, develop the corresponding data collection program to realize real-time data collection and transmission;

1.4 Data preprocessing: clean, convert and format the collected data to ensure data quality and consistency;

1.5 Data storage: The processed data shall be stored in the corresponding database or data storage medium for subsequent data analysis and optimization.

The step 2 includes the following steps:

2.1. Data cleaning: remove invalid, erroneous and duplicate data to ensure data quality and accuracy;

2.2. Data conversion: convert data from different sources and formats into a unified data format to facilitate subsequent data analysis and processing;

2.3. Data standardization: converting data into a unified scale to facilitate data comparison and analysis;

2.4. Data mining: Through data mining algorithms, a large amount of data is mined and analyzed to discover the patterns and trends in the data, providing a basis for subsequent production planning and scheduling optimization;

2.5. Data visualization: Display the processed data in the form of charts, images, etc. to facilitate intuitive understanding and analysis of the data.

The step three includes the following steps:

3.1. Determine production goals: clarify the goals and requirements of the production plan, including production volume, product quality and production costs;

3.2. Analyze the production process: Analyze the production process of steelmaking and steel rolling, and clarify the process, equipment, quality, quantity and progress elements of each production link;

3.3. Develop preliminary production plan: Based on the analysis and forecast data, develop a preliminary production plan, including the time, equipment, personnel and material arrangements for each process;

3.4. Optimize production plan: Based on the preliminary production plan, optimize it through scheduling optimization algorithm to obtain a more reasonable and efficient production plan;

3.5. Approval of production plan: Submit the optimized production plan to relevant departments for approval to ensure the rationality and feasibility of the production plan;

3.6. Execute production plan: Carry out production according to the approved production plan, monitor the data in the production process in real time, and adjust the production plan in time.

The step 4 includes the following steps:

4.1. Determine the optimization goal: clarify the goals and requirements of production scheduling optimization, including production efficiency, production cost and production cycle;

4.2. Analyze production data: Through data collection and analysis, obtain various data in the production process, including equipment status, production progress and product quality;

4.3 Select optimization algorithm: According to the optimization goal and the characteristics of production data, select the appropriate scheduling optimization algorithm, including genetic algorithm, simulated annealing algorithm and particle swarm optimization algorithm;

4.4. Formulate optimization plan: According to the preliminary production plan and optimization algorithm, formulate a specific optimization plan, including process sequence, time arrangement and resource allocation;

4.5 Implement optimization plan: optimize the production schedule according to the formulated optimization plan to obtain a more reasonable and efficient production schedule;

4.6 Evaluate the optimization effect: Evaluate the optimized production schedule, compare the production efficiency and production cost before and after optimization, and judge the optimization effect;

4.7 Adjust the optimization plan: Adjust the optimization plan according to the evaluation results to continuously improve the optimization effect.

The step five includes the following steps:

5.1. Monitoring the production process: Real-time monitoring of various data in the steelmaking and rolling production process, including order information, equipment status and production progress, through the data acquisition module;

5.2. Identify abnormal situations: Use data analysis algorithms to identify and analyze monitored data and discover abnormal situations, including equipment failures and order changes;

5.3 Determine the abnormality type: classify and diagnose the identified abnormalities and determine the specific cause of the abnormality type;

5.4 Generate adjustment plan: Generate corresponding adjustment plan according to the type and severity of abnormal situation, including suspension of production, adjustment of production plan and maintenance of equipment;

5.5. Approval of adjustment plan: Submit the adjustment plan to relevant departments for approval to ensure the rationality and feasibility of the adjustment plan;

5.6 Implementation of adjustment plan: Adjust production plan and schedule according to the approved adjustment plan to ensure the stability and safety of the production process.

5.7 Record abnormal information: Record and archive abnormal situations as a reference for subsequent optimization and improvement.

The step six includes the following steps:

6.1. Design visual interface: Design corresponding visual interface according to user needs and operating habits, including charts, images and tables;

6.2. Collect production data: Obtain various data in the steelmaking and rolling production process through the data collection module, including order information, equipment status and production progress;

6.3. Processing production data: Process and analyze the collected production data, extract key information and indicators, and provide data support for visualization;

6.4. Realize dynamic display: Display the processed data in a dynamic way, including real-time updating of charts and dynamic display of production process;

6.5 Provide interactive functions: Provide interactive functions in the visual interface, including click operation and drag and drop, to facilitate users to monitor and manage the production process;

6.6. Integrate other systems: Integrate the visualization system with other production management systems to achieve data sharing and interaction and improve production management efficiency.

The beneficial effects of the present invention are: by adopting advanced scheduling based on advanced algorithms and artificial intelligence, it can respond to changes in the production environment in real time and adjust production plans and schedules in a timely manner; it can integrate various production information to provide a comprehensive production planning and scheduling solution; it can predict and optimize based on historical data and real-time information, thereby providing more accurate and optimized production plans and schedules; it provides a wealth of visualization tools that can display production plans and schedules in a graphical way, which is convenient for users to understand and operate, improve production efficiency and reduce production costs.

DETAILED DESCRIPTION

In order to make the purpose, technical solutions and advantages of the invention implementation cases clearer, the technical solutions in the implementation cases of the present invention will be clearly and completely described below. Obviously, the implementation cases described are only a small part of the implementation cases of the present invention, rather than all the implementation cases. Based on the implementation cases in the present invention, all other implementation cases obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

A dynamic integrated advanced scheduling method for steelmaking and steel rolling comprises the following steps:

Step 1: Data collection: The data collection module collects various data in the steelmaking and rolling production process in real time, including order information, equipment status and production progress;

Step 2: Data processing: Process and analyze the collected data through artificial intelligence algorithms and big data technology to obtain relevant information on production planning and scheduling;

Step 3: Production plan formulation: formulate corresponding production plan according to the processed data and market demand;

Step 4: Production schedule optimization: According to the production plan and actual situation, the production schedule is optimized in real time through the scheduling optimization algorithm to ensure the stability and efficiency of the production process;

Step 5. Exception handling: Use the exception handling module to monitor abnormal situations in the production process in real time, and make timely adjustments and optimizations;

Step 6. Visual display: The production plan and schedule are displayed to users in a graphical manner through the visualization module to facilitate user understanding and operation.

The step 1 includes the following steps:

1.1. Determine the scope of data collection: clarify the type and source of data to be collected, including order information, equipment status and production progress;

1.2. Select data collection method: According to the data source and type, select the appropriate data collection method, including sensors, data interfaces and databases;

1.3. Develop data collection program: According to the selected data collection method and data collection scope, develop the corresponding data collection program to realize real-time data collection and transmission;

1.4 Data preprocessing: clean, convert and format the collected data to ensure data quality and consistency;

1.5 Data storage: The processed data shall be stored in the corresponding database or data storage medium for subsequent data analysis and optimization.

The step 2 includes the following steps:

2.1. Data cleaning: remove invalid, erroneous and duplicate data to ensure data quality and accuracy;

2.2. Data conversion: convert data from different sources and formats into a unified data format to facilitate subsequent data analysis and processing;

2.3. Data standardization: converting data into a unified scale to facilitate data comparison and analysis;

2.4. Data mining: Through data mining algorithms, a large amount of data is mined and analyzed to discover the patterns and trends in the data, providing a basis for subsequent production planning and scheduling optimization;

2.5. Data visualization: Display the processed data in the form of charts, images, etc. to facilitate intuitive understanding and analysis of the data.

The step three includes the following steps:

3.1. Determine production goals: clarify the goals and requirements of the production plan, including production volume, product quality and production costs;

3.2. Analyze the production process: Analyze the production process of steelmaking and steel rolling, and clarify the process, equipment, quality, quantity and progress elements of each production link;

3.3. Develop preliminary production plan: Based on the analysis and forecast data, develop a preliminary production plan, including the time, equipment, personnel and material arrangements for each process;

3.4. Optimize production plan: Based on the preliminary production plan, optimize it through scheduling optimization algorithm to obtain a more reasonable and efficient production plan;

3.5. Approval of production plan: Submit the optimized production plan to relevant departments for approval to ensure the rationality and feasibility of the production plan;

3.6. Execute production plan: Carry out production according to the approved production plan, monitor the data in the production process in real time, and adjust the production plan in time.

The step 4 includes the following steps:

4.1. Determine the optimization goal: clarify the goals and requirements of production scheduling optimization, including production efficiency, production cost and production cycle;

4.2. Analyze production data: Through data collection and analysis, obtain various data in the production process, including equipment status, production progress and product quality;

4.3 Select optimization algorithm: According to the optimization goal and the characteristics of production data, select the appropriate scheduling optimization algorithm, including genetic algorithm, simulated annealing algorithm and particle swarm optimization algorithm;

4.4. Formulate optimization plan: According to the preliminary production plan and optimization algorithm, formulate a specific optimization plan, including process sequence, time arrangement and resource allocation;

4.5 Implement optimization plan: optimize the production schedule according to the formulated optimization plan to obtain a more reasonable and efficient production schedule;

4.6 Evaluate the optimization effect: Evaluate the optimized production schedule, compare the production efficiency and production cost before and after optimization, and judge the optimization effect;

4.7 Adjust the optimization plan: Adjust the optimization plan according to the evaluation results to continuously improve the optimization effect.

The step five includes the following steps:

5.1. Monitoring the production process: Real-time monitoring of various data in the steelmaking and rolling production process, including order information, equipment status and production progress, through the data acquisition module;

5.2. Identify abnormal situations: Use data analysis algorithms to identify and analyze monitored data and discover abnormal situations, including equipment failures and order changes;

5.3 Determine the abnormality type: classify and diagnose the identified abnormalities and determine the specific cause of the abnormality type;

5.4 Generate adjustment plan: Generate corresponding adjustment plan according to the type and severity of abnormal situation, including suspension of production, adjustment of production plan and maintenance of equipment;

5.5. Approval of adjustment plan: Submit the adjustment plan to relevant departments for approval to ensure the rationality and feasibility of the adjustment plan;

5.6 Implementation of adjustment plan: Adjust production plan and schedule according to the approved adjustment plan to ensure the stability and safety of the production process.

5.7 Record abnormal information: Record and archive abnormal situations as a reference for subsequent optimization and improvement.

The step six includes the following steps:

6.1. Design visual interface: Design corresponding visual interface according to user needs and operating habits, including charts, images and tables;

6.2. Collect production data: Obtain various data in the steelmaking and rolling production process through the data collection module, including order information, equipment status and production progress;

6.3. Processing production data: Process and analyze the collected production data, extract key information and indicators, and provide data support for visualization;

6.4. Realize dynamic display: Display the processed data in a dynamic way, including real-time updating of charts and dynamic display of production process;

6.5 Provide interactive functions: Provide interactive functions in the visual interface, including click operation and drag and drop, to facilitate users to monitor and manage the production process;

6.6. Integrate other systems: Integrate the visualization system with other production management systems to achieve data sharing and interaction and improve production management efficiency.

Dynamic Integrated Advanced Scheduling Technology (DIAST) is an advanced scheduling technology based on advanced algorithms and artificial intelligence. This technology is widely used in the field of production planning and scheduling, aiming to improve production efficiency and reduce production costs.

DIAST technology has the following features:

1. Dynamicity: DIAST can respond to changes in the production environment in real time, such as equipment failures, order changes, etc., and adjust production plans and schedules in a timely manner.

2. Integration: DIAST can integrate various production information, including order information, inventory information, equipment information, etc., to provide a comprehensive production planning and scheduling solution.

3. Advanced: DIAST uses advanced algorithms and artificial intelligence technology, which can predict and optimize based on historical data and real-time information, thereby providing more accurate and optimized production planning and scheduling.

4. Visualization: DIAST provides a wealth of visualization tools that can display production plans and schedules in a graphical way, making it easier for users to understand and operate.

The present invention can realize the visual display of the steelmaking and steel rolling production process, facilitate users to monitor and manage the production process, and improve production management efficiency and production process transparency.

Claims (7)

1. A dynamic integrated advanced scheduling method for steelmaking and steel rolling, characterized by comprising the following steps: Step 1: Data collection: The data collection module collects various data in the steelmaking and rolling production process in real time, including order information, equipment status and production progress; Step 2: Data processing: Process and analyze the collected data through artificial intelligence algorithms and big data technology to obtain relevant information on production planning and scheduling; Step 3: Production plan formulation: formulate corresponding production plan according to the processed data and market demand; Step 4: Production schedule optimization: According to the production plan and actual situation, the production schedule is optimized in real time through the scheduling optimization algorithm to ensure the stability and efficiency of the production process; Step 5. Exception handling: Use the exception handling module to monitor abnormal situations in the production process in real time, and make timely adjustments and optimizations; Step 6. Visual display: The production plan and schedule are displayed to users in a graphical manner through the visualization module to facilitate user understanding and operation. 2. A dynamic integrated advanced scheduling method for steelmaking and steel rolling according to claim 1, characterized in that: said step 1 comprises the following steps: 1.1. Determine the scope of data collection: clarify the type and source of data to be collected, including order information, equipment status and production progress; 1.2. Select data collection method: According to the data source and type, select the appropriate data collection method, including sensors, data interfaces and databases; 1.3. Develop data collection program: According to the selected data collection method and data collection scope, develop the corresponding data collection program to realize real-time data collection and transmission; 1.4 Data preprocessing: clean, convert and format the collected data to ensure data quality and consistency; 1.5 Data storage: The processed data shall be stored in the corresponding database or data storage medium for subsequent data analysis and optimization. 3. The method for dynamic integrated high-level scheduling of steelmaking and steel rolling according to claim 1, characterized in that: said step 2 comprises the following steps: 2.1. Data cleaning: remove invalid, erroneous and duplicate data to ensure data quality and accuracy; 2.2. Data conversion: convert data from different sources and formats into a unified data format to facilitate subsequent data analysis and processing; 2.3. Data standardization: converting data into a unified scale to facilitate data comparison and analysis; 2.4. Data mining: Through data mining algorithms, a large amount of data is mined and analyzed to discover the patterns and trends in the data, providing a basis for subsequent production planning and scheduling optimization; 2.5. Data visualization: Display the processed data in the form of charts, images, etc. to facilitate intuitive understanding and analysis of the data. 4. The method for dynamic integrated high-level scheduling of steelmaking and steel rolling according to claim 1, characterized in that: said step 3 comprises the following steps: 3.1. Determine production goals: clarify the goals and requirements of the production plan, including production volume, product quality and production costs; 3.2. Analyze the production process: Analyze the production process of steelmaking and steel rolling, and clarify the process, equipment, quality, quantity and progress elements of each production link; 3.3. Develop preliminary production plan: Based on the analysis and forecast data, develop a preliminary production plan, including the time, equipment, personnel and material arrangements for each process; 3.4. Optimize production plan: Based on the preliminary production plan, optimize it through scheduling optimization algorithm to obtain a more reasonable and efficient production plan; 3.5. Approval of production plan: Submit the optimized production plan to relevant departments for approval to ensure the rationality and feasibility of the production plan; 3.6. Execute production plan: Carry out production according to the approved production plan, monitor the data in the production process in real time, and adjust the production plan in time. 5. The method for dynamic integrated high-level scheduling of steelmaking and steel rolling according to claim 1, characterized in that: said step 4 comprises the following steps: 4.1. Determine the optimization goal: clarify the goals and requirements of production scheduling optimization, including production efficiency, production cost and production cycle; 4.2. Analyze production data: Through data collection and analysis, obtain various data in the production process, including equipment status, production progress and product quality; 4.3 Select optimization algorithm: According to the optimization goal and the characteristics of production data, select the appropriate scheduling optimization algorithm, including genetic algorithm, simulated annealing algorithm and particle swarm optimization algorithm; 4.4. Formulate optimization plan: According to the preliminary production plan and optimization algorithm, formulate a specific optimization plan, including process sequence, time arrangement and resource allocation; 4.5. Implement the optimization plan: optimize the production schedule according to the formulated optimization plan to obtain a more reasonable and efficient production schedule; 4.6. Evaluate the optimization effect: Evaluate the optimized production schedule, compare the production efficiency and production cost before and after optimization, and judge the optimization effect; 4.7. Adjust the optimization plan: Adjust the optimization plan according to the evaluation results to continuously improve the optimization effect. 6. A dynamic integrated advanced scheduling method for steelmaking and steel rolling according to claim 1, characterized in that: said step 5 comprises the following steps: 5.1. Monitoring the production process: Real-time monitoring of various data in the steelmaking and rolling production process, including order information, equipment status and production progress, through the data acquisition module; 5.2. Identify abnormal situations: Use data analysis algorithms to identify and analyze monitored data and discover abnormal situations, including equipment failures and order changes; 5.3. Determine the abnormality type: classify and diagnose the identified abnormalities and determine the specific cause of the abnormality type; 5.4. Generate adjustment plan: Generate corresponding adjustment plan according to the type and severity of abnormal situation, including suspension of production, adjustment of production plan and maintenance of equipment; 5.5. Approval of adjustment plan: Submit the adjustment plan to relevant departments for approval to ensure the rationality and feasibility of the adjustment plan; 5.6. Execute the adjustment plan: adjust the production plan and schedule according to the approved adjustment plan to ensure the stability and safety of the production process; 5.7. Record abnormal information: Record and archive abnormal situations as a reference for subsequent optimization and improvement. 7. The method for dynamic integrated high-level scheduling of steelmaking and steel rolling according to claim 1, characterized in that: said step 6 comprises the following steps: 6.1. Design visual interface: Design corresponding visual interface according to user needs and operating habits, including charts, images and tables; 6.2. Collect production data: Obtain various data in the steelmaking and rolling production process through the data collection module, including order information, equipment status and production progress; 6.3. Processing production data: Process and analyze the collected production data, extract key information and indicators, and provide data support for visualization; 6.4. Realize dynamic display: Display the processed data in a dynamic way, including real-time updating of charts and dynamic display of production process; 6.5. Provide interactive functions: Provide interactive functions in the visual interface, including click operations and dragging, to facilitate users to monitor and manage the production process; 6.6. Integrate other systems: Integrate the visualization system with other production management systems to achieve data sharing and interaction and improve production management efficiency.