Task intelligent optimization scheduling method for industrial data

Abstract

The invention provides an intelligent task optimization scheduling method for industrial data, and relates to the technical field of industrial scheduling. The intelligent task optimization scheduling method for industrial data comprises the steps of S1, starting a task, S2, running equipment, S3, inputting a new task, S4, calculating an emergency value, S5, collecting and preprocessing data, S6, storing old data, S7, predicting a fault, S8, replacing the equipment, and S9, ending the task. The data acquisition in the step S5 comprises the equipment operation state, the production progress, the raw material inventory, the equipment temperature, the production speed, the yield and the energy consumption. According to the invention, through analysis of historical data and real-time data, potential risks of equipment faults can be effectively identified, possible risks are reduced through automatic adjustment, equipment abnormity is processed in time, production interruption caused by faults is avoided, and meanwhile, downtime can be reduced through an active fault management mode of the scheduling method, so that production efficiency is improved. The production loss is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial scheduling, and in particular to an intelligent optimization scheduling method for tasks oriented to industrial data. Background Art

[0002] With the advancement of industrial development, the manufacturing industry is facing an increasingly complex production environment. Traditional scheduling methods often rely on manual experience and are difficult to cope with dynamically changing production needs and market environments. At the same time, the rapid growth of industrial data provides rich information, but how to effectively use this data for real-time scheduling has become an urgent problem to be solved. Therefore, it is particularly important to propose an intelligent task scheduling method for industrial data. Summary of the invention

[0003] In view of the deficiencies in the prior art, the present invention provides an intelligent optimization scheduling method for tasks oriented to industrial data, which solves the problem that traditional scheduling methods are overly dependent on the experience of production personnel.

[0004] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for intelligent optimization and scheduling of tasks for industrial data includes the following steps: S1. Start the task Input production tasks to prepare production equipment; S2. Equipment operation After the production task is input, the production equipment performs production work. If there is no new industrial task or fault during the production process, the process jumps to step S9. If a new task needs to be urgently executed during the operation of the equipment, the current task operation data jumps to step S6 for transmission and storage; S3. New task input Enter new production tasks; S4. Emergency value calculation Calculate the urgency value of the task in step S1 and the newly input task in step S3, and jump to step S2, so that the equipment gives priority to producing the task with the higher urgency value; S5. Data collection and data preprocessing The Internet of Things technology and the sensor devices installed on the production equipment are used to collect various data of the equipment in real time during operation, and the collected data are cleaned and sorted, outliers are removed, missing data are filled, and the data is converted into a format suitable for analysis for reference by production personnel. At the same time, the data is transmitted to step S7 for analysis; S6. Old data storage When executing an emergency task in step S2, the original task data is transferred here for storage, and after the emergency task is completed, the task is jumped to step S1, and the starting task continues to run; S7. Fault prediction Based on the historical operation data and the real-time collected data, the abnormality is identified, the possibility of equipment failure is predicted, and the equipment failure is processed. If the equipment failure is serious and cannot be processed successfully in a short time, the process jumps to step S8. If the process is successful in a short time, the process jumps to step S2. S8. Equipment Replacement When the original production equipment has a fault that cannot be quickly repaired, the backup production equipment is started, and after the production is completed, the process jumps to step S9; S9. Mission completed The production of tasks in this stage is completed; Through the above technical solutions, the scheduling method can effectively improve production efficiency, optimize task scheduling and reduce equipment idle time through real-time data collection and analysis, and secondly, give priority to high-priority tasks through emergency value calculation to ensure on-time delivery.

[0005] Furthermore, the calculation formula of the emergency value in step S4 is: emergency value = w1*(influence of delivery period) + w2*(influence of remaining time) + w3*(task priority) + w4*(influence of resource occupation) + w5*(influence of output) + w6*(influence of delayed cost), where w1, w2, ..., w6 are the weights of each factor; Through the above technical solution, it is possible to better evaluate the priority of a task in the production scheduling process, so as to reasonably arrange production resources and optimize the task execution order.

[0006] Furthermore, the data collected in step S5 includes equipment operation status, production progress, raw material inventory, equipment temperature, production speed, output, and energy consumption; Through the above technical solutions, by analyzing this data, enterprises can optimize production processes, improve efficiency, reduce costs, and enhance decision-making capabilities.

[0007] Furthermore, the fault prediction in step S7 includes automatic processing and manual processing. When the abnormal index exceeds the preset threshold, it is automatically adjusted to reduce the feeding speed and production speed. When it is not possible to handle it by itself, an alarm is immediately triggered to notify the production personnel; Through the above technical solution, potential problems can be identified in advance, the risk of equipment downtime can be reduced, and when production personnel fail to handle them in time, they can be automatically handled within the scope.

[0008] The present invention provides a method for intelligent optimization and scheduling of tasks for industrial data, which has the following beneficial effects: The present invention provides an intelligent optimization scheduling method for tasks oriented to industrial data. This scheduling method can give priority to urgent tasks and reduce delay time through real-time task scheduling and emergency value calculation, thereby improving overall production efficiency. At the same time, it enables production equipment to respond more flexibly to new task requirements and maximize the use of production resources.

[0009] The present invention provides an intelligent optimization scheduling method for tasks oriented to industrial data. The scheduling method can effectively identify the potential risks of equipment failures by analyzing historical data and real-time data, reduce possible risks through automatic adjustment, handle equipment abnormalities in a timely manner, and avoid production interruptions caused by failures. At the same time, the active fault management mode of the scheduling method can reduce downtime and reduce production losses.

[0010] The present invention provides an intelligent optimization and scheduling method for tasks based on industrial data. This intelligent optimization and scheduling method for tasks based on industrial data not only improves production efficiency by combining a variety of advanced technical means, but also lays a foundation for the sustainable development of the enterprise, and at the same time helps to improve the efficiency of production management and the timeliness of problem handling. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a schematic diagram of the task scheduling process of the present invention; Figure 2 This is a diagram of the architecture of fault prediction in the present invention. DETAILED DESCRIPTION

[0012] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Example

[0013] like Figure 1 As shown, an embodiment of the present invention provides a method for intelligent optimization scheduling of tasks for industrial data, comprising the following steps: S1. Start the task Input production tasks to prepare production equipment; S2. Equipment operation After the production task is input, the production equipment performs production work. If there is no new industrial task or fault during the production process, the process jumps to step S9. If a new task needs to be urgently executed during the operation of the equipment, the current task operation data jumps to step S6 for transmission and storage; S3. New task input Enter new production tasks; S4. Emergency value calculation Calculate the urgency value of the task in step S1 and the newly input task in step S3, and jump to step S2, so that the equipment gives priority to producing the task with the higher urgency value; S5. Data collection and data preprocessing The Internet of Things technology and the sensor devices installed on the production equipment are used to collect various data of the equipment in real time during operation, and the collected data are cleaned and sorted, outliers are removed, missing data are filled, and the data is converted into a format suitable for analysis for reference by production personnel. At the same time, the data is transmitted to step S7 for analysis; S6. Old data storage When executing an emergency task in step S2, the original task data is transferred here for storage, and after the emergency task is completed, the task is jumped to step S1, and the starting task continues to run; S7. Fault prediction Based on the historical operation data and the real-time collected data, the abnormality is identified, the possibility of equipment failure is predicted, and the equipment failure is processed. If the equipment failure is serious and cannot be processed successfully in a short time, the process jumps to step S8. If the process is successful in a short time, the process jumps to step S2. S8. Equipment Replacement When the original production equipment has a fault that cannot be quickly repaired, the backup production equipment is started, and after the production is completed, the process jumps to step S9; S9. Mission completed The production of tasks in this stage is completed; This scheduling method can effectively improve production efficiency by optimizing task scheduling and reducing equipment idle time through real-time data collection and analysis. Secondly, by calculating the emergency value, high-priority tasks are prioritized to ensure on-time delivery.

[0014] The calculation formula of the emergency value in step S4 is: Emergency value = w1* (impact of delivery time) + w2* (impact of remaining time) + w3* (task priority) + w4* (impact of resource occupancy) + w5* (impact of output) + w6* (impact of delayed cost), where w1, w2, ..., w6 are the weights of each factor, which can be used to better evaluate the priority of a task in the production scheduling process, so as to reasonably arrange production resources and optimize the order of task execution.

[0015] The data collected in step S5 include equipment operating status, production progress, raw material inventory, equipment temperature, production speed, output, and energy consumption. By analyzing these data, enterprises can optimize production processes, improve efficiency, reduce costs, and enhance decision-making capabilities.

[0016] like Figure 2As described above, the fault prediction in step S7 includes automatic processing and manual processing. When the abnormal index exceeds the preset threshold, it is automatically adjusted to reduce the feeding speed and production speed. When it is not possible to handle it by itself, an alarm is immediately triggered to notify the production personnel. Potential problems can be identified in advance and the risk of equipment downtime can be reduced. When the production personnel fail to handle it in time, it can be automatically handled within the scope.

[0017] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A task intelligent optimization scheduling method for industrial data, characterized in that: The following steps are involved: S1. Start the task Input production tasks to prepare production equipment; S2. Equipment operation After the production task is input, the production equipment performs production work. If there is no new industrial task or fault during the production process, the process jumps to step S9. If a new task needs to be urgently executed during the operation of the equipment, the current task operation data jumps to step S6 for transmission and storage; S3. New task input Enter new production tasks; S4. Emergency value calculation Calculate the urgency value of the task in step S1 and the newly input task in step S3, and jump to step S2, so that the equipment gives priority to producing the task with the higher urgency value; S5. Data collection and data preprocessing The Internet of Things technology and the sensor devices installed on the production equipment are used to collect various data of the equipment in real time during operation, and the collected data are cleaned and sorted, outliers are removed, missing data are filled, and the data is converted into a format suitable for analysis for reference by production personnel. At the same time, the data is transmitted to step S7 for analysis; S6. Old data storage When executing an emergency task in step S2, the original task data is transferred here for storage, and after the emergency task is completed, the task is jumped to step S1, and the starting task continues to run; S7. Fault prediction Based on the historical operation data and the real-time collected data, the abnormality is identified, the possibility of equipment failure is predicted, and the equipment failure is processed. If the equipment failure is serious and cannot be processed successfully in a short time, the process jumps to step S8. If the process is successful in a short time, the process jumps to step S2. S8. Equipment Replacement When the original production equipment has a fault that cannot be quickly repaired, the backup production equipment is started, and after the production is completed, the process jumps to step S9; S9. Mission completed The production of this phase of tasks is completed.

2. The method for intelligent optimization and scheduling of tasks for industrial data according to claim 1 is characterized in that: The calculation formula of the emergency value in step S4 is: Emergency value = w1* (influence of delivery period) + w2* (influence of remaining time) + w3* (task priority) + w4* (influence of resource occupation) + w5* (influence of output) + w6* (influence of delayed cost), where w1, w2, ..., w6 are the weights of each factor.

3. The method for intelligent optimization and scheduling of tasks for industrial data according to claim 1 is characterized in that: The data collected in step S5 includes equipment operating status, production progress, raw material inventory, equipment temperature, production speed, output, and energy consumption.

4. The method for intelligent optimization and scheduling of tasks for industrial data according to claim 1 is characterized in that: The fault prediction in step S7 includes automatic processing and manual processing. When the abnormal index exceeds the preset threshold, it is automatically adjusted to reduce the feeding speed and production speed. When it is not possible to handle it by itself, an alarm is immediately triggered to notify the production personnel.

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