Production workshop adaptive scheduling method, device, equipment and medium

Abstract

The present invention relates to the technical field of production workshop scheduling. Specifically, it relates to a production workshop adaptive scheduling method, device, equipment and medium, wherein the method comprises forming a preliminary task and equipment matching scheme based on the equipment processable product types, current task status and line change time parameters in a pre-established equipment capability database, forming an optimized task execution plan based on the preliminary task and equipment matching scheme, calculating the line change time between different production tasks based on the optimized task execution plan, and adjusting the task scheduling sequence based on the line change time to obtain the adjusted task scheduling sequence, optimizing the adjusted task scheduling sequence using a production task allocation optimization model, determining the execution time of each production task, allocating equipment and line change plan, and generating a production scheduling scheme. The present invention has the effect of improving 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] As the manufacturing industry evolves toward intelligent, flexible, and efficient manufacturing, high-variety, small-batch production has become a key approach for modern manufacturers. Scheduling optimization in production workshops is crucial for improving production efficiency, reducing costs, and increasing equipment utilization. The complexity of production tasks, the diversity of equipment, and the uncertainty of order demand make traditional production scheduling methods difficult to meet the demands of modern manufacturing. Summary of the Invention

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

[0004] The above-mentioned invention objective of this application is achieved through the following technical solutions:

[0005] A production workshop adaptive scheduling method, the production workshop adaptive scheduling method comprising:

[0006] Obtain production task demand information;

[0007] By using the pre-established equipment capability database, which contains the types of products that can be processed by the equipment, the current task status, and the line change time parameters, the equipment that meets the production task requirements is selected to form a preliminary task and equipment matching plan;

[0008] Calculate the production task priority based on the preliminary task and equipment matching plan, combined with the order delivery cycle, production batch size in the production task demand information, and the production equipment load in the equipment capability database, to form an optimized task execution plan;

[0009] Calculating the line change time between different production tasks based on the optimized task execution plan and combining the equipment processing capacity, current task status, and line change time parameters in the equipment capability database, and adjusting the task scheduling sequence based on the line change time to obtain an adjusted task scheduling sequence;

[0010] Utilizing a production task allocation optimization model to optimize the adjusted task scheduling sequence, calculate a production task allocation plan, determine the execution time of each production task, allocate equipment and a line change plan, and generate a production scheduling plan;

[0011] The production scheduling plan is input into a production control system, and the production control system executes production tasks based on the production scheduling plan.

[0012] By implementing this technical solution, the equipment capacity database is used to screen equipment that meets production task requirements. Task allocation ensures that it aligns with the equipment's processing capacity, current task status, and changeover time requirements. This avoids downtime caused by insufficient equipment capacity or mismatched status, improves equipment utilization, and reduces resource waste. By calculating the priority of production tasks and taking into account order delivery cycles, batch sizes, and equipment load, the task execution sequence is optimized, prioritizing critical tasks and urgent orders, improving order delivery efficiency, and reducing the risk of production delays. By calculating the changeover time between tasks and optimizing the task scheduling sequence, this reduces changeover time, improves production continuity, reduces production downtime caused by frequent changeovers, and enhances production line efficiency. By utilizing a production task allocation optimization model, the adjusted task scheduling sequence is optimized to ensure that tasks are appropriately distributed across different equipment, balancing equipment loads, preventing some equipment from operating at excessive capacity or being idle at low capacity, and improving overall production capacity. Automated scheduling reduces scheduling errors caused by human intervention, improves the accuracy and responsiveness of production scheduling, and ensures a more stable and efficient production process, ultimately enhancing overall production efficiency.

[0013] In a preferred example, the present application can be further configured as follows: the equipment that meets the production task requirement information is screened 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 capability database, to form a preliminary task and equipment matching plan, including:

[0014] Based on the product type, process requirements, and processing size in the production task requirement information, screen equipment capable of processing the task from the equipment capability database, and eliminate equipment that does not meet the processing requirements by comparing the types of products that the equipment can process, thereby obtaining a preliminary set of optional equipment;

[0015] Based on the current task status of the preliminary set of optional devices, devices that are idle or expected to complete line change within a set time are screened, and by comparing the task occupancy time with the line change time parameter, devices with excessively long occupancy time or line change time exceeding a preset line change time threshold are eliminated to obtain a set of task executable devices;

[0016] Based on the unit time processing capacity and processing accuracy of the set of equipment that can execute the task, the equipment that meets the requirements of the production task is screened, and combined with the historical failure rate of the equipment, the preliminary task and equipment matching plan is obtained by giving priority to equipment with fast processing speed, satisfactory accuracy and low historical failure rate.

[0017] By adopting the above technical solution and considering the equipment's current task status, we select equipment that is idle or can complete a line change within a set time, reducing waiting times caused by equipment occupancy or excessive line change times and improving production continuity. Through a comprehensive assessment of processing capacity per unit time, processing accuracy, and historical failure rates, we prioritize high-efficiency, high-precision, and stable equipment, reducing quality fluctuations and the impact of equipment failures during the production process, thereby improving overall production efficiency and resource utilization.

[0018] In a preferred example, the present application may be further configured as follows: calculating the production task priority based on the preliminary task and equipment matching plan, combined with the order delivery cycle, production batch size, and production equipment load in the equipment capability database in the production task demand information, to form an optimized task execution plan, including:

[0019] Calculate the remaining delivery time of the production task based on the order delivery cycle in the production task demand information, and calculate the estimated completion time of the production task in combination with the equipment processing capacity in the equipment capability database and the current task status. 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;

[0020] Extracting production tasks of the same task type based on the task urgency sorting list, and determining whether merging conditions or splitting conditions are met based on the production batch size in the production task demand information, and obtaining optimized production batches by merging tasks that meet the merging conditions and splitting tasks that exceed the equipment processing capacity;

[0021] According to the optimized production batch and the production equipment load in the equipment capability database, the current task load of each device is calculated, and the optimized task execution plan is obtained by adjusting the distribution of tasks among different devices.

[0022] By adopting the above technical solution, we analyze task types and batch sizes, rationally merge or split tasks, optimize production batches, reduce production efficiency losses caused by frequent switching of small batch tasks, and avoid assigning tasks that exceed the processing capacity of equipment, thereby improving equipment processing efficiency. By calculating the current task load of the equipment, we dynamically adjust the distribution of tasks between different equipment, balance the equipment load, prevent some equipment from being overloaded or idle for long periods of time, improve overall production capacity and production scheduling flexibility, thereby improving production operation efficiency and resource utilization.

[0023] In a preferred example, the present application may be further configured as follows: calculating the line change time between different production tasks based on the optimized task execution plan, combining the equipment processing capacity, current task status, and line change time parameters in the equipment capability database, and adjusting the task scheduling sequence according to the line change time to obtain the adjusted task scheduling sequence, including:

[0024] Extracting the execution order of each task based on the optimized task execution plan, and screening the equipment that can execute the task in combination with the equipment processing capabilities in the equipment capability database to form a corresponding relationship between the task and the executable equipment;

[0025] Determine the process requirements of adjacent tasks based on the correspondence between the tasks and executable equipment, and calculate the line change time between tasks in combination with the line change time parameters in the equipment capability database to form a task line change time matrix;

[0026] According to the task line switching time matrix, the task scheduling sequence in the optimized task execution plan is adjusted to obtain an adjusted task scheduling sequence.

[0027] By adopting the above technical solution, we analyze the process requirements between tasks and combine them with the line change time parameters to calculate the task line change time and form a line change time matrix. This ensures precise control of line change time and reduces unnecessary time loss during the line change process. By dynamically adjusting the task scheduling sequence, we optimize task sequencing, reduce the impact of frequent line changes on production efficiency, improve production continuity and stability, thereby improving overall production operation efficiency, reducing non-value-added time in the production process, and improving resource utilization.

[0028] In a preferred example, the present application can be further configured as follows: the production task allocation optimization model is used to optimize the adjusted task scheduling sequence, calculate the production task allocation plan, determine the execution time of each production task, allocate equipment and line change plan, and generate a production scheduling plan, including:

[0029] Based on the adjusted task scheduling sequence, combined with the types of products that can be processed by the equipment in the equipment capability database, the current task status and the production load situation, the execution constraint parameters of the production task are extracted, and the execution constraint parameters are input into the production task allocation optimization model to obtain an initial task allocation parameter set;

[0030] The initial parameter set of task allocation is optimized by a mixed integer optimization algorithm, and a task allocation plan that meets the constraints of line change time, equipment load balancing and task priority is calculated to obtain an optimized production scheduling plan.

[0031] By adopting the above technical solution, by constructing the initial parameter set of task allocation and using the mixed integer optimization algorithm for optimization calculation, it is ensured that task allocation meets the line change time optimization, equipment load balancing and task priority constraints, reducing line change time, improving equipment utilization, avoiding equipment overload or underload operation, and improving 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.

[0032] In a preferred example, the present application may be further configured as follows: optimizing the initial task allocation parameter set by a mixed integer optimization algorithm, calculating a task allocation plan that satisfies the line change time, equipment load balancing, and task priority constraints, and obtaining an optimized production scheduling plan, including:

[0033] Assigning an initial parameter set to the task, constructing an optimization objective function, and setting optimization solution variables and constraints based on the optimization objective function;

[0034] The optimization solution variables are iteratively calculated using the mixed integer optimization algorithm to adjust the distribution of tasks on equipment, prioritize matching equipment with short line change times, optimize task scheduling based on the principle of equipment load balancing, and dynamically adjust the execution order based on task priorities to obtain the execution time of each production task, the assigned equipment, and the line change plan;

[0035] The execution time of each production task, the allocation equipment and the line change plan are judged separately. If the execution time of each production task, the allocation equipment and the line change plan all meet the line change time, equipment load balancing and task priority requirements, the optimized production scheduling plan is output.

[0036] By adopting the above technical solution, the task allocation plan is iteratively calculated using a mixed integer optimization algorithm, dynamically adjusting the distribution of tasks on equipment, prioritizing equipment with short changeover times, reducing changeover time, and improving production continuity. At the same time, task scheduling is optimized based on the principle of equipment load balancing to prevent equipment overload or inefficient operation and improve overall equipment utilization. The execution order is adjusted based on task priority to ensure that high-priority tasks are executed first, improving the timeliness of order delivery. Ultimately, by determining whether the optimized production task execution time, task allocation equipment, and changeover plan meet the optimization objectives, ensuring optimal changeover time, balanced equipment load, and task priority requirements, the optimized production scheduling plan is output to achieve efficient execution of production tasks.

[0037] In a preferred example, the present application may be further configured as follows: the production workshop adaptive scheduling method further includes:

[0038] Real-time monitoring of equipment status, task execution progress, and production environment parameters in the production workshop to obtain production process information, and analysis of the production process information to obtain real-time task execution status information, real-time equipment load information, and real-time line change time change information;

[0039] The real-time task execution status information, the real-time equipment load information and the real-time line change time change information are compared with the optimized production scheduling plan to determine whether there are deviations in the execution time of each production task, the allocation equipment and the line change plan. When the real-time deviation exceeds the preset deviation threshold, the execution time of each production task, the allocation equipment and the line change plan are adjusted to update the optimized production scheduling plan.

[0040] By implementing this technical solution, the system compares this information with the optimized production scheduling plan in real time to determine if there are any deviations in task execution time, equipment allocation, and line change plans, ensuring the consistency of the scheduling plan. If the deviation exceeds a preset threshold, the system automatically adjusts the task execution time, equipment allocation, and line change plans, dynamically updating the production scheduling plan. This enables the production process to adapt itself, ensuring efficient and on-time execution of tasks and minimizing production anomalies.

[0041] The second object of the present invention is achieved through the following technical solutions:

[0042] A production workshop adaptive scheduling device, the production workshop adaptive scheduling device comprising:

[0043] Task requirement acquisition module, used to obtain production task requirement information;

[0044] The task and equipment matching module is used to screen equipment that meets the production task requirements 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 capability database, and form a preliminary task and equipment matching plan;

[0045] A task priority calculation module is used to calculate the production task priority based on the preliminary task and equipment matching plan, combined with the order delivery cycle, production batch size and production equipment load in the equipment capability database in the production task demand information, to form an optimized task execution plan;

[0046] A task scheduling optimization module is used to calculate the line change time between different production tasks based on the optimized task execution plan, combined with the equipment processing capacity, current task status and line change time parameters in the equipment capability database, and adjust the task scheduling sequence according to the line change time to obtain an adjusted task scheduling sequence;

[0047] A task allocation optimization module is used to optimize the adjusted task scheduling sequence using a production task allocation optimization model, calculate a production task allocation plan, determine the execution time of each production task, allocate equipment and line change plans, and generate a production scheduling plan;

[0048] 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 task based on the production scheduling plan.

[0049] By implementing this technical solution, the equipment capacity database is used to screen equipment that meets production task requirements. Task allocation ensures that it aligns with the equipment's processing capacity, current task status, and changeover time requirements. This avoids downtime caused by insufficient equipment capacity or mismatched status, improves equipment utilization, and reduces resource waste. By calculating the priority of production tasks and taking into account order delivery cycles, batch sizes, and equipment load, the task execution sequence is optimized, prioritizing critical tasks and urgent orders, improving order delivery efficiency, and reducing the risk of production delays. By calculating the changeover time between tasks and optimizing the task scheduling sequence, this reduces changeover time, improves production continuity, reduces production downtime caused by frequent changeovers, and enhances production line efficiency. By utilizing a production task allocation optimization model, the adjusted task scheduling sequence is optimized to ensure that tasks are appropriately distributed across different equipment, balancing equipment loads, preventing some equipment from operating at excessive capacity or being idle at low capacity, and improving overall production capacity. Automated scheduling reduces scheduling errors caused by human intervention, improves the accuracy and responsiveness of production scheduling, and ensures a more stable and efficient production process, ultimately enhancing overall production efficiency.

[0050] The third objective of this application is achieved through the following technical solutions:

[0051] 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-mentioned production workshop adaptive scheduling method are implemented.

[0052] The fourth objective of this application is achieved through the following technical solutions:

[0053] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned production workshop adaptive scheduling method.

[0054] In summary, this application includes at least one of the following beneficial technical effects:

[0055] 1. Use the equipment capability database to screen equipment that meets production task requirements, ensuring that task allocations are consistent with the equipment's processing capabilities, current task status, and line change time requirements. This avoids downtime caused by insufficient equipment capacity or mismatched status, improves equipment utilization, and reduces resource waste. By calculating the priority of production tasks and combining order delivery cycles, production batch sizes, and equipment load, the task execution sequence is optimized, prioritizing critical tasks and urgent orders, improving order delivery efficiency, and reducing the risk of production delays. By calculating the line change time between tasks and optimizing the task scheduling sequence, line change time is reduced, production continuity is improved, production stagnation caused by frequent line changeovers is reduced, and production line efficiency is improved. By utilizing the production task allocation optimization model, the adjusted task scheduling sequence is optimized to ensure that tasks are reasonably allocated to different equipment, balancing equipment loads, avoiding long-term overload or low-load idleness of certain equipment, and improving overall production capacity. Through automated scheduling execution, scheduling errors caused by human intervention are reduced, the accuracy and responsiveness of production scheduling are improved, making the production process more stable and efficient, and ultimately improving overall production efficiency.

[0056] 2. Iterate the task allocation plan through a mixed integer optimization algorithm, dynamically adjust the allocation of tasks on the equipment, give priority to matching equipment with short line change time, reduce line change time, improve production continuity, and optimize task scheduling based on the principle of equipment load balancing to prevent equipment overload or inefficient operation and improve overall equipment utilization. Adjust the execution order based on task priority 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 equipment and line change plan meet the optimization goals, ensure the optimal line change time, equipment load balance and task priority requirements, output the optimized production scheduling plan, and achieve efficient execution of production tasks;

[0057] 3. By comparing this information with the optimized production scheduling plan in real time, the system determines whether there are any deviations in task execution time, equipment allocation, and line change plans, ensuring the consistency of the scheduling plan. When the deviation exceeds the preset threshold, the system automatically adjusts the task execution time, equipment allocation, and line change plans, dynamically updating the production scheduling plan. This enables the production process to adapt itself, ensuring efficient execution of tasks according to plan and reducing production anomalies. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 This is a flow chart of a production workshop adaptive scheduling method in one embodiment of the present application;

[0059] Figure 2 This is a flowchart for implementing step S20 in the production workshop adaptive scheduling method in one embodiment of the present application;

[0060] Figure 3 This is a flowchart for implementing step S30 in the production workshop adaptive scheduling method in one embodiment of the present application;

[0061] Figure 4 This is a flowchart for implementing step S40 in the production workshop adaptive scheduling method in one embodiment of the present application;

[0062] Figure 5 This is a flowchart for implementing step S50 in the production workshop adaptive scheduling method in one embodiment of the present application;

[0063] Figure 6 This is a flowchart for implementing step S502 in the production workshop adaptive scheduling method in one embodiment of the present application;

[0064] Figure 7 This is a flowchart for implementing the step S60 in the production workshop adaptive scheduling method in one embodiment of the present application;

[0065] Figure 8 This is a principle block diagram of a production workshop adaptive scheduling device in one embodiment of the present application;

[0066] Figure 9 It is a schematic diagram of a device in one embodiment of the present application. DETAILED DESCRIPTION

[0067] The present application is further described in detail below with reference to the accompanying drawings.

[0068] In one embodiment, if Figure 1 As shown, the present application discloses a production workshop adaptive scheduling method, which specifically includes the following steps:

[0069] S10: Obtain production task demand information.

[0070] Specifically, production task requirement information refers to all key production parameters and task attributes required to complete a specific 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, specifications, production process requirements, quality standards, and required raw materials and accessories. Production batch information involves production batch size, whether production needs to be merged or split, and minimum / maximum production batch limits. Production priority information determines the priority of tasks based on order delivery cycle and customer demand urgency to ensure that high-priority tasks are scheduled first. Process flow information includes the processing steps required for the product, processing parameters, production sequence, and required equipment type. Equipment requirement information involves the specific equipment requirements for the 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 regulations.

[0071] S20: By using the pre-established equipment capability database, which contains the types of products that can be processed by the equipment, the current task status, and the line change time parameters, the equipment that meets the production task requirements is selected to form a preliminary task and equipment matching plan.

[0072] Specifically, the processable product type information of all equipment is extracted from the equipment capability database, and the processable product type information is compared with the product model, process requirements and processing size in the production task requirement information. String matching, fuzzy matching or classification screening methods are used to eliminate equipment that does not meet the processing requirements to obtain a preliminary set of optional equipment. The current task status of each equipment in the preliminary set of optional equipment is then obtained from the equipment capability database to determine whether the equipment is in idle, running, maintenance or standby state. If the equipment is executing a task, the available time of the equipment is calculated based on the expected completion time of the task, and the equipment that can be released within the task scheduling time range is screened out. Subsequently, the line change time required for task switching is calculated based on the equipment line change time parameter. By comparing the task occupancy time with the line change time, equipment with a line change time exceeding the set threshold is eliminated. Finally, a set of task-executable equipment is obtained, and further screening is carried out based on the processing capacity per unit time and processing accuracy. On the premise of meeting the task requirements, equipment with fast processing speed, satisfactory accuracy and low historical failure rate is given priority to form a preliminary task and equipment matching plan.

[0073] S30: Based on 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 in the equipment capacity database, the production task priority is calculated to form an optimized task execution plan.

[0074] Specifically, the order delivery cycle is extracted from the production task demand information, the remaining delivery time for each task is calculated, and the expected completion time of the task is predicted by combining the equipment processing capacity and current task status in the equipment capability database. By comparing the remaining delivery time of the task with the expected completion time, the urgency of the task is determined. Tasks with higher urgency are prioritized to ensure that the production plan can be completed on time. Then, according to the task urgency ranking, production tasks of the same task type are screened. Combined with the production batch size in the production task demand information, the tasks can be determined whether to be merged or split. If the task batch size is small and the process requirements are consistent, it is preferentially merged to reduce the number of line changes and production switching costs. If the task batch size is large and exceeds the processing capacity of the equipment, the task is split into multiple small batches to match the actual processing capacity and production load of the equipment. Finally, based on the optimized production batch size, the current equipment load in the equipment capability database is queried to evaluate the task occupancy ratio of each equipment. If the equipment load is too high, the equipment allocation of the task is adjusted, and some tasks are transferred to equipment with lower load but sufficient processing capacity to balance equipment utilization and reduce the overload of some equipment. Finally, an optimized task execution plan is formed.

[0075] S40: 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, the line change time between different production tasks is calculated, and the task scheduling sequence is adjusted according to the line change time to obtain the adjusted task scheduling sequence.

[0076] Specifically, the execution order of tasks is extracted from the optimized task execution plan. Combined with the equipment capability database, the processing capacity and current task status of each task's corresponding equipment are queried to determine the specific execution equipment for each task. The process requirements, material type, and processing parameters of adjacent tasks are then obtained. If two tasks are processed on the same equipment, the process characteristics are further analyzed. If the process parameters are completely consistent, the line change time is minimal, requiring only basic cleaning. If the process parameters differ, equipment adjustment, fixture replacement, or material switching are required, and the line change time increases with the complexity of the adjustment. Next, based on the line change time parameters in the equipment capability database, the total line change time for different task sequences is calculated, and the impact of different sorting schemes on overall production efficiency is analyzed. If a certain task sequence scheme has a long line change time, the task scheduling sequence is adjusted. By rearranging the task execution order, tasks with high similarity are prioritized for continuous processing, reducing the time loss caused by frequent line changes. While meeting production task requirements, the impact of line changes on equipment utilization is minimized, ultimately resulting in the adjusted task scheduling sequence.

[0077] S50: Utilize the production task allocation optimization model to optimize the adjusted task scheduling sequence, calculate the production task allocation plan, determine the execution time of each production task, allocate equipment and line change plan, and generate a production scheduling plan.

[0078] Specifically, the execution time requirements, task priorities, and equipment requirements of each production task are extracted from the adjusted task scheduling sequence. Combined with the types of products that can be processed by the equipment, the current task status, production load, and changeover time parameters in the equipment capability database, a set of optional equipment that meets the task requirements is selected. If the process requirements of a task can be processed by multiple equipment, equipment with lower current load and shorter changeover time is prioritized to reduce production switching time and equipment waiting time. Then, a task allocation optimization objective is constructed, comprehensively considering the impact of task priority, equipment utilization, and changeover time. The execution time of different tasks on the optional equipment is calculated, and the overall production efficiency of different allocation schemes is evaluated. If the load of a certain equipment is too high, the task allocation is adjusted to optimize the balance of tasks across equipment, so that tasks are reasonably distributed across different equipment and avoid overloading or inefficient operation of individual equipment. Next, based on the optimized task allocation results, the specific execution time of each task is calculated. Combined with the changeover time parameters, the task sequence is adjusted to reduce unnecessary equipment switching. By optimizing the changeover plan, the task is optimally switched between equipment, and the production scheduling solution is finally generated.

[0079] S60: Input the production scheduling plan into the production control system, and the production control system executes the production task based on the production scheduling plan.

[0080] Specifically, the production scheduling plan is converted into executable task instructions, including task number, execution time, assigned equipment, and line change plan. The instructions are sorted according to the task priority and equipment availability, and sent to the corresponding production equipment. At the same time, before the task is executed, it is confirmed whether the equipment status meets the scheduling requirements. If the equipment is in a faulty or maintenance state, the task assignment is adjusted and the backup equipment is selected to execute the production task. During the task execution process, the equipment completes the processing operation according to the scheduling plan. If the task involves line change, the tooling replacement, parameter adjustment, and equipment cleaning are performed according to the line change plan to ensure smooth task conversion. During the task execution process, the equipment operating status and task progress are monitored in real time. If a task delay, equipment failure, or process deviation is detected, the exception handling process is triggered, the task assignment is re-evaluated, and the task scheduling sequence is adjusted according to the actual production situation to ensure that the production task is carried out smoothly according to the optimized scheduling plan.

[0081] In one embodiment, if Figure 2As shown, in step S20, the equipment that meets the production task requirements is screened 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 capability database, and a preliminary task and equipment matching plan is formed, including:

[0082] S201: Based on the product type, process requirements and processing size in the production task requirement information, select equipment that can process the task from the equipment capability database, and eliminate 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.

[0083] Specifically, product types, process requirements and processing dimensions are extracted from the production task requirement information, and the processable product types, executable processes and supported processing dimension ranges of all equipment are obtained from the equipment capability database. Subsequently, a preliminary match is made between the product types and the processable product types of the equipment to screen out equipment with basic processing capabilities. Then, the matched equipment is further screened by comparing the processing technologies supported by the equipment to eliminate equipment that does not have the required process capabilities. 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.

[0084] S202: Based on the current task status in the preliminary optional device set, filter out devices that are idle or expected to complete the line change within the set time. By comparing the task occupancy time with the line change time parameter, eliminate devices with too long occupancy time or line change time exceeding the preset line change time threshold, and obtain the task executable device set.

[0085] Specifically, the current task status of each device is obtained from the preliminary set of optional devices, including whether it is idle, running, standby or maintenance. Combined with the task scheduling time window, the devices that are currently idle or expected to be released within the set time are screened out. For devices in the running state, the remaining task execution time is calculated, and it is determined whether the device can complete the current task and switch to the new task within the required time. Then, the line change time parameters of the device are obtained from the device capability database, the line change time required to switch from the current task to the new task is calculated, and compared with the maximum line change time allowed by the task. If the line change time of a device exceeds the preset threshold, the device is eliminated to avoid affecting production efficiency due to excessive line change time. Finally, the devices that meet the task occupancy time requirements, have a reasonable line change time and can execute the new task within the set time are screened out to form a set of task executable devices.

[0086] S203: Based on the unit time processing capacity and processing accuracy of the task executable equipment set, screen the equipment that meets the production task requirements, and combine the historical failure rate of the equipment to obtain a preliminary task and equipment matching plan by giving priority to equipment with fast processing speed, satisfactory accuracy and low historical failure rate.

[0087] Specifically, the unit-time processing capacity of each device is obtained from the set of task-executable devices. Combined with the task batch and delivery time in the production task requirements, the estimated processing time of different devices on the task is calculated, and devices with insufficient processing capacity or processing time exceeding the allowed time range of the task are eliminated. Then, the processing accuracy parameters of the equipment are obtained and compared with the process requirements of the production task. Devices whose accuracy meets the task requirements are screened out. If the processing accuracy of a device is lower than the minimum tolerance range required by the task, the device is excluded to ensure that the processing quality meets production standards. Next, the historical failure rate of the device is extracted from the historical operation data of the device. The frequency of failure of each device within the past set time range is calculated, and the screened devices are sorted according to the failure rate. On the premise of meeting the processing capacity and accuracy requirements, devices with low historical failure rates are given priority to reduce the risk of equipment downtime or maintenance during the production process. Finally, a preliminary task and equipment matching plan is obtained.

[0088] In one embodiment, if Figure 3 As shown, in step S30, based on 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 in the equipment capacity database, the production task priority is calculated to form an optimized task execution plan, including:

[0089] S301: Calculate the remaining delivery time of the production task based on the order delivery cycle in the production task demand information, and calculate the estimated completion time of the production task in combination with the equipment processing capacity and current task status in the equipment capability 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.

[0090] Specifically, the order delivery cycle T is extracted from the production task demand information d and the current time T c , calculate the remaining delivery time T of the production task r : , where T r Indicates the remaining time of the task from the delivery deadline. If T r If the time is too short, the task has a higher priority. Then, the unit time processing capacity C of the selected device and the current task status are obtained from the device capability database, and the idle time T of the device after the current task is completed is calculated. e: , where T cur is the remaining execution time of the device’s current task. If the device is currently idle, T e =T c Next, calculate the estimated completion time T of the task on the executable device f : , where P is the processing batch of the production task, C is the processing capacity of the equipment per unit time, T f Indicates the earliest possible completion time of the task. Then, calculate the task urgency U: U=T r -T f , where U is the time margin of the task. If U is a negative value, it means that the task has the risk of delayed delivery and needs to be prioritized. Finally, all tasks are sorted according to U, and the task priorities are arranged from high to low according to the urgency, forming a task urgency sorting list.

[0091] S302: Extract production tasks of the same task type based on the task urgency sorting list, and determine whether the merging or splitting conditions are met based on the production batch size in the production task demand information. By merging batches of tasks that meet the merging conditions and splitting batches of tasks that exceed the equipment processing capacity, an optimized production batch is obtained.

[0092] Specifically, according to the task urgency sorting list, tasks are classified according to product model and process requirements, production tasks with the same task type are screened, and their corresponding production batch size P is extracted. i and process characteristics, and then determine whether it meets the conditions for merging or splitting. For task merging, if the product models and process parameters of multiple tasks are consistent, and the order delivery cycle T d If merging is allowed, calculate the total production batch size P after merging merge : , where n is the number of mergeable tasks. If P merge , not exceeding 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 of a single task is i Exceeding the maximum processing capacity of the equipment C max , then calculate the number of batches after splitting N split : ,in, Indicates the rounding operation to ensure that the batch size of each split task is P split satisfy: ,The split tasks will be executed in different time periods or on ,equipment to match the actual processing capacity of the equipment, and finally, ,an optimized production batch will be generated.

[0093] S303: Calculate the current task load of each device based on the optimized production batch and production equipment load in the equipment capacity database, and obtain an optimized task execution plan by adjusting the distribution of tasks among different devices.

[0094] Specifically, first, the current task load of each device is extracted from the device capability database, including the current assigned task volume L of the device. k , processing capacity per unit time C k And the maximum load L that the equipment can bear max , then calculate the total task load L' of each device under the current task arrangement based on 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 load L of the equipment max , then the tasks need to be reallocated. Then, the executable device k' with lower current load is selected and the new task load A of the device is calculated after it takes on part of the task. , if A is less than or equal to L max , task i is reallocated to machine k' to balance the load across the machines and ensure that the machines are not overloaded or operate inefficiently due to uneven task distribution. Next, the estimated completion times for the adjusted tasks are recalculated. If the adjustment causes the task completion time to exceed the delivery cycle, the task sequence is further optimized to prioritize high-priority tasks to machines with higher processing capacity. Ultimately, the optimized task execution plan is obtained.

[0095] In one embodiment, if Figure 4 As shown, in step S40, the line change time between different production tasks is calculated based on the optimized task execution plan, combined with the equipment processing capacity, current task status and line change time parameters in the equipment capacity database, and the task scheduling sequence is adjusted according to the line change time to obtain the adjusted task scheduling sequence, including:

[0096] S401: Extract the execution order of each task according to the optimized task execution plan, and screen the equipment that can execute the task in combination with the equipment processing capabilities in the equipment capability database to form a corresponding relationship between the task and the executable equipment.

[0097] Specifically, all pending tasks are extracted from the optimized task execution plan and sorted by task priority and scheduled execution time, ensuring that high-priority tasks are scheduled for execution within a reasonable timeframe. Next, the machine capability database retrieves the processable product types, processing capacity ranges, and current task status of all equipment. Equipment that can meet the processing requirements is screened based on the task's process requirements. If a task's process requirements fully match the equipment's processing capacity, that equipment is prioritized for inclusion in the list of eligible equipment for the task. If a task can be performed by multiple equipment, the equipment's load is further screened, prioritizing equipment with a lower current load or expected to be released within the scheduled timeframe to improve resource utilization. Next, the task's executable nature is analyzed on different equipment. If a task must be completed on a specific equipment, it is directly assigned to that equipment. Otherwise, the compatibility of multiple available equipment is comprehensively evaluated based on the task's processing time, the equipment's current status, and the line change time, forming a corresponding relationship between the task and the eligible equipment.

[0098] S402: Determine the process requirements of adjacent tasks based on the correspondence between tasks and executable equipment, and calculate the line change time between tasks in combination with the line change time parameters in the equipment capability database to form a task line change time matrix.

[0099] Specifically, the processing sequence of all tasks is extracted from the correspondence between tasks and executable equipment, and adjacent tasks assigned to the same equipment are screened out to obtain the process requirements of each task, including the processing technology type, tool or fixture requirements, material characteristics, and equipment processing parameters. Then, the line change time parameters of the corresponding equipment are extracted from the equipment capability database, including the standard line change time between different processes, tool change time, material switching time, and equipment adjustment time, and the line change time T between adjacent tasks i and j is calculated. ij : , where T base is the basic line change time. When the process requirements of adjacent tasks are the same, T base =0 or take the shortest standard line change time, T tool is the tool or fixture replacement time. If tasks i and j require different tools or fixtures, then this item is the time required for tool or fixture replacement. Otherwise, it is zero. T material is the material switching time. If tasks i and j use different materials, this item is the time to change the material. Otherwise, it is zero. T setup =The equipment adjustment time. If task i and task j require different equipment parameter settings, then this item is the parameter adjustment time. Otherwise, the value is zero. Finally, the line change time between all tasks is organized into the task line change time matrix T: .

[0100] S403: Adjusting the task scheduling sequence in the optimized task execution plan according to the task line switching time matrix to obtain an adjusted task scheduling sequence.

[0101] Specifically, the changeover times between all tasks are extracted from the task changeover time matrix. The impact of different task ordering schemes on overall changeover time is analyzed. If the changeover time between adjacent tasks within a task is long, the task order needs to be adjusted to reduce the impact of changeover time on the production rhythm. Then, based on the task priorities in the optimized task execution plan, combined with the task delivery cycles and equipment availability, the schedulable range of different tasks is evaluated, and the order of low-priority tasks is adjusted first. If the adjusted task order still results in long changeover times, the task's executability on different equipment is further analyzed, and some tasks are attempted to be reassigned to equipment with shorter changeover times to optimize the overall execution efficiency of task scheduling. Next, based on the expected completion times of the tasks, the task order is ensured to ensure that the adjustment does not affect the on-time completion of high-priority tasks. If the task adjustment affects the delivery cycle, the task scheduling order is adjusted retroactively to maintain a balance between minimizing changeover times and ensuring the feasibility of the task delivery cycle. Finally, the adjusted task scheduling order is generated.

[0102] In one embodiment, if Figure 5 As shown, in step S50, the adjusted task scheduling sequence is optimized using the production task allocation optimization model, a production task allocation plan is calculated, the execution time of each production task, the equipment allocation and line change plan are determined, and a production scheduling plan is generated, including:

[0103] S501: Based on the adjusted task scheduling sequence, combined with the equipment processable product types, current task status and production load in the equipment capability database, the execution constraint parameters of the production task are extracted, and the execution constraint parameters are input into the production task allocation optimization model to obtain the initial parameter set of the task allocation.

[0104] Specifically, the execution time window for each task, including the task's planned start time, estimated completion time, and task priority, is extracted from the adjusted task scheduling sequence. This is combined with the types of products that can be processed by the equipment in the equipment capability database to screen for available equipment capable of executing the task. If a task can be processed by multiple equipment, the current task status of the equipment is further analyzed to select equipment that is expected to be available within the task scheduling time window. Furthermore, the available capacity of each equipment is calculated, taking into account the equipment's current production load, to ensure that the task allocation does not exceed the equipment's maximum processing capacity. Then, based on the production task's process requirements, the constraint parameters required for task execution are extracted, including the minimum processing accuracy required, line change time requirement, maximum tolerable production delay time, and task batch restrictions. These parameters are then matched with the available equipment information. If a piece of equipment's processing capacity, load status, and task scheduling window meet the requirements, the equipment is included in the set of available equipment for task allocation. Next, all task execution constraint parameters, including task scheduling time, equipment selection range, production load, process constraints, and line change time requirements, are organized into the input data required by the task allocation optimization model, and the initial parameter set for task allocation is constructed.

[0105] S502: Optimize the initial parameter set of task allocation through a mixed integer optimization algorithm, calculate a task allocation plan that meets the constraints of line change time, equipment load balancing, and task priority, and obtain an optimized production scheduling plan.

[0106] Specifically, based on the initial set of task allocation parameters, optimization objectives are constructed, including minimizing total changeover time, balancing equipment load, and ensuring on-time completion of high-priority tasks. Decision variables are defined to represent task allocation, task execution time, and equipment changeover schedule. Constraints are then established, including task allocation constraints to ensure that each task is assigned to an executable device; time constraints to ensure that task start times conform to the scheduling order and that tasks are completed within a specified time window; changeover time constraints to control the changeover time of adjacent tasks within the maximum allowable changeover time; equipment load balancing constraints to ensure that task loads across all devices are distributed within a reasonable range, avoiding overloaded or idle devices; and task priority constraints to ensure that high-priority tasks are not delayed by low-priority tasks, ensuring that urgent tasks are completed first. A mixed integer optimization algorithm is then used to solve the problem. During each iteration, the device allocation and execution order of tasks are adjusted based on the changeover time matrix and equipment load status, and the objective function value of the current solution is calculated. If the objective function value continues to improve during the iteration, the task allocation and execution order are further adjusted until all constraints are met and the optimization converges, ultimately resulting in the optimized task allocation solution.

[0107] In one embodiment, if Figure 6As shown, in step S502, the initial task allocation parameter set is optimized by a mixed integer optimization algorithm, and a task allocation plan that satisfies the line change time, equipment load balancing, and task priority constraints is calculated to obtain an optimized production scheduling plan, including:

[0108] 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.

[0109] Specifically, based on the initial parameter set of task allocation, the optimization objectives are set to minimize the task changeover time, balance the equipment load, and ensure that tasks are completed in order of priority. Among them, minimizing the task changeover time is achieved by optimizing the scheduling order of tasks to maximize the process similarity of adjacent tasks to reduce the accumulation of changeover 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 switching time between task m and task n. The second term is used to balance the equipment load. V 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 task execution time variable U m Indicates the actual start time of task m, and the line change judgment variable Z mnq Indicates whether tasks m and n are executed consecutively on device q. Setting these optimization variables accurately describes the task allocation, execution time, and device load. Next, based on the optimization objective function and optimization variables, constraints are established. The task allocation constraint ensures that each task can only be assigned to one device. The line change time constraint ensures that the line change time between tasks does not exceed a set threshold. The device load constraint limits the maximum number of tasks that can be assigned to a device. The task execution time constraint ensures that tasks are executed in the predetermined order. The task priority constraint ensures that high-priority tasks are not delayed due to interference from low-priority tasks.

[0110] S5022: Iteratively calculate the optimization solution variables through a mixed integer optimization algorithm, adjust the distribution of tasks on equipment, prioritize matching equipment with short line change times, 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, equipment allocation, and line change plan for each production task.

[0111] Specifically, in each iteration, the switching time W of all tasks on the optional equipment is first calculated. mn , and sort all devices according to their changeover time from smallest to largest, giving priority to devices with the shortest changeover time for task allocation. At the same time, calculate the total changeover time of the current task allocation scheme and compare it with the changeover time of the previous iteration. If the optimized changeover time is reduced, retain the new task allocation scheme, otherwise adjust the task allocation retroactively. Next, calculate the current load V of the device q , and adjust task scheduling based on the principle of equipment load balancing, calculate the task load deviation of each device, and adjust 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 while meeting the optimization of line change time. Then, dynamically adjust the execution order based on task priority to ensure that high-priority tasks will not be delayed by low-priority tasks, and calculate the task execution time U under the current task order m , ensuring that the start time of high-priority tasks meets the priority constraints. If the execution time of a task does not meet the priority requirements, the task order is readjusted to prioritize the execution of high-priority tasks. Finally, in each iteration, the objective function value of the current task allocation scheme is calculated and compared with the objective function value of the previous iteration. If the objective function value continues to optimize, the iteration is continued until the optimization converges, ultimately obtaining the optimized task execution time, task allocation equipment, and line change plan.

[0112] S5023: Determine the execution time, equipment allocation, and line change plan for each production task. If the execution time, equipment allocation, and line change plan for each production task meet the line change time, equipment load balancing, and task priority requirements, output the optimized production scheduling plan.

[0113] Specifically, the execution time of each production task is determined, the planned execution time is extracted, and the task priority is checked to see if any low-priority tasks begin executing before higher-priority tasks. If the task order does not meet the priority requirements, the task scheduling order is adjusted to prioritize the execution time of high-priority tasks, ensuring that the task order complies with the scheduling constraints. Next, the task assignments are determined for the equipment to which they are assigned, analyzing the equipment's current task load to ensure that the task assignments do not cause some equipment to be overloaded or idle. If there is an imbalance in equipment load distribution, tasks are reallocated to ensure that tasks are evenly distributed across equipment, avoiding irrational utilization of production resources. Next, the line change plan is evaluated to see if the line change time between tasks meets a set threshold. If the line change time exceeds the limit, the task order is optimized, prioritizing tasks with similar processes to execute adjacently to minimize the impact of line change time on production rhythm. Finally, when the execution times, assigned equipment, and line change plans for all production tasks meet the requirements for line change time optimization, equipment load balancing, and task priority, the optimized production scheduling solution is output.

[0114] In one embodiment, if Figure 7 As shown, after step S60, the production workshop adaptive scheduling method further includes:

[0115] 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 information and real-time line change time change information.

[0116] Specifically, the task execution data in the production process information is analyzed, and the real-time task execution status information is obtained by comparing the current progress, start time and expected completion time of the task with the deviation of the scheduling plan; then, the equipment operation data in the production process information is analyzed, and the real-time equipment load information is obtained by calculating the task occupancy, operation status and load level of the equipment; then, the line change data in the production process information is analyzed, and the real-time line change time change information is obtained by calculating the deviation between the task line change time and the planned line change time.

[0117] S80: Compare the real-time task execution status information, real-time equipment load information and real-time line change time change information with the optimized production scheduling plan to determine whether there is any deviation in the execution time, equipment allocation and line change plan of each production task. When the real-time deviation exceeds the preset deviation threshold, adjust the execution time, equipment allocation and line change plan of each production task and update the optimized production scheduling plan.

[0118] Specifically, the real-time task execution status information is compared with the task execution time in the optimized production scheduling plan, and the difference between the actual completion time and the planned completion time of the task is calculated. If the difference exceeds the preset task execution deviation threshold, it is determined that the task has an execution time deviation. Then, the real-time equipment load information is compared with the equipment allocation in the optimized production scheduling plan, and the difference between the current task load and the expected load of the equipment is calculated. If the difference exceeds the set equipment load deviation threshold, it is determined that the equipment allocation is abnormal. Then, the real-time line change time change information is compared with the line change plan in the optimized production scheduling plan, and the difference between the actual line change time and the planned line change time is calculated. If the difference exceeds the set line change time deviation threshold, it is determined that the line change plan has deviated. Finally, the deviated task execution time, assigned equipment, and line change plan are adjusted, giving priority to adjusting the order of tasks with less line change time impact. If the scheduling optimization goal cannot be met, the task is reallocated to the equipment with lower load, and the task execution time is recalculated to ensure that the adjusted plan meets the task priority and equipment load balancing requirements, and finally the optimized production scheduling plan is updated.

[0119] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0120] In one embodiment, a production workshop adaptive scheduling device is provided, which corresponds one-to-one to the production workshop adaptive scheduling method in the above embodiment. Figure 8 As shown, the 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 functional modules are described in detail as follows:

[0121] Task requirement acquisition module, used to obtain production task requirement information;

[0122] The task and equipment matching module is used to screen equipment that meets the production task requirements 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 capability database, and form a preliminary task and equipment matching plan;

[0123] The task priority calculation module is used to calculate the production task priority based on the preliminary task and equipment matching plan, combined with the order delivery cycle, production batch size and production equipment load in the equipment capacity database in the production task demand information, and form an optimized task execution plan;

[0124] The task scheduling optimization module is used to calculate the line change time between different production tasks based on the optimized task execution plan, combined with the equipment processing capacity, current task status and line change time parameters in the equipment capability database, and adjust the task scheduling sequence according to the line change time to obtain the adjusted task scheduling sequence;

[0125] The task allocation optimization module is used to optimize the adjusted task scheduling sequence using the production task allocation optimization model, calculate the production task allocation plan, determine the execution time of each production task, allocate equipment and line change plan, and generate a production scheduling plan;

[0126] 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.

[0127] Optional task and device matching modules include:

[0128] The equipment preliminary screening submodule is used to select equipment that can process the task from the equipment capability database based on the product type, process requirements, and processing size in the production task requirement information. By comparing the types of products that the equipment can process, equipment that does not meet the processing requirements is eliminated to obtain a preliminary set of optional equipment;

[0129] The task executable screening submodule is used to screen devices that are idle or expected to complete line change within a set time based on the current task status in the preliminary optional device set. By comparing the task occupancy time with the line change time parameter, devices with excessive occupancy time or line change time exceeding the preset line change time threshold are eliminated to obtain the task executable device set;

[0130] The task and equipment matching optimization submodule is used to screen equipment that meets the production task requirements based on the unit time processing capacity and processing accuracy of the task executable equipment set, and combine the historical failure rate of the equipment to obtain a preliminary task and equipment matching plan by giving priority to equipment with fast processing speed, satisfactory accuracy and low historical failure rate.

[0131] Optionally, the task priority calculation module includes:

[0132] The task urgency calculation submodule is used to calculate the remaining delivery time of the production task based on the order delivery cycle in the production task demand information, and calculate the expected completion time of the production task in combination with the equipment processing capacity and current task status in the equipment capability database. By comparing the remaining delivery time of the production task with the expected completion time of the production task, the urgency of the task is determined and a task urgency ranking list is obtained;

[0133] The task batch optimization submodule is used to sort the task list according to the urgency, extract production tasks of the same task type, and determine whether the merging or splitting conditions are met based on the production batch size in the production task demand information. The optimized production batch is obtained by merging the tasks that meet the merging conditions and splitting the tasks that exceed the equipment processing capacity.

[0134] The task execution plan optimization submodule is used to calculate the current task load of each device based on the optimized production batch and production equipment load in the equipment capacity database, and obtain the optimized task execution plan by adjusting the distribution of tasks among different devices.

[0135] Optionally, the task scheduling optimization module includes:

[0136] The task and equipment matching relationship generation submodule is used to extract the execution order of each task based on the optimized task execution plan, and screen the equipment that can execute the task in combination with the equipment processing capabilities in the equipment capability database to form a corresponding relationship between the task and the executable equipment;

[0137] The task changeover time calculation submodule is used to determine the process requirements of adjacent tasks based on the correspondence between tasks and executable equipment, and calculate the changeover time between tasks in combination with the changeover time parameters in the equipment capability database to form a task changeover time matrix;

[0138] The task scheduling optimization submodule is used to adjust the task scheduling sequence in the optimized task execution plan according to the task line change time matrix to obtain the adjusted task scheduling sequence.

[0139] Optionally, the task allocation optimization module includes:

[0140] The task execution constraint extraction submodule is used to extract the execution constraint parameters of the production task based on the adjusted task scheduling sequence, combined with the equipment processable product types, current task status and production load in the equipment capability database, and input the execution constraint parameters into the production task allocation optimization model to obtain the initial parameter set of the task allocation;

[0141] The task allocation optimization calculation submodule is used to optimize the initial parameter set of task allocation through a mixed integer optimization algorithm, calculate the task allocation plan that meets the constraints of line change time, equipment load balancing and task priority, and obtain the optimized production scheduling plan.

[0142] Optionally, the task allocation optimization calculation submodule includes:

[0143] The optimization target and constraint setting unit is used to allocate the initial parameter set according to the task, construct the optimization target function, and set the optimization solution variables and constraint conditions based on the optimization target function;

[0144] The task allocation iterative optimization unit is used to iteratively calculate the optimization solution variables using a mixed integer optimization algorithm, adjust the allocation of tasks on equipment, prioritize equipment with short line change times, optimize task scheduling based on the principle of equipment load balancing, and dynamically adjust the execution order based on task priorities to obtain the execution time, equipment allocation, and line change plan for each production task;

[0145] The optimization result verification and solution output unit is used to judge the execution time, equipment allocation and line change plan of each production task respectively. If the execution time, equipment allocation and line change plan of each production task meet the requirements of line change time, equipment load balance and task priority, the optimized production scheduling plan will be output.

[0146] Optionally, the production control execution module may include:

[0147] The production process monitoring and analysis module is used to monitor the equipment status, task execution progress and production environment parameters of 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 information and real-time line change time change information;

[0148] The production scheduling dynamic adjustment module is used to compare the real-time task execution status information, real-time equipment load information and real-time line change time change information with the optimized production scheduling plan, to determine whether there are deviations in the execution time, equipment allocation and line change plan of each production task, and when the real-time deviation exceeds the preset deviation threshold, adjust the execution time, equipment allocation and line change plan of each production task, and update the optimized production scheduling plan.

[0149] For the specific definition of the production workshop adaptive scheduling device, please refer to the definition of the production workshop adaptive scheduling method above, and will not be repeated here. The various modules in the above-mentioned production workshop adaptive scheduling device can be implemented in whole or in part by software, hardware, and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0150] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 9As shown. The computer device includes a processor, memory, network interface, and database connected via a system bus. 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 computer program in the non-volatile storage medium. The database of the computer device is used as a device capability database. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, it implements a production workshop adaptive scheduling method.

[0151] In one embodiment, a computer device is provided, including 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 following steps are performed:

[0152] Obtain production task demand information;

[0153] By using the pre-established equipment capability database, including the types of products that can be processed by the equipment, the current task status, and the line change time parameters, the equipment that meets the production task requirements is selected to form a preliminary task and equipment matching plan.

[0154] Based on the preliminary task and equipment matching plan, combined with the order delivery cycle, production batch size and production equipment load in the equipment capacity database in the production task demand information, the production task priority is calculated to form an optimized task execution plan;

[0155] Based on the optimized task execution plan, combined with the equipment processing capacity, current task status and line change time parameters in the equipment capacity database, the line change time between different production tasks is calculated, and the task scheduling sequence is adjusted according to the line change time to obtain the adjusted task scheduling sequence;

[0156] Use the production task allocation optimization model to optimize the adjusted task scheduling sequence, calculate the production task allocation plan, determine the execution time of each production task, allocate equipment and line change plan, and generate a production scheduling plan;

[0157] The production scheduling plan is input into the production control system, and the production control system executes the production tasks based on the production scheduling plan.

[0158] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0159] Obtain production task demand information;

[0160] By using the pre-established equipment capability database, including the types of products that can be processed by the equipment, the current task status, and the line change time parameters, the equipment that meets the production task requirements is selected to form a preliminary task and equipment matching plan.

[0161] Based on the preliminary task and equipment matching plan, combined with the order delivery cycle, production batch size and production equipment load in the equipment capacity database in the production task demand information, the production task priority is calculated to form an optimized task execution plan;

[0162] Based on the optimized task execution plan, combined with the equipment processing capacity, current task status and line change time parameters in the equipment capacity database, the line change time between different production tasks is calculated, and the task scheduling sequence is adjusted according to the line change time to obtain the adjusted task scheduling sequence;

[0163] Use the production task allocation optimization model to optimize the adjusted task scheduling sequence, calculate the production task allocation plan, determine the execution time of each production task, allocate equipment and line change plan, and generate a production scheduling plan;

[0164] The production scheduling plan is input into the production control system, and the production control system executes the production tasks based on the production scheduling plan.

[0165] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media used in the various embodiments provided herein may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may 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 (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM).

[0166] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0167] The above-described 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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A production workshop adaptive scheduling method, characterized in that: The production workshop adaptive scheduling method includes: Obtain production task demand information; By using the pre-established equipment capability database, which contains the types of products that can be processed by the equipment, the current task status, and the line change time parameters, the equipment that meets the production task requirements is selected to form a preliminary task and equipment matching plan; Calculate the production task priority based on the preliminary task and equipment matching plan, combined with the order delivery cycle, production batch size in the production task demand information, and the production equipment load in the equipment capability database, to form an optimized task execution plan; Calculating the line change time between different production tasks based on the optimized task execution plan and combining the equipment processing capacity, current task status, and line change time parameters in the equipment capability database, and adjusting the task scheduling sequence based on the line change time to obtain an adjusted task scheduling sequence; Utilizing a production task allocation optimization model to optimize the adjusted task scheduling sequence, calculate a production task allocation plan, determine the execution time of each production task, allocate equipment and a line change plan, and generate a production scheduling plan; The generating of the production scheduling plan includes: Based on the adjusted task scheduling sequence, combined with the types of products that can be processed by the equipment in the equipment capability database, the current task status and the production load situation, the execution constraint parameters of the production task are extracted, and the execution constraint parameters are input into the production task allocation optimization model to obtain an initial task allocation parameter set; The initial parameter set of the task allocation is optimized by a mixed integer optimization algorithm, and a task allocation plan that satisfies the constraints of minimizing line change time, balancing equipment load, and task priority is calculated to obtain an optimized production scheduling plan; The production scheduling plan is input into a production control system, and the production control system executes production tasks based on the production scheduling plan.

2. The production workshop adaptive scheduling method according to claim 1, characterized in that: The equipment that meets the production task requirements is screened 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 capability database, forming a preliminary task and equipment matching plan, including: Based on the product type, process requirements, and processing size in the production task requirement information, screen equipment capable of processing the task from the equipment capability database, and eliminate equipment that does not meet the processing requirements by comparing the types of products that the equipment can process, thereby obtaining a preliminary set of optional equipment; Based on the current task status of the preliminary set of optional devices, devices that are idle or expected to complete line change within a set time are screened, and by comparing the task occupancy time with the line change time parameter, devices with excessively long occupancy time or line change time exceeding a preset line change time threshold are eliminated to obtain a set of task executable devices; Based on the unit time processing capacity and processing accuracy of the set of equipment that can execute the task, the equipment that meets the requirements of the production task is screened, and combined with the historical failure rate of the equipment, the preliminary task and equipment matching plan is obtained by giving priority to equipment with fast processing speed, satisfactory accuracy and low historical failure rate.

3. The production workshop adaptive scheduling method according to claim 1, characterized in that: The production task priority is calculated based on the preliminary task and equipment matching plan, combined with the order delivery cycle, production batch size and production equipment load in the equipment capability database, to form an optimized task execution plan, including: Calculate the remaining delivery time of the production task based on the order delivery cycle in the production task demand information, and calculate the estimated completion time of the production task in combination with the equipment processing capacity in the equipment capability database and the current task status. 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; Extracting production tasks of the same task type based on the task urgency sorting list, and determining whether merging conditions or splitting conditions are met based on the production batch size in the production task demand information, and obtaining optimized production batches by merging tasks that meet the merging conditions and splitting tasks that exceed the equipment processing capacity; According to the optimized production batch and the production equipment load in the equipment capability database, the current task load of each device is calculated, and the optimized task execution plan is obtained by adjusting the distribution of tasks among different devices.

4. The production workshop adaptive scheduling method according to claim 1, characterized in that: The step of calculating the line change time between different production tasks based on the optimized task execution plan and combining the equipment processing capacity, current task status, and line change time parameters in the equipment capability database, and adjusting the task scheduling sequence according to the line change time to obtain the adjusted task scheduling sequence includes: Extracting the execution order of each task based on the optimized task execution plan, and screening the equipment that can execute the task in combination with the equipment processing capabilities in the equipment capability database to form a corresponding relationship between the task and the executable equipment; Determine the process requirements of adjacent tasks based on the correspondence between the tasks and executable equipment, and calculate the line change time between tasks in combination with the line change time parameters in the equipment capability database to form a task line change time matrix; According to the task line switching time matrix, the task scheduling sequence in the optimized task execution plan is adjusted to obtain an adjusted task scheduling sequence.

5. The production workshop adaptive scheduling method according to claim 1, characterized in that: The mixed integer optimization algorithm is used to optimize the initial task allocation parameter set, calculate the task allocation plan that meets the line change time, equipment load balancing and task priority constraints, and obtain the optimized production scheduling plan, including: Assigning an initial parameter set to the task, constructing an optimization objective function, and setting optimization solution variables and constraints based on the optimization objective function; The optimization solution variables are iteratively calculated using the mixed integer optimization algorithm to adjust the distribution of tasks on equipment, prioritize matching equipment with short line change times, optimize task scheduling based on the principle of equipment load balancing, and dynamically adjust the execution order based on task priorities to obtain the execution time of each production task, the assigned equipment, and the line change plan; The execution time of each production task, the allocation equipment and the line change plan are judged separately. If the execution time of each production task, the allocation equipment and the line change plan all meet the line change time, equipment load balancing and task priority requirements, the optimized production scheduling plan is output.

6. The production workshop adaptive scheduling method according to claim 1, characterized in that: The production workshop adaptive scheduling method further includes: Real-time monitoring of equipment status, task execution progress, and production environment parameters in the production workshop to obtain production process information, and analysis of the production process information to obtain real-time task execution status information, real-time equipment load information, and real-time line change time change information; The real-time task execution status information, the real-time equipment load information and the real-time line change time change information are compared with the optimized production scheduling plan to determine whether there are deviations in the execution time of each production task, the allocation equipment and the line change plan. When the real-time deviation exceeds the preset deviation threshold, the execution time of each production task, the allocation equipment and the line change plan are adjusted to update the optimized production scheduling plan.

7. A production workshop adaptive scheduling device, characterized in that: The production workshop adaptive scheduling device includes: Task requirement acquisition module, used to obtain production task requirement information; The task and equipment matching module is used to screen equipment that meets the production task requirements 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 capability database, and form a preliminary task and equipment matching plan; A task priority calculation module is used to calculate the production task priority based on the preliminary task and equipment matching plan, combined with the order delivery cycle, production batch size and production equipment load in the equipment capability database in the production task demand information, to form an optimized task execution plan; A task scheduling optimization module is used to calculate the line change time between different production tasks based on the optimized task execution plan, combined with the equipment processing capacity, current task status and line change time parameters in the equipment capability database, and adjust the task scheduling sequence according to the line change time to obtain an adjusted task scheduling sequence; A task allocation optimization module is used to optimize the adjusted task scheduling sequence using a production task allocation optimization model, calculate a production task allocation plan, determine the execution time of each production task, allocate equipment and line change plans, and generate a production scheduling plan; Task allocation optimization module, including: a task execution constraint extraction submodule, configured to extract the execution constraint parameters of the production task based on the adjusted task scheduling sequence, in combination with the types of products that can be processed by the equipment in the equipment capability database, the current task status, and the production load, and input the execution constraint parameters into the production task allocation optimization model to obtain an initial task allocation parameter set; The task allocation optimization calculation submodule is used to optimize the initial task allocation parameter set through a mixed integer optimization algorithm, calculate the task allocation plan that meets the constraints of minimizing line change time, equipment load balancing and task priority, and obtain the optimized 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 task based on the production scheduling plan.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the production workshop adaptive scheduling method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the production workshop adaptive scheduling method according to any one of claims 1 to 6 are implemented.

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