Production workshop adaptive scheduling method, apparatus and device, and medium

Abstract

The invention relates to the technical field of production workshop scheduling. The production workshop self-adaptive scheduling method comprises the steps that a preliminary task and equipment matching scheme is formed through equipment machinable product types, current task states and line changing time parameters in a preset equipment capacity database, and the production workshop self-adaptive scheduling equipment is obtained according to the preliminary task and equipment matching scheme. The method comprises the following steps: optimizing a task scheduling sequence to form an optimized task execution plan, calculating line change time among different production tasks according to the optimized task execution plan, adjusting the task scheduling sequence through the line change time to obtain an adjusted task scheduling sequence, and optimizing the adjusted task scheduling sequence by utilizing a production task allocation optimization model to obtain a production task scheduling sequence. And determining execution time, distribution equipment and a line change plan of each production task, and generating a production scheduling scheme. The method has the effect of improving the production operation efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of production workshop scheduling, and in particular, to a production workshop adaptive scheduling method, device, equipment, and medium. Background Art

[0002] Currently, with the development of the manufacturing industry towards intelligence, flexibility, and high efficiency, the multi-variety, small-batch production mode has become an important production method for modern manufacturing enterprises. The scheduling optimization of the production workshop is of great significance for improving production efficiency, reducing costs, and increasing equipment utilization. The complexity of production tasks, the diversity of equipment, and the uncertainty of order demands make it difficult for traditional production scheduling methods to meet the needs of modern manufacturing. Summary of the Invention

[0003] In order to improve the production operation efficiency, the present application provides a production workshop adaptive scheduling method, device, equipment, and medium.

[0004] The first invention object of the present application is achieved through the following technical solutions: A production workshop adaptive scheduling method, the production workshop adaptive scheduling method includes: Obtain production task requirement information; Through the equipment processable product types, current task status, and line change time parameters in the pre-established equipment capacity database, screen the equipment that meets the production task requirement information to form a preliminary task and equipment matching plan; According to the preliminary task and equipment matching plan, combine the order delivery cycle and production batch size in the production task requirement information and the production equipment load situation in the equipment capacity database to calculate the production task priority and form an optimized task execution plan; According to the optimized task execution plan, combine the equipment processing capacity, current task status, and line change time parameters in the equipment capacity database to calculate the line change time between different production tasks, and adjust the task scheduling order through the line change time to obtain an adjusted task scheduling order; Use the production task allocation optimization model to optimize the adjusted task scheduling order, calculate the production task allocation plan, determine the execution time, allocated equipment, and line change plan for each production task, and generate a production scheduling plan; Input the production scheduling plan into the production control system, and the production control system executes production tasks based on the production scheduling plan.

[0005] By adopting the above technical solutions, equipment that meets the production task requirements is screened through the equipment capacity database, ensuring that task allocation conforms to the processing capacity of the equipment, the current task status, and the line change time requirements, avoiding downtime waiting caused by insufficient equipment capacity or mismatched status, improving equipment utilization rate, and reducing resource waste. By calculating the priority of production tasks, combining the order delivery cycle, production batch size, and equipment load conditions, the task execution order is optimized, enabling key tasks and urgent orders to be processed preferentially, improving order delivery efficiency, and reducing the risk of production delays. By calculating the line change time between tasks and optimizing the task scheduling order, the line change time is reduced, the continuity of production is improved, production stagnation caused by frequent line changes is reduced, and the operating efficiency of the production line is increased. By using the production task allocation optimization model to optimize the adjusted task scheduling order, tasks are reasonably allocated to different equipment, the equipment load is balanced, and some equipment is prevented from running overloaded for a long time or lying idle with low load, improving the overall production capacity. Through automated scheduling execution, scheduling errors caused by human intervention are reduced, the accuracy and response speed of production scheduling are improved, the production process is made more stable and efficient, and ultimately the overall production operation efficiency is enhanced.

[0006] In a preferred example of the present application, it can be further configured that: the equipment in the pre-established equipment capacity database can process product types, current task status, and line change time parameters, and screen the equipment that meets the production task requirement information to form a preliminary task and equipment matching plan, including: According to the product type, process requirements, and processing dimensions in the production task requirement information, screen the equipment that can process the tasks from the equipment capacity database, and eliminate the equipment that does not meet the processing requirements by comparing the product types that the equipment can process, to obtain a preliminary optional equipment set; According to the current task status in the preliminary optional equipment set, screen the equipment that is idle or is expected to complete the line change within the set time, and eliminate the equipment with too long occupation time or line change time exceeding the preset line change duration threshold by comparing the task occupation time with the line change time parameters, to obtain a set of equipment on which the tasks can be executed; According to the unit time processing capacity and processing accuracy of the set of equipment on which the tasks can be executed, screen the equipment that meets the production task requirements, and combine the historical failure rate of the equipment. By preferentially selecting the equipment with fast processing speed, accuracy meeting the requirements, and low historical failure rate, obtain the preliminary task and equipment matching plan.

[0007] By adopting the above technical solutions, by combining the current task status of the equipment, screening for idle equipment or equipment that can complete line change within a set time, waiting caused by equipment occupation or overly long line change time is reduced, and production continuity is improved. Through the comprehensive evaluation of the processing capacity per unit time, processing accuracy, and historical failure rate, equipment with high efficiency, high precision, and good stability is preferentially selected, the quality fluctuations and equipment failure impacts during the production process are reduced, and the overall production efficiency and resource utilization rate are improved.

[0008] In a preferred example of the present application, it can be further configured as follows: According to the preliminary task and equipment matching plan, in combination with the order delivery cycle, production batch size in the production task requirement information, and the production equipment load situation in the equipment capacity database, calculate the production task priority level to form an optimized task execution plan, including: According to the order delivery cycle in the production task requirement information, calculate the remaining delivery time of the production task, and in combination with the equipment processing capacity and the current task status in the equipment capacity database, calculate the estimated completion time of the production task. By comparing the remaining delivery time of the production task with the estimated completion time of the production task, determine the urgency of the task to obtain a task urgency ranking list; According to the task urgency ranking list, extract production tasks with the same task type, and based on the production batch size in the production task requirement information, determine whether they meet the merging condition or splitting condition. By merging batches of tasks that meet the merging condition and splitting batches of tasks that exceed the equipment processing capacity range, obtain an optimized production batch; According to the optimized production batch and the production equipment load situation in the equipment capacity database, calculate the current task load of each equipment, and by adjusting the allocation of tasks among different equipment, obtain the optimized task execution plan.

[0009] By adopting the above technical solutions, by analyzing the task type and batch size, tasks are reasonably merged or split, the production batch is optimized, the production efficiency decline caused by frequent switching of small batch tasks is reduced, and at the same time, the allocation of tasks that exceed the equipment processing capacity is avoided, improving the equipment processing efficiency. By calculating the current task load of the equipment, dynamically adjusting the allocation of tasks among different equipment, balancing the equipment load, preventing some equipment from being overloaded or idling for a long time, improving the overall production capacity and the flexibility of production scheduling, thereby enhancing the production operation efficiency and resource utilization rate.

[0010] In a preferred example, the present application can be further configured as follows: according to the optimized task execution plan, combining the equipment processing capabilities, the current task status, and the line change time parameters in the equipment capability database, calculate the line change time between different production tasks, and adjust the task scheduling order through the line change time to obtain the adjusted task scheduling order, including: According to the optimized task execution plan, extract the execution order of each task, and combine the equipment processing capabilities in the equipment capability database to screen the equipment that can execute the task, and form the corresponding relationship between the task and the executable equipment; According to the corresponding relationship between the task and the executable equipment, determine the process requirements of adjacent tasks, and combine the line change time parameters in the equipment capability database to calculate the line change time between each task, and form a task line change time matrix; According to the task line change time matrix, adjust the task scheduling order in the optimized task execution plan to obtain the adjusted task scheduling order.

[0011] By adopting the above technical solution, by analyzing the process requirements between tasks and combining the line change time parameters, calculate the task line change time, form a line change time matrix, ensure the precise control of the line change time, and reduce unnecessary time loss during the line change process. Through the dynamic adjustment of the task scheduling order, optimize the task sorting, reduce the impact of frequent line changes on production efficiency, improve the continuity and stability of production, thereby enhancing the overall production operation efficiency, reducing the non-value-added time in the production process, and improving resource utilization rate.

[0012] In a preferred example, the present application can be further configured as follows: utilize the production task allocation optimization model to optimize the adjusted task scheduling order, calculate the production task allocation plan, determine the execution time, allocated equipment, and line change plan of each production task, and generate a production scheduling plan, including: Based on the adjusted task scheduling order, combine the equipment-processable product types, the current task status, and the production load situation in the equipment capability database, extract the execution constraint parameters of the production task, and input the execution constraint parameters into the production task allocation optimization model to obtain the initial task allocation parameter set; Optimize the initial task allocation parameter set through a mixed integer optimization algorithm, calculate the task allocation plan that satisfies the line change time, equipment load balance, and task priority constraints, and obtain the optimized production scheduling plan.

[0013] By adopting the above technical solution, by constructing an initial task assignment parameter set and using a mixed-integer optimization algorithm for optimization calculation, it is ensured that the task assignment meets the requirements of changeover time optimization, equipment load balance, and task priority constraints, reduces the changeover time, improves the equipment utilization rate, avoids overloading or underloading of equipment, enhances the accuracy of task scheduling and the execution efficiency of production scheduling, thereby improving the overall production operation efficiency and the rationality of resource allocation.

[0014] In a preferred example of the present application, it can be further configured as follows: The mixed-integer optimization algorithm is used to optimize the initial task assignment parameter set, calculate a task assignment plan that meets the changeover time, equipment load balance, and task priority constraints, and obtain an optimized production scheduling plan, including: According to the initial task assignment parameter set, an optimization objective function is constructed, and based on the optimization objective function, optimization solution variables and constraint conditions are set; The mixed-integer optimization algorithm is used to perform iterative calculations on the optimization solution variables, adjust the assignment of tasks to equipment, preferentially match equipment with short changeover times, optimize task scheduling based on the principle of equipment load balance, and dynamically adjust the execution order in combination with task priorities to obtain the execution time of each production task, the assigned equipment, and the changeover plan; The execution time of each production task, the assigned equipment, and the changeover plan are respectively judged. If the execution time of each production task, the assigned equipment, and the changeover plan all meet the requirements of changeover time, equipment load balance, and task priority, the optimized production scheduling plan is output.

[0015] By adopting the above technical solution, the mixed-integer optimization algorithm is used to perform iterative calculations on the task assignment plan, dynamically adjust the assignment of tasks to equipment, preferentially match equipment with short changeover times, reduce the changeover time, improve production continuity, and at the same time optimize task scheduling based on the principle of equipment load balance to prevent equipment overloading or inefficient operation and improve the overall equipment utilization rate. The execution order is adjusted in combination with task priorities to ensure that high-priority tasks are executed first and enhance the timeliness of order delivery. Finally, by judging whether the execution time of the optimized production task, the task assignment equipment, and the changeover plan meet the optimization objectives, it is ensured that the changeover time is optimal, the equipment load is balanced, and the task priority requirements are met, and the optimized production scheduling plan is output to achieve the efficient execution of production tasks.

[0016] In a preferred example of the present application, it can be further configured as follows: The production workshop adaptive scheduling method further includes: Monitor the equipment status, task execution progress, and production environment parameters in the production workshop in real time to obtain production process information, and analyze the production process information to obtain real-time task execution status information, real-time equipment load information, and real-time changeover time change information; Compare the real-time task execution status information, the real-time equipment load information, and the real-time changeover time change information with the optimized production scheduling plan to determine whether there are deviations in the execution time of each production task, the allocated equipment, and the changeover plan, and when the real-time deviation exceeds the preset deviation threshold, adjust the execution time of each production task, the allocated equipment, and the changeover plan, and update the optimized production scheduling plan.

[0017] By adopting the above technical solution, by comparing the above information with the optimized production scheduling plan in real time, determine whether there are deviations in the task execution time, allocated equipment, and changeover plan, and ensure the execution consistency of the scheduling plan. When the deviation exceeds the preset threshold, automatically adjust the task execution time, equipment allocation, and changeover plan, dynamically update the production scheduling plan, enable the production process to have the ability of adaptive adjustment, ensure that tasks are executed efficiently according to the plan, and reduce production anomalies.

[0018] The second above-mentioned invention object of the present application is achieved through the following technical solutions: A production workshop adaptive scheduling device, the production workshop adaptive scheduling device includes: A task requirement acquisition module, used to acquire production task requirement information; A task and equipment matching module, used to screen the equipment that meets the production task requirement information through the equipment processable product types, current task status, and changeover time parameters in the pre-established equipment capability database, and form a preliminary task and equipment matching plan; A task priority calculation module, used to calculate the production task priority according to the preliminary task and equipment matching plan, combined with the order delivery cycle and production batch size in the production task requirement information and the production equipment load situation in the equipment capability database, and form an optimized task execution plan; A task scheduling optimization module, used to calculate the changeover time between different production tasks according to the optimized task execution plan, combined with the equipment processing capacity, current task status, and changeover time parameters in the equipment capability database, and adjust the task scheduling order through the changeover time to obtain an adjusted task scheduling order; A task allocation optimization module, used to optimize the adjusted task scheduling order by using the production task allocation optimization model, calculate the production task allocation plan, determine the execution time, allocated equipment, and changeover plan of each production task, and generate a production scheduling plan; The production control execution module is used to input the production scheduling plan into the production control system, and the production control system executes production tasks based on the production scheduling plan.

[0019] By adopting the above technical solutions, equipment that meets the production task requirements is screened through the equipment capacity database to ensure that task allocation conforms to the processing capacity of the equipment, the current task status, and the line change time requirements, avoiding downtime waiting caused by insufficient equipment capacity or mismatched status, improving equipment utilization rate, and reducing resource waste. By calculating the priority of production tasks, combining the order delivery cycle, production batch size, and equipment load conditions, the task execution order is optimized, enabling key tasks and urgent orders to be processed first, improving order delivery efficiency, and reducing the risk of production delays. By calculating the line change time between tasks and optimizing the task scheduling order, the line change time is reduced, the continuity of production is improved, production stagnation caused by frequent line changes is reduced, and the operation efficiency of the production line is improved. By using the production task allocation optimization model to optimize the adjusted task scheduling order, tasks are reasonably allocated to different equipment to balance the equipment load, avoiding long-term overloading or low-load idling of some equipment, and improving the overall production capacity. Through automated scheduling execution, scheduling errors caused by human intervention are reduced, the accuracy and response speed of production scheduling are improved, the production process is made more stable and efficient, and ultimately the overall production operation efficiency is enhanced.

[0020] The above object three of the present application is achieved through the following technical solutions: A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above production workshop adaptive scheduling method are implemented.

[0021] The above object four of the present application is achieved through the following technical solutions: A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above production workshop adaptive scheduling method are implemented.

[0022] In summary, the present application includes at least one of the following beneficial technical effects: 1. Screen devices that meet the production task requirements through the device capacity database, ensure that task allocation conforms to the processing capacity of the devices, the current task status, and the line change time requirements, avoid downtime waiting caused by insufficient device capacity or mismatched status, improve device utilization rate, and reduce resource waste. By calculating the priority of production tasks, combining the order delivery cycle, production batch size, and device load conditions, optimize the task execution order, so that key tasks and urgent orders are processed first, improve the order delivery efficiency, and reduce the risk of production delays. By calculating the line change time between tasks and optimizing the task scheduling order, reduce the line change time, improve production continuity, reduce production stagnation caused by frequent line changes, and improve the operation efficiency of the production line. By using the production task allocation optimization model, optimize the adjusted task scheduling order to ensure that tasks are reasonably allocated to different devices, balance the device load, avoid long-term overloading or underutilization of some devices, and improve the overall production capacity. Through automated scheduling execution, reduce the scheduling errors caused by human intervention, improve the accuracy and response speed of production scheduling, make the production process more stable and efficient, and ultimately improve the overall production operation efficiency; 2. Iteratively calculate the task allocation plan through the mixed integer optimization algorithm, dynamically adjust the task allocation on the devices, give priority to matching the devices with short line change time, reduce the line change time, improve production continuity, and at the same time optimize the task scheduling based on the principle of device load balance to prevent device overload or inefficient operation and improve the overall device utilization rate. Combine the task priority to adjust the execution order to ensure that high-priority tasks are executed first and improve the timeliness of order delivery. Finally, by judging whether the optimized production task execution time, task allocation devices, and line change plan meet the optimization goals, ensure the optimal line change time, device load balance, and task priority requirements, and output the optimized production scheduling plan to achieve the efficient execution of production tasks; 3. By comparing the above information with the optimized production scheduling plan in real time, judge whether there are deviations in the task execution time, allocated devices, and line change plan, and ensure the execution consistency of the scheduling plan. When the deviation exceeds the preset threshold, automatically adjust the task execution time, device allocation, and line change plan, dynamically update the production scheduling plan, make the production process have the ability of adaptive adjustment, ensure that tasks are executed efficiently according to the plan, and reduce production anomalies. Description of the Drawings

[0023] Figure 1 is a flowchart of a production workshop adaptive scheduling method in an embodiment of the present application; Figure 2 is an implementation flowchart of step S20 in the production workshop adaptive scheduling method in an embodiment of the present application; Figure 3 is an implementation flowchart of step S30 in the production workshop adaptive scheduling method in an embodiment of the present application; Figure 4 It is a flowchart of an implementation in step S40 of the production workshop adaptive scheduling method in an embodiment of the present application; Figure 5 It is a flowchart of an implementation in step S50 of the production workshop adaptive scheduling method in an embodiment of the present application; Figure 6 It is a flowchart of an implementation in step S502 of the production workshop adaptive scheduling method in an embodiment of the present application; Figure 7 It is a flowchart of an implementation after step S60 of the production workshop adaptive scheduling method in an embodiment of the present application; Figure 8 It is a principle block diagram of a production workshop adaptive scheduling device in an embodiment of the present application; Figure 9 It is a schematic diagram of equipment in an embodiment of the present application. Detailed implementation manners

[0024] The following further details the present application with reference to the accompanying drawings.

[0025] In one embodiment, as Figure 1 shown, the present application discloses a production workshop adaptive scheduling method, which specifically includes the following steps: S10: Obtain production task requirement information.

[0026] Specifically, production task requirement information refers to all key production parameters and task attributes required to complete a certain order or manufacturing requirement during the production process, including order information, product information, production batch information, production priority information, process flow information, equipment requirement information, and production constraint information. Order information includes order number, customer information, order creation time, order delivery cycle, and order status. Product information includes product model, specification, production process requirements, quality standards, and required raw materials and accessories. Production batch information involves the size of the production batch, whether merging or splitting production is required, and minimum / maximum production batch limits. Production priority information determines the priority of tasks based on the order delivery cycle and the urgency of customer needs to ensure that high-priority tasks are scheduled first. Process flow information includes the processing steps, processing process parameters, production sequence, and required equipment types that the product needs to go through. Equipment requirement information involves specific requirements for equipment for this task, such as equipment model, processing capacity, accuracy requirements, and line change requirements. Production constraint information covers production cycle, production line load, energy consumption requirements, and safety specifications, etc.

[0027] S20: Based on the types of products that can be processed by the equipment, the current task status, and the line change time parameters in the pre-established equipment capacity database, screen the equipment that meets the production task requirement information to form a preliminary task and equipment matching plan.

[0028] Specifically, extract the information on the types of products that can be processed by all equipment from the equipment capacity database, and compare the information on the types of products that can be processed with the product model, process requirements, and processing dimensions in the production task requirement information. Use string matching, fuzzy matching, or classification screening methods to eliminate the equipment that does not meet the processing requirements to obtain a preliminary set of optional equipment. Then, obtain the current task status of each piece of equipment in the preliminary set of optional equipment from the equipment capacity database, and determine whether the equipment is in an idle, running, maintenance, or standby state. If the equipment is performing a task, calculate the available time of the equipment based on the estimated completion time of the task, and screen out the equipment that can be released within the task scheduling time range. Subsequently, combine the equipment line change time parameters to calculate the line change time required for task switching. By comparing the task occupancy time with the line change time, eliminate the equipment whose line change time exceeds the set threshold. Finally, obtain a set of equipment on which the task can be executed, and further screen based on the processing capacity per unit time and processing accuracy. On the premise of meeting the task requirements, preferentially select the equipment with a fast processing speed, accuracy meeting the requirements, and a low historical failure rate to form a preliminary task and equipment matching plan.

[0029] S30: According to the preliminary task and equipment matching plan, combine the order delivery cycle and production batch size in the production task requirement information and the production equipment load situation in the equipment capacity database to calculate the production task priority and form an optimized task execution plan.

[0030] Specifically, extract the order delivery cycle from the production task requirement information, calculate the remaining delivery time for each task, and combine the equipment processing capabilities and the current task status in the equipment capacity database to predict the estimated completion time of the task. By comparing the remaining delivery time of the task with the estimated completion time, judge the urgency of the task. Tasks with a higher urgency level are ranked higher in the priority order to ensure that the production plan can be completed on time. Then, sort the production tasks according to the task urgency level, filter out the production tasks of the same type, and combine the production batch size in the production task requirement information to judge whether the tasks can be merged or split. If the batch of the task is small and the process requirements are the same, give priority to merging to reduce the number of line changes and production switching costs. If the task batch is large and exceeds the processing capacity of the equipment, split the task into multiple small batches to match the actual processing capacity and production load of the equipment. Finally, according to the optimized production batch, query the current load situation of the equipment in the equipment capacity database, evaluate the task occupancy ratio of each equipment. If the equipment load is too high, adjust the equipment allocation of the task, and transfer some tasks to the equipment with a lower load but with processing capabilities to balance the equipment utilization rate and reduce the overloading operation of some equipment, and finally form an optimized task execution plan.

[0031] S40: According to the optimized task execution plan, combine the equipment processing capabilities, the current task status, and the line change time parameters in the equipment capacity database to calculate the line change time between different production tasks, and adjust the task scheduling order through the line change time to obtain the adjusted task scheduling order.

[0032] Specifically, extract the execution order of the tasks from the optimized task execution plan, and combine the equipment capacity database to query the processing capabilities and the current task status of the equipment corresponding to each task to determine the specific execution equipment for each task. Then, obtain the process requirements, material types, and processing parameters between adjacent tasks. If two tasks are processed on the same equipment, further analyze the process characteristics. If the process parameters are exactly the same, the line change time is the shortest, and only basic cleaning is required. If there are differences in the process parameters, equipment adjustment, fixture replacement, or material switching are required, and the line change time increases with the increase in the adjustment complexity. Then, according to the line change time parameters in the equipment capacity database, calculate the total line change time under different task orders, and analyze the impact of different sorting schemes on the overall production efficiency. If the line change time of a certain task order scheme is relatively long, adjust the task scheduling order. By rearranging the task execution order, give priority to continuous processing of tasks with high similarity to reduce the time loss caused by frequent line changes, and minimize the impact of line changes on the equipment utilization rate under the premise of meeting the production task requirements, and finally obtain the adjusted task scheduling order.

[0033] S50: Optimize the adjusted task scheduling order using the production task allocation optimization model, calculate the production task allocation plan, determine the execution time, allocated equipment, and line change plan for each production task, and generate a production scheduling plan.

[0034] Specifically, extract the execution time requirements, task priorities, and equipment requirement information of each production task from the adjusted task scheduling order, and combine the product types that the equipment can process, the current task status, the production load situation, and the line change time parameters in the equipment capacity database to screen the set of optional equipment that meets the task requirements. If the process requirements of a certain task can be processed by multiple pieces of equipment, preferentially select the equipment with a lower current load and a shorter line change time to reduce the production switching time and equipment waiting time. Then, construct an optimization objective for task allocation, comprehensively consider the task priority, equipment utilization rate, and the impact of line change time, calculate the execution time of different tasks on the optional equipment, and evaluate the overall production efficiency of different allocation plans. If the load of a certain piece of equipment is too high, adjust the task allocation to optimize the balance of tasks among the equipment, so that the tasks are reasonably distributed on different pieces of equipment to avoid overloading or inefficient operation of individual equipment. Next, based on the optimized task allocation results, calculate the specific execution time of each task, and combine the line change time parameters to adjust the task order to reduce unnecessary equipment switching. By optimizing the line change plan, make the task reach the optimal state when switching between equipment, and finally generate a production scheduling plan.

[0035] S60: Input the production scheduling plan into the production control system, and the production control system executes the production tasks based on the production scheduling plan.

[0036] Specifically, convert the production scheduling plan into executable task instructions, including task numbers, execution times, allocated equipment, and line change plans, and sort the instructions according to the task priorities and equipment availability status, and send the instructions to the corresponding production equipment. At the same time, before the task execution, confirm whether the equipment status meets the scheduling requirements. If the equipment is in a faulty or maintenance state, adjust the task allocation and select a standby equipment to execute the production task. During the task execution, the equipment completes the processing operations according to the scheduling plan. If the task involves a line change, perform tooling replacement, parameter adjustment, and equipment cleaning according to the line change plan to ensure a smooth task conversion. During the task execution, monitor the equipment operation status and task progress in real time. If a task delay, equipment failure, or process deviation is detected, trigger an exception handling process, re-evaluate the task allocation, and adjust the task scheduling order according to the actual production situation to ensure that the production tasks are carried out smoothly according to the optimized scheduling plan.

[0037] In one embodiment, as Figure 2As shown in the figure, in step S20, that is, by means of the product types that can be processed by the equipment, the current task status, and the line change time parameters in the pre-established equipment capability database, the equipment that meets the production task requirement information is screened to form a preliminary task and equipment matching scheme, including: S201: According to the product type, process requirements, and processing dimensions in the production task requirement information, the equipment that can process the task is screened from the equipment capability database. By comparing the product types that can be processed by the equipment, the equipment that does not meet the processing requirements is excluded to obtain a preliminary set of optional equipment.

[0038] Specifically, the product type, process requirements, and processing dimensions are extracted from the production task requirement information, and the product types that can be processed by all the equipment, the executable processes, and the supported processing dimension ranges are obtained from the equipment capability database. Subsequently, a preliminary match is made according to the product type and the product types that can be processed by the equipment, and the equipment with basic processing capabilities is screened out. Then, the matched equipment is further screened. By comparing the processing processes supported by the equipment, the equipment that does not have the required process capabilities is excluded. Finally, the processing dimension parameters of the equipment are checked. If the maximum or minimum processing range of the equipment cannot cover the processing dimensions required by the task, the equipment is excluded. After screening, a preliminary set of optional equipment that meets the task processing requirements is obtained.

[0039] S202: According to the current task status in the preliminary set of optional equipment, the equipment that is idle or expected to complete the line change within the set time is screened. By comparing the task occupancy time and the line change time parameters, the equipment with too long occupancy time or line change time exceeding the preset line change duration threshold is excluded to obtain a set of equipment on which the task can be executed.

[0040] Specifically, the current task status of each piece of equipment is obtained from the preliminary set of optional equipment, including whether it is in an idle, running, standby, or maintenance state, and combined with the task scheduling time window, the equipment that is currently in an idle state or expected to be released within the set time is screened. For the equipment in the running state, the remaining task execution time is calculated, and it is judged whether the equipment can complete the current task and switch to the new task within the required time. Then, the line change time parameter of the equipment is obtained from the equipment capability database, the line change time required to switch from the current task to the new task is calculated, and it is compared with the maximum line change duration allowed by the task. If the line change time of a certain piece of equipment exceeds the preset threshold, the equipment is excluded to avoid affecting the production efficiency due to too long line change time. Finally, the equipment that meets the task occupancy time requirements, has a reasonable line change time, and can execute the new task within the set time is screened out to form a set of equipment on which the task can be executed.

[0041] S203: According to the unit time processing capacity and processing accuracy of the set of task-executable devices, screen the devices that meet the production task requirements, and combine the historical failure rates of the devices. By preferentially selecting devices with high processing speed, accuracy meeting requirements, and low historical failure rates, obtain a preliminary task-device matching plan.

[0042] Specifically, obtain the unit time processing capacity of each device from the set of task-executable devices, and combine the task batch and delivery time in the production task requirements to calculate the estimated processing time of different devices for this task. Eliminate the devices with insufficient processing capacity or processing time exceeding the allowable time range of the task. Then, obtain the processing accuracy parameters of the devices and compare them with the process requirements of the production task to screen out the devices with accuracy meeting the task requirements. If the processing accuracy of a device is lower than the minimum tolerance range required by the task, exclude this device to ensure that the processing quality meets the production standards. Next, extract the historical failure rates of the devices from the device historical operation data, calculate the failure occurrence frequency of each device within the past set time range, and sort the screened devices according to the failure rates. On the premise of meeting the processing capacity and accuracy requirements, preferentially select the devices with low historical failure rates to reduce the risks brought by equipment downtime or maintenance during the production process, and finally obtain a preliminary task-device matching plan.

[0043] In one embodiment, as Figure 3 shown, in step S30, that is, according to the preliminary task-device matching plan, combine the order delivery cycle, production batch size in the production task requirement information, and the production equipment load situation in the equipment capacity database to calculate the production task priority, and form an optimized task execution plan, including: S301: According to the order delivery cycle in the production task requirement information, calculate the remaining delivery time of the production task, and combine the device processing capacity and the current task status in the equipment capacity database to calculate the estimated completion time of the production task. By comparing the remaining delivery time of the production task with the estimated completion time of the production task, determine the urgency of the task to obtain a task urgency ranking list.

[0044] Specifically, extract the order delivery cycle T d and the current time T c from the production task requirement information, and calculate the remaining delivery time T r of the production task: , where T r represents the remaining time of this task until the delivery deadline. If T r is too short, the priority of this task is higher. Then, obtain the unit time processing capacity C of the selected device and the current task status from the equipment capacity database, and calculate the idle time T e of the device after completing the current task: , where T cur is the remaining execution time of the current task of the device. If the device is currently idle, then T e = T c . Next, calculate the estimated completion time T f of this task on the executable device: , where P is the processing batch size of the production task, C is the processing capacity of the device per unit time, and T f represents the earliest possible completion time of the task. Subsequently, calculate the task urgency U: U = T r - T f , where U is the time margin of the task. If U is negative, it means there is a risk of delivery delay for this task and the priority needs to be increased. Finally, sort all tasks according to U, arrange the task priorities from high to low according to the urgency, and form a task urgency sorted list.

[0045] S302: According to the task urgency sorted list, extract the production tasks with the same task type, and based on the production batch size in the production task requirement information, judge whether it meets the merging condition or the splitting condition. By merging the batches of the tasks that meet the merging condition and splitting the batches of the tasks that exceed the device processing capacity range, obtain the optimized production batches.

[0046] Specifically, according to the task urgency sorted list, classify the tasks according to the product model and process requirements, screen the production tasks with the same task type, and extract their corresponding production batch sizes P i and process characteristics, and then judge whether it meets the merging condition or the splitting condition. For task merging, if the product models and process parameters of multiple tasks are the same and the order delivery cycle T d allows merging, then calculate the total production batch size P merge after merging: , where n is the number of tasks that can be merged. If P merge does not exceed the set maximum batch limit P max , then perform task merging to reduce the number of line changes and improve production efficiency. For task splitting, if the batch size P i of a single task exceeds the maximum processable capacity C max of the device, then calculate the number of batches N split after splitting: , where represents the ceiling operation to ensure that the batch size P split of each split task meets: . The split tasks will be executed in different time periods or on different devices to match the actual processing capacity of the device. Finally, generate the optimized production batches.

[0047] S303: Calculate the current task load of each device according to the production equipment load situation in the optimized production batch and equipment capacity database, and obtain the optimized task execution plan by adjusting the distribution of tasks among different devices.

[0048] Specifically, first, extract the current task load situation of each device from the equipment capacity database, including the current assigned task volume L of the device k , the unit time processing capacity C k and the maximum tolerable load L of the device max , and then calculate the total task load L' of each device under the current task arrangement according to the optimized production batch k : , where X ik is a binary variable indicating whether task i is assigned to device k, P i is the production batch of task i. If the calculated L' k exceeds the maximum tolerable load L of the device max , then the task needs to be reallocated. Then, screen out the executable device k' with a lower current load, and calculate the new task load A of the device after undertaking part of the tasks . If A is less than or equal to L max , then reallocate task i to device k' to balance the load of tasks among devices and ensure that the device will not be overloaded or operate inefficiently due to uneven task distribution. Then, for the tasks with adjusted allocation, recalculate the estimated completion time. If the adjustment causes the task completion time to exceed the delivery cycle, continue to optimize the task order so that high-priority tasks are preferentially assigned to devices with higher processing capabilities. Finally, obtain the optimized task execution plan.

[0049] In one embodiment, as Figure 4 shown, in step S40, that is, according to the optimized task execution plan, combined with the equipment processing capacity, current task status and line change time parameters in the equipment capacity database, calculate the line change time between different production tasks, and adjust the task scheduling order through the line change time to obtain the adjusted task scheduling order, including: S401: According to the optimized task execution plan, extract the execution order of each task, and combine the equipment processing capacity in the equipment capacity database to screen the devices that can execute the tasks, and form the corresponding relationship between tasks and executable devices.

[0050] Specifically, all tasks to be executed are extracted from the optimized task execution plan and sorted according to task priority and scheduled execution time to ensure that high-priority tasks can be scheduled for execution within a reasonable time. Then, the types of products that can be processed by all devices, the processing capacity range, and the current task status of all devices are obtained from the device capacity database, and devices that can meet the processing requirements are screened according to the process requirements of the tasks. If the process requirements of a certain task exactly match the processing capacity of a device, the device is preferentially included in the list of devices on which the task can be executed. If a task can be executed by multiple devices, the load conditions of the devices are further screened, and devices with a lower current load or devices that are expected to be released within the scheduling time are preferentially selected to improve resource utilization. Next, the executability of tasks on different devices is analyzed. If a task must be completed on a specific device, the task is directly assigned to that device. Otherwise, based on the task processing time, the current status of the device, and the line change time, the adaptability of multiple optional devices is comprehensively evaluated to form the corresponding relationship between tasks and executable devices.

[0051] S402: According to the corresponding relationship between tasks and executable devices, determine the process requirements of adjacent tasks, and combine the line change time parameters in the device capacity database to calculate the line change time between tasks to form a task line change time matrix.

[0052] Specifically, extract the processing order of all tasks from the corresponding relationship between tasks and executable devices, and screen out adjacent tasks assigned to the same device. Obtain the process requirements of each task, including the type of processing technology, tool or fixture requirements, material characteristics, and device processing parameters. Then, extract the line change time parameters of the corresponding device from the device capacity database, including the standard line change time between different processes, tool change time, material change time, and device adjustment time, and calculate the line change time T between adjacent tasks i and j ij : , where T base is the basic line change time. When the process requirements of adjacent tasks are the same, T base = 0 or takes the shortest standard line change time, T tool is the tool or fixture change time. If tasks i and j require different tools or fixtures, this item is the time required for tool or fixture change, otherwise it takes a value of zero. T material is the material change time. If tasks i and j use different materials, this item is the time for material change, otherwise it takes a value of zero. T setup is the device adjustment time. If tasks i and j require different device parameter settings, this item is the parameter adjustment time, otherwise it takes a value of zero. Finally, organize the line change times between all tasks into a task line change time matrix T: .

[0053] S403: According to the task line change time matrix, adjust the task scheduling order in the optimized task execution plan to obtain the adjusted task scheduling order.

[0054] Specifically, extract the line change times between all tasks from the task line change time matrix, and analyze the impact of different task sorting schemes on the overall line change time. If the line change time between adjacent tasks of a certain task is relatively long, the task order needs to be adjusted to reduce the impact of the line change time on the production rhythm. Then, according to the task priorities in the optimized task execution plan, combined with the delivery cycle of the tasks and the available status of the equipment, evaluate the schedulable range of different tasks, and preferentially adjust the order of low-priority tasks. If there is still a relatively long line change time after the adjusted task order, further analyze the executability of the tasks on different equipment, and try to reassign some tasks to the equipment with shorter line change times to optimize the overall execution efficiency of the task scheduling. Next, combined with the estimated completion time of the tasks, ensure that adjusting the task order will not affect the timely completion of high-priority tasks. If the adjustment of the tasks causes the delivery cycle to be affected, then backtrack and adjust to maintain a balance between the shortest line change time and the feasibility of the task delivery cycle for the task scheduling order. Finally, generate the adjusted task scheduling order.

[0055] In one embodiment, as Figure 5 shown, in step S50, that is, use the production task allocation optimization model to optimize the adjusted task scheduling order, calculate the production task allocation plan, determine the execution time, allocated equipment and line change plan for each production task, and generate the production scheduling plan, including: S501: Based on the adjusted task scheduling order, combined with the product types that the equipment in the equipment capacity database can process, the current task status and the production load situation, extract the execution constraint parameters of the production tasks, and input the execution constraint parameters into the production task allocation optimization model to obtain the initial parameter set of task allocation.

[0056] Specifically, the execution time window of each task is extracted from the adjusted task scheduling order, including the planned start time, estimated completion time, and task priority of the task. Combining with the types of products that the equipment can process recorded in the equipment capacity database, the optional equipment capable of executing the task is screened. If a task can be processed by multiple devices, the current task status of the devices is further analyzed to screen out the devices that are expected to be available within the task scheduling time window. At the same time, considering the current production load of the devices, the available production capacity of each device is calculated to ensure that the task assignment does not exceed the maximum processing capacity of the device. Then, according to the process requirements of the production task, the constraint parameters required for task execution are extracted, including the minimum processing accuracy required for the task, the line change time requirement, the maximum tolerable production delay time, and the task batch limit. These parameters are matched with the optional device information. If the processing capacity, load status, and task scheduling window of a device all meet the requirements, the device is included in the set of optional devices for task assignment. Next, the execution constraint parameters of all tasks, including the task scheduling time, device optional range, production load, process constraints, and line change time requirements, are sorted into the input data required for the task assignment optimization model, and an initial parameter set for task assignment is constructed.

[0057] S502: Optimize the initial parameter set for task assignment through a mixed-integer optimization algorithm, calculate the task assignment plan that satisfies the line change time, equipment load balance, and task priority constraints, and obtain the optimized production scheduling plan.

[0058] Specifically, based on the initial parameter set for task assignment, an optimization objective is constructed, including minimizing the total line change time, balancing the equipment load, and ensuring that high-priority tasks are completed on time. Decision variables are defined to represent the task assignment situation, task execution time, and equipment line change arrangement. Then, constraint conditions are established, including task assignment constraints to ensure that each task must be assigned to an executable device; time constraints to ensure that the task start time conforms to the scheduling order and the task is completed within the specified time window; line change time constraints to control that the line change time between adjacent tasks does not exceed the set maximum allowable line change duration; equipment load balance constraints to ensure that the task loads of all devices are allocated within a reasonable range to avoid overloading or idling of individual devices; task priority constraints to ensure that high-priority tasks are not delayed by low-priority tasks and that urgent tasks can be completed first. Next, a mixed-integer optimization algorithm is used for solution. In each iteration process, based on the line change time matrix and the equipment load status, the device assignment and execution order of the tasks are adjusted, and the objective function value of the current plan is calculated. If the objective function value is continuously optimized during the iteration process, the task assignment and execution order are continued to be adjusted until all constraint conditions are met and optimization convergence is achieved, and finally the optimized task assignment plan is obtained.

[0059] In one embodiment, as Figure 6As shown, in step S502, the initial parameter set of task allocation is optimized by a mixed integer optimization algorithm, and a task allocation scheme that satisfies the line change time, equipment load balancing and task priority constraints is calculated to obtain an optimized production scheduling scheme, including: S5021: Assign an initial parameter set according to the task, construct an optimization objective function, and set optimization solution variables and constraints based on the optimization objective function.

[0060] Specifically, based on the initial parameter set of task allocation, the optimization goal is set to minimize the task line change time, balance the equipment load and ensure that the tasks are completed in order of priority. Among them, minimizing the task line change time is achieved by optimizing the scheduling order of tasks to maximize the process similarity of adjacent tasks to reduce the accumulation of line change time. Balancing the equipment load is achieved by controlling the amount of tasks assigned to each device to make the workload of all devices as close to the average load level as possible. Ensuring that tasks are completed in order of priority is achieved by preferentially allocating high-priority tasks to idle devices when scheduling tasks to reduce the waiting time of high-priority tasks. The optimization objective function is as follows: , where the first term is used to minimize the line change time, Z mnq Indicates whether task m and task n are executed adjacently on device q, W mn represents the line switching time between task m and task n. The second term is used to balance the equipment load. q represents the task load of device q, Q is the total number of devices. Then, based on the optimization objective function, the optimization solution variables are set, where the task allocation variable Y mq It is used to indicate whether task m is assigned to device q, and the task execution time variable U m Indicates the actual start time of task m, and the line change determination variable Z mnq Indicates whether task m and task n are executed continuously on device q. Setting these optimization variables can accurately describe the task allocation, execution time and equipment load. Next, based on the optimization objective function and optimization variables, constraints are established, where the task allocation constraint ensures that each task can only be assigned to one device, the line switching time constraint ensures that the line switching time between tasks does not exceed the set threshold, the equipment load constraint limits the maximum task allocation of the equipment, the task execution time constraint ensures that the tasks are executed in the predetermined order, and the task priority constraint ensures that high-priority tasks are not delayed due to interference from low-priority tasks.

[0061] S5022: Iterate the optimization solution variables through the mixed integer optimization algorithm, adjust the distribution of tasks on the equipment, give priority to matching equipment with short line change time, optimize task scheduling based on the principle of equipment load balancing, and dynamically adjust the execution order based on task priority to obtain the execution time, allocated equipment and line change plan for each production task.

[0062] Specifically, in each iteration, first calculate the line change time W of all tasks on the optional devices mn , and sort all devices in ascending order according to the line change time. Prioritize the device with the shortest line change time for task allocation. At the same time, calculate the total line change time of the current task allocation plan and compare it with the line change time of the previous iteration. If the optimized line change time is reduced, retain the new task allocation plan; otherwise, backtrack and adjust the task allocation. Then, calculate the current load V of the device q , and adjust the task scheduling based on the principle of device load balancing. Calculate the task load deviation of each device and adjust the task allocation to make the load of each device as balanced as possible, and avoid the situation where the load of individual devices is too high or too low under the premise of meeting the optimization of the line change time. Subsequently, dynamically adjust the execution order in combination with the task priorities to ensure that high-priority tasks are not delayed by low-priority tasks. Calculate the task execution time U under the current task sorting m , and ensure that the start time of high-priority tasks meets the priority constraints. If the execution time of a certain task does not meet the priority requirements, re-adjust the task order to give priority to high-priority tasks. Finally, in each iteration, calculate the objective function value of the current task allocation plan and compare it with the objective function value of the previous iteration. If the objective function value continues to be optimized, continue the iteration until the optimization converges, and finally obtain the optimized task execution time, task allocation devices, and line change plan.

[0063] S5023: Judge the execution time, allocated devices, and line change plan of each production task respectively. If the execution time, allocated devices, and line change plan of each production task all meet the requirements of line change time, device load balance, and task priorities, output the optimized production scheduling plan.

[0064] Specifically, judge the execution time of each production task, extract the planned execution time of the task, and check whether there is a situation where a low-priority task starts execution earlier than a high-priority task according to the task priority. If the task order fails to meet the priority requirements, adjust the task scheduling order so that the execution time of high-priority tasks is arranged first to ensure that the task order conforms to the scheduling constraints. Then, judge the equipment assigned to the task, analyze the current task load of the equipment, and ensure that the task assignment will not cause some equipment to be overloaded or idle. If there is an uneven distribution of equipment loads, reassign the tasks so that the tasks are evenly distributed among the equipment to avoid unreasonable utilization of production resources. Next, judge the line change plan, check whether the line change time between tasks meets the set threshold. If the line change time exceeds the limit, optimize the task sorting, and give priority to arranging tasks with similar processes to be executed adjacent to each other to reduce the impact of line change time on the production rhythm. Finally, when the execution time, assigned equipment, and line change plan of all production tasks meet the requirements of line change time optimization, equipment load balance, and task priority, output the optimized production scheduling plan.

[0065] In one embodiment, as Figure 7 shown, after step S60, that is, the production workshop adaptive scheduling method further includes: S70: Monitor the equipment status, task execution progress, and production environment parameters of the production workshop in real time to obtain production process information, and analyze the production process information to obtain real-time task execution status information, real-time equipment load situation information, and real-time line change time change situation information.

[0066] Specifically, analyze the task execution data in the production process information, and obtain real-time task execution status information by comparing the current progress, start time, and estimated completion time of the task with the deviation of the scheduling plan; then, analyze the equipment operation data in the production process information, and obtain real-time equipment load situation information by calculating the task occupancy, operation status, and load level of the equipment; next, analyze the line change data in the production process information, and obtain real-time line change time change situation information by calculating the deviation between the task line change time and the planned line change time.

[0067] S80: Compare the real-time task execution status information, real-time equipment load situation information, and real-time line change time change situation information with the optimized production scheduling plan, judge whether there is a deviation in the execution time, assigned equipment, and line change plan of each production task, and adjust the execution time, assigned equipment, and line change plan of each production task and update the optimized production scheduling plan when the real-time deviation exceeds the preset deviation threshold.

[0068] Specifically, compare the real-time task execution status information with the task execution time in the optimized production scheduling plan, calculate the difference between the actual completion time and the planned completion time of the task. If this difference exceeds the preset task execution deviation threshold, it is determined that there is an execution time deviation for the task. Then, compare the real-time equipment load situation information with the equipment allocation in the optimized production scheduling plan, calculate the difference between the current task load and the expected load of the equipment. If this difference exceeds the set equipment load deviation threshold, it is determined that there is an abnormality in the equipment allocation. Next, compare the real-time change situation information of the line change time with the line change plan in the optimized production scheduling plan, calculate the difference between the actual line change time and the planned line change time. If this difference exceeds the set line change time deviation threshold, it is determined that there is a deviation in the line change plan. Finally, adjust the deviated task execution time, allocated equipment, and line change plan, and give priority to adjusting the task order with less impact on the line change time. If the scheduling optimization goal cannot be met, re-allocate the tasks to equipment with lower loads and recalculate the task execution time to ensure that the adjusted plan meets the requirements of task priorities and equipment load balance, and finally update the optimized production scheduling plan.

[0069] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not imply the order of execution. The order of execution of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0070] In one embodiment, a production workshop adaptive scheduling device is provided, and this production workshop adaptive scheduling device corresponds one-to-one with the production workshop adaptive scheduling method in the above embodiment. As Figure 8 shown, this production workshop adaptive scheduling device includes a task demand acquisition module, a task and equipment matching module, a task priority calculation module, a task scheduling optimization module, a task allocation optimization module, and a production control execution module. The detailed description of each functional module is as follows: The task demand acquisition module is used to acquire production task demand information; The task and equipment matching module is used to screen the equipment that meets the production task demand information through the product types that can be processed by the equipment, the current task status, and the line change time parameters in the pre-established equipment capability database, and form a preliminary task and equipment matching plan; The task priority calculation module is used to calculate the production task priority according to the preliminary task and equipment matching plan, combined with the order delivery cycle in the production task demand information, the production batch size, and the production equipment load situation in the equipment capability database, and form an optimized task execution plan; The task scheduling optimization module is used to calculate the line change time between different production tasks according to the optimized task execution plan, in combination with the equipment processing capabilities, current task status, and line change time parameters in the equipment capacity database, and adjust the task scheduling order through the line change time to obtain the adjusted task scheduling order; The task assignment optimization module is used to optimize the adjusted task scheduling order using the production task assignment optimization model, calculate the production task assignment plan, determine the execution time, assigned equipment, and line change plan for each production task, and generate a production scheduling plan; The production control execution module is used to input the production scheduling plan into the production control system, and the production control system executes the production tasks based on the production scheduling plan.

[0071] Optionally, the task and equipment matching module includes: The equipment preliminary screening sub-module is used to screen the equipment that can process the tasks from the equipment capacity database according to the product type, process requirements, and processing dimensions in the production task demand information, and eliminate the equipment that does not meet the processing requirements by comparing the types of products that the equipment can process, to obtain a preliminary set of optional equipment; The task executability screening sub-module is used to screen the equipment that is idle or expected to complete the line change within the set time according to the current task status in the preliminary set of optional equipment, and eliminate the equipment with too long occupation time or line change time exceeding the preset line change duration threshold by comparing the task occupation time with the line change time parameters, to obtain a set of task executable equipment; The task and equipment matching optimization sub-module is used to screen the equipment that meets the production task requirements according to the unit time processing capacity and processing accuracy of the set of task executable equipment, and combine the historical failure rate of the equipment, and obtain a preliminary task and equipment matching plan by preferentially selecting the equipment with fast processing speed, compliance with accuracy requirements, and low historical failure rate.

[0072] Optionally, the task priority calculation module includes: The task urgency calculation sub-module is used to calculate the remaining delivery time of the production task according to the order delivery cycle in the production task demand information, and combine the equipment processing capabilities and current task status in the equipment capacity database to calculate the estimated completion time of the production task, and determine the urgency of the task by comparing the remaining delivery time of the production task with the estimated completion time of the production task, to obtain a task urgency ranking list; The task batch optimization sub-module is used to extract the production tasks with the same task type according to the task urgency ranking list, and judge whether they meet the merging conditions or splitting conditions based on the production batch size in the production task demand information, and obtain the optimized production batch by merging the tasks that meet the merging conditions and splitting the tasks that exceed the equipment processing capacity range; The task execution plan optimization sub-module is used to calculate the current task load of each device according to the optimized production batch and the production equipment load situation in the equipment capacity database, and obtain an optimized task execution plan by adjusting the distribution of tasks among different devices.

[0073] Optionally, the task scheduling optimization module includes: The task and equipment matching relationship generation sub-module is used to extract the execution order of each task according to the optimized task execution plan, and combine the equipment processing capabilities in the equipment capacity database to screen the devices that can execute the tasks, and form the corresponding relationship between the tasks and the executable devices; The task line change time calculation sub-module is used to determine the process requirements of adjacent tasks according to the corresponding relationship between the tasks and the executable devices, and combine the line change time parameters in the equipment capacity database to calculate the line change time between each task, and form a task line change time matrix; The task scheduling optimization sub-module is used to adjust the task scheduling order in the optimized task execution plan according to the task line change time matrix, and obtain the adjusted task scheduling order.

[0074] Optionally, the task allocation optimization module includes: The task execution constraint extraction sub-module is used to extract the execution constraint parameters of the production tasks based on the adjusted task scheduling order, combine the product types that can be processed by the equipment, the current task status and the production load situation in the equipment capacity database, and input the execution constraint parameters into the production task allocation optimization model to obtain the initial task allocation parameter set; The task allocation optimization calculation sub-module is used to optimize the initial task allocation parameter set through a mixed integer optimization algorithm, calculate the task allocation scheme that satisfies the line change time, equipment load balance and task priority constraints, and obtain the optimized production scheduling scheme.

[0075] Optionally, the task allocation optimization calculation sub-module includes: The optimization objective and constraint setting unit is used to construct an optimization objective function according to the initial task allocation parameter set, and based on the optimization objective function, set the optimization solution variables and constraint conditions; The task allocation iterative optimization unit is used to perform iterative calculations on the optimization solution variables through a mixed integer optimization algorithm, adjust the allocation of tasks on the equipment, give priority to matching the equipment with a short line change time, optimize the task scheduling based on the equipment load balance principle, and dynamically adjust the execution order in combination with the task priority to obtain the execution time, allocated equipment and line change plan of each production task; The optimization result verification and solution output unit is used to judge the execution time, allocated equipment and line change plan of each production task respectively. If the execution time, allocated equipment and line change plan of each production task all meet the requirements of line change time, equipment load balance and task priority, an optimized production scheduling solution is output.

[0076] Optionally, after the production control execution module, there are included: The production process monitoring and analysis module is used to monitor the equipment status, task execution progress and production environment parameters in the production workshop in real time, obtain production process information, and analyze the production process information to obtain real-time task execution status information, real-time equipment load situation information and real-time line change time variation information; The production scheduling dynamic adjustment module is used to compare the real-time task execution status information, real-time equipment load situation information and real-time line change time variation information with the optimized production scheduling solution, judge whether there are deviations in the execution time, allocated equipment and line change plan of each production task, and when the real-time deviation exceeds the preset deviation threshold, adjust the execution time, allocated equipment and line change plan of each production task, and update the optimized production scheduling solution.

[0077] For the specific limitations of the production workshop adaptive scheduling device, reference can be made to the limitations of the production workshop adaptive scheduling method in the above text, which will not be elaborated here. Each module in the above production workshop adaptive scheduling device can be implemented in whole or in part through software, hardware and their combination. The above modules can be embedded in the processor of the computer device in hardware form or be independent of it, or be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0078] In one embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 9 shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used for the device capability database. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a production workshop adaptive scheduling method.

[0079] In one embodiment, there is provided a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented: Obtain production task requirement information; Filter the devices that meet the production task requirement information through the device-processable product types, current task status, and line change time parameters in the pre-established device capability database to form a preliminary task-device matching plan; According to the preliminary task-device matching plan, combine the order delivery cycle and production batch size in the production task requirement information and the production equipment load situation in the device capability database to calculate the production task priority and form an optimized task execution plan; According to the optimized task execution plan, combine the device processing capabilities, current task status, and line change time parameters in the device capability database to calculate the line change time between different production tasks, and adjust the task scheduling order through the line change time to obtain an adjusted task scheduling order; Optimize the adjusted task scheduling order using the production task allocation optimization model, calculate the production task allocation plan, determine the execution time, allocated device, and line change plan for each production task, and generate a production scheduling plan; Input the production scheduling plan into the production control system, and the production control system executes the production tasks based on the production scheduling plan.

[0080] In one embodiment, there is provided a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: Obtain production task requirement information; Filter the devices that meet the production task requirement information through the device-processable product types, current task status, and line change time parameters in the pre-established device capability database to form a preliminary task-device matching plan; According to the preliminary task-device matching plan, combine the order delivery cycle and production batch size in the production task requirement information and the production equipment load situation in the device capability database to calculate the production task priority and form an optimized task execution plan; According to the optimized task execution plan, combine the device processing capabilities, current task status, and line change time parameters in the device capability database to calculate the line change time between different production tasks, and adjust the task scheduling order through the line change time to obtain an adjusted task scheduling order; Optimize the adjusted task scheduling order using the production task allocation optimization model, calculate the production task allocation plan, determine the execution time, allocated device, and line change plan for each production task, and generate a production scheduling plan; Input the production scheduling plan into the production control system, and the production control system executes production tasks based on the production scheduling plan.

[0081] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in this application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0082] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the above-mentioned division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules as needed, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above.

[0083] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. An adaptive scheduling method for a production workshop, characterized in that, The described adaptive scheduling method for the production workshop includes: Obtaining production task requirement information; Filtering the equipment that meets the production task requirement information through the equipment - processable product types, current task status, and line - change time parameters in the pre - established equipment capacity database, and forming a preliminary task - equipment matching plan; According to the preliminary task - equipment matching plan, combining the order delivery cycle and production batch size in the production task requirement information and the production equipment load situation in the equipment capacity database, calculating the production task priority, and forming an optimized task execution plan; According to the optimized task execution plan, combining the equipment processing capacity, current task status, and line - change time parameters in the equipment capacity database, calculating the line - change time between different production tasks, and adjusting the task scheduling order through the line - change time to obtain an adjusted task scheduling order; Using the production task assignment optimization model to optimize the adjusted task scheduling order, calculating the production task assignment plan, determining the execution time, assigned equipment, and line - change plan for each production task, and generating a production scheduling plan; Inputting the production scheduling plan into the production control system, and the production control system executing production tasks based on the production scheduling plan.

2. The adaptive scheduling method for a production workshop according to claim 1, wherein The step of filtering the equipment that meets the production task requirement information through the equipment - processable product types, current task status, and line - change time parameters in the pre - established equipment capacity database, and forming a preliminary task - equipment matching plan includes: According to the product type, process requirements, and processing dimensions in the production task requirement information, screening the equipment that can process the tasks from the equipment capacity database, and eliminating the equipment that does not meet the processing requirements by comparing the equipment - processable product types, to obtain a preliminary optional equipment set; According to the current task status in the preliminary optional equipment set, screening the equipment that is idle or expected to complete line - change within a set time, and eliminating the equipment with too long occupation time or line - change time exceeding the preset line - change duration threshold by comparing the task occupation time with the line - change time parameters, to obtain a task - executable equipment set; According to the unit - time processing capacity and processing accuracy of the task - executable equipment set, screening the equipment that meets the production task requirements, and combining with the historical failure rate of the equipment, and preferentially selecting the equipment with fast processing speed, accuracy meeting the requirements, and low historical failure rate to obtain the preliminary task - equipment matching plan.

3. The production workshop adaptive scheduling method according to claim 1, wherein The step of calculating the production task priority according to the preliminary task - equipment matching plan, combining the order delivery cycle and production batch size in the production task requirement information and the production equipment load situation in the equipment capacity database, and forming an optimized task execution plan includes: Calculate the remaining delivery time of the production task according to the order delivery cycle in the production task requirement information, and calculate the estimated completion time of the production task in combination with the equipment processing capacity and the current task status in the equipment capacity database. Determine the urgency of the task by comparing the remaining delivery time of the production task with the estimated completion time of the production task, and obtain a task urgency ranking list; Extract production tasks with the same task type according to the task urgency ranking list, and judge whether they meet the merging conditions or splitting conditions based on the production batch size in the production task requirement information. Obtain an optimized production batch by merging batches of tasks that meet the merging conditions and splitting batches of tasks that exceed the equipment processing capacity range; Calculate the current task load of each device according to the optimized production batch and the production equipment load situation in the equipment capacity database, and obtain the optimized task execution plan by adjusting the allocation of tasks among different devices.

4. The adaptive scheduling method for a production workshop according to claim 1, wherein According to the optimized task execution plan, calculate the line change time between different production tasks in combination with the equipment processing capacity, the current task status, and the line change time parameters in the equipment capacity database, and adjust the task scheduling order through the line change time to obtain an adjusted task scheduling order, including: According to the optimized task execution plan, extract the execution order of each task, and screen the devices that can execute the task in combination with the equipment processing capacity in the equipment capacity database to form a correspondence between tasks and executable devices; According to the correspondence between tasks and executable devices, determine the process requirements of adjacent tasks, and calculate the line change time between tasks in combination with the line change time parameters in the equipment capacity database to form a task line change time matrix; According to the task line change time matrix, adjust the task scheduling order in the optimized task execution plan to obtain an adjusted task scheduling order.

5. The adaptive scheduling method for a production workshop according to claim 1, wherein Optimize the adjusted task scheduling order by using the production task allocation optimization model, calculate the production task allocation plan, determine the execution time, allocated device, and line change plan of each production task, and generate a production scheduling plan, including: Based on the adjusted task scheduling order, extract the execution constraint parameters of the production task in combination with the product types that can be processed by the equipment, the current task status, and the production load situation in the equipment capacity database, and input the execution constraint parameters into the production task allocation optimization model to obtain an initial task allocation parameter set; Optimize the initial task allocation parameter set by using a mixed integer optimization algorithm, calculate a task allocation plan that meets the line change time, equipment load balance, and task priority constraints, and obtain an optimized production scheduling plan.

6. The adaptive scheduling method for a production workshop according to claim 5, wherein, Optimize the initial task allocation parameter set by using a mixed integer optimization algorithm, calculate a task allocation plan that meets the line change time, equipment load balance, and task priority constraints, and obtain an optimized production scheduling plan, including: Construct an optimization objective function according to the initial parameter set of the task assignment. Based on the optimization objective function, set the optimization solution variables and constraints; Perform iterative calculations on the optimization solution variables through the mixed-integer optimization algorithm, adjust the assignment of tasks to devices, preferentially match devices with short changeover times, optimize task scheduling based on the principle of balanced device load, and dynamically adjust the execution order in combination with task priorities to obtain the execution time of each production task, the assigned devices, and the changeover plan; Judge the execution time of each production task, the assigned devices, and the changeover plan respectively. If the execution time of each production task, the assigned devices, and the changeover plan all meet the requirements of changeover time, balanced device load, and task priorities, output the optimized production scheduling plan.

7. The adaptive scheduling method for a production workshop according to claim 1, characterized in that The adaptive scheduling method for the production workshop further includes: Monitor the device status, task execution progress, and production environment parameters in the production workshop in real time to obtain production process information, and analyze the production process information to obtain real-time task execution status information, real-time device load information, and real-time changeover time change information; Compare the real-time task execution status information, the real-time device load information, and the real-time changeover time change information with the optimized production scheduling plan to determine whether there are deviations in the execution time of each production task, the assigned devices, and the changeover plan, and when the real-time deviation exceeds the preset deviation threshold, adjust the execution time of each production task, the assigned devices, and the changeover plan, and update the optimized production scheduling plan.

8. An adaptive scheduling device for a production workshop, characterized in that, The adaptive scheduling device for the production workshop includes: A task requirement acquisition module for acquiring production task requirement information; A task and device matching module for screening devices that meet the production task requirement information through the device-processable product types, current task status, and changeover time parameters in a pre-established device capability database to form a preliminary task and device matching plan; A task priority calculation module for calculating production task priorities according to the preliminary task and device matching plan, in combination with the order delivery cycle and production batch size in the production task requirement information and the production device load situation in the device capability database to form an optimized task execution plan; A task scheduling optimization module for calculating the changeover time between different production tasks according to the optimized task execution plan, in combination with the device processing capabilities, current task status, and changeover time parameters in the device capability database, and adjusting the task scheduling order through the changeover time to obtain an adjusted task scheduling order; A task assignment optimization module for optimizing the adjusted task scheduling order using a production task assignment optimization model, calculating a production task assignment plan, determining the execution time, assigned devices, and changeover plan for each production task, and generating a production scheduling plan; A production control execution module for inputting the production scheduling plan into a production control system, and the production control system executing production tasks based on the production scheduling plan.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, the steps of the adaptive scheduling method for the production workshop according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, the steps of the adaptive scheduling method for the production workshop according to any one of claims 1 to 7 are implemented.

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