In-line production arrangement automation implementation method, device, equipment and storage medium

Abstract

The application discloses an in-line production scheduling automation implementation method, device and equipment and a storage medium, relates to the production manufacturing technical field, and comprises the following steps: acquiring scheduling related data, sending a scheduling request to an APS, determining machine tool equipment and pre-adjustment equipment participating in scheduling, and time periods of the equipment participating in scheduling; sequentially performing task verification and resource verification, predicting a state of a part planned production task based on a verification result and a queue being processed by current equipment; scheduling parts planned production tasks performed by each machine tool, and estimating processing time of each part planned production task; when there is a deviation between an actual completion time of the part planned production task and the estimated processing time, rescheduling subsequent part planned production tasks of the machine tool where the current part planned production task is located. The application can improve the plan executability and on-time delivery capability.

Description

Technical Field

[0001] This application relates to the field of manufacturing technology, specifically to a method, apparatus, equipment, and storage medium for automating in-line production scheduling. Background Technology

[0002] In the automated machining of molds and precision parts, enterprises generally use MES (Manufacturing Execution System) to manage work orders and process status, and APS (Advanced Planning and Scheduling) to perform master production scheduling at the factory or shop floor level. Automated production lines typically consist of pallet / rack systems, robotic loading and unloading, CNC (Computer Numerical Control), EDM (Electrical Discharge Machining), CMM (Coordinate Measuring Machine), cleaning equipment, and other workstations. Actual operation depends not only on work orders and process time, but also heavily on real-time conditions on the production line side, such as tool life, NC / CMM / discharge programs, electrode alignment and positioning, whether workpieces have been pre-adjusted and arrived at their designated locations on the line, and whether automated tasks have been issued.

[0003] Existing APS scheduling typically uses planned start time, planned end time, process and equipment resource type as the main inputs, which is insufficient in characterizing the physical existence, resource effectiveness and task executability within the production line. Although automated control systems can execute specific tasks, they lack the ability to integrate APS / MES planning with real-time status within the production line and to predict the task sequence and risk status of each machine tool in the future, resulting in a systematic deviation between planning and on-site executability. Summary of the Invention

[0004] This application provides a method, apparatus, equipment, and storage medium for automating in-line production scheduling, which can improve the feasibility of plans and on-time delivery capabilities.

[0005] In a first aspect, embodiments of this application provide a method for automating in-line production scheduling, the method comprising: Obtain production scheduling data and send a production scheduling request to APS to determine the machine tools and pre-adjusted equipment involved in production scheduling, as well as the time period for each piece of equipment to participate in production scheduling, and generate the planned production tasks of parts and the correspondence between each machine tool. Task verification and resource verification are performed sequentially. Based on the verification results and the queue currently being processed by the equipment, the status of the planned production task of the part is predicted. Based on the priority of the planned production tasks for parts and the APS algorithm, the planned production tasks for each machine tool are scheduled and the processing time for each planned production task is estimated. When there is a discrepancy between the actual completion time and the estimated processing time of the planned production task for a part, the subsequent unplanned production tasks for the machine tool where the current planned production task is located will be rescheduled.

[0006] In conjunction with the first aspect, in one implementation, the step of acquiring production scheduling-related data and sending a production scheduling request to the APS, determining the machine tools and pre-setup equipment participating in the production scheduling, as well as the time period for each piece of equipment to participate in the production scheduling, and generating the correspondence between the planned production tasks of parts and each machine tool, specifically includes: Obtain the planned production tasks of parts associated with the automated production line resources, obtain the machine tool status and equipment resource results reported by the automated production line, obtain the BOM according to the mold number and part number, send a scheduling request to the APS and receive the scheduling results; Based on the configuration of the automated line, determine the machine tools and pre-adjusted equipment to participate in production scheduling, and determine the time period for each piece of equipment to participate in production scheduling. When a piece of equipment does not participate in production scheduling, the planned production task of the part is allocated to other machine tool resources in the same group. Based on the proactive specification of MES order priorities and automated line priorities, the corresponding relationship between the planned production tasks of each part and each machine tool is generated through process-equipment mapping and manufacturability rule matching.

[0007] In conjunction with the first aspect, in one implementation method, The task verification is as follows: if the part production task in the APS scheduling plan has no parts in the automated line, the task verification fails, the reason for the failure is recorded, and resource verification is no longer performed. The resource verification process involves verifying the equipment production resources corresponding to the part production plan task according to the process type for the part production plan task. If a certain item fails the verification, the reason for the failure is recorded.

[0008] In conjunction with the first aspect, in one implementation method, The status of the planned production task for the parts includes: in progress, in progress but lacking resources, pending processing, pending processing but lacking resources, no task, and completed. The part being processed is a planned production task that is currently being machined. The lack of resources in the processing refers to the fact that the planned production task of the parts has been started but there is a lack of equipment production resources; The parts to be processed are already on the shelf and there is an automated task, and the equipment production resources are sufficient; The lack of resources to be processed refers to a part that is already on the shelf and has an automated task, but it is predicted that the equipment production resources will not be sufficient when the part is processed. The term "no task" refers to a production schedule that exists but a processing task has not yet been established. The planned production task for the parts has been completed.

[0009] In conjunction with the first aspect, in one implementation method, For part production plans with a quantity greater than 1 in the MES plan, the part production plans are split or aggregated according to the status of each part production plan to obtain multiple sub-tasks, and task verification and resource verification are performed on each sub-task. For EDM tasks, the EDM task is split into subtasks according to the granularity of the discharge task, and task verification and resource verification are performed on each subtask.

[0010] In conjunction with the first aspect, in one implementation, the step of scheduling planned production tasks for each machine tool and estimating the processing time for each planned production task based on the priority of the planned production tasks and the APS algorithm specifically includes: Based on the priority of the planned production tasks and the APS algorithm, the planned production tasks of each machine tool are scheduled. The material change time is added between adjacent planned production tasks. If a part does not arrive at the site, the planned production task corresponding to that part is skipped and the machine tool is released to the next planned production task. Based on work order hours, MES system or standard default data tables of automated production lines, the processing time for each part's planned production task is estimated.

[0011] In conjunction with the first aspect, in one implementation method, The method for automating in-line production scheduling also includes outputting results. The output results include the output of task blocks, estimated duration, prediction status, task verification and resource verification results corresponding to each machine tool time axis, as well as the pre-adjustment tasks of the parts plan that need to be pre-adjusted for pre-adjustment scheduling.

[0012] Secondly, embodiments of this application provide an in-line production scheduling automation device, the in-line production scheduling automation device comprising: The matching module is used to acquire production scheduling-related data and send production scheduling requests to the APS, determine the machine tools and pre-adjusted equipment participating in production scheduling, as well as the time period for each piece of equipment to participate in production scheduling, and generate the correspondence between the planned production tasks of parts and each machine tool. The prediction module is used to perform task verification and resource verification in sequence, and based on the verification results and the queue that the equipment is currently processing, it predicts the status of the planned production task of the part. The time estimation module is used to schedule the planned production tasks of each machine tool based on the priority of the planned production tasks and the APS algorithm, and to estimate the processing time of each planned production task. The production scheduling correction module is used to reschedule subsequent unfinished production tasks for the machine tool where the current planned production task is located when there is a deviation between the actual completion time and the estimated processing time of the planned production task for the part.

[0013] Thirdly, embodiments of this application provide an in-line production scheduling automation implementation device, the in-line production scheduling automation implementation device including a processor, a memory, and an in-line production scheduling automation implementation program stored in the memory and executable by the processor, wherein when the in-line production scheduling automation implementation program is executed by the processor, it implements the steps of the in-line production scheduling automation implementation method described above.

[0014] Fourthly, embodiments of this application provide a computer-readable storage medium storing an in-line production scheduling automation implementation program, wherein when the in-line production scheduling automation implementation program is executed by a processor, it implements the steps of the in-line production scheduling automation implementation method described above.

[0015] The beneficial effects of the technical solutions provided in this application include: (1) Improve the feasibility and on-time delivery capability of the plan: the two-layer verification and five predictive states will structure the unexecutable section and reduce the blind queuing of missing programs, tools, electrodes and tasks; (2) Reduce work-in-process inventory and equipment waiting time: The prediction time axis calculates processing time, material change, task skipping and relocation of equipment in the same group of tasks, so that the prediction plan is closer to the actual cycle time of the production line; (3) Improve the foresight of resource preparation: Output downtime, skips and corresponding parts production plan details in the production scheduling forecast to facilitate the advance arrangement of tool preparation, electrode preparation and pre-adjustment; (4) Enhance the interpretability of exceptions: When a task fails, a reason explanation is provided to facilitate problem finding and integration with other systems; (5) Scalable and configurable: Resource verification items are parameterized by process type, and the triggering recalculation methods include manual, periodic and event-driven; (6) Decoupling of prediction and actual control and reducing production line risk: Prediction does not replace actual production on site; correction is triggered according to actual time after actual production; priority and on-site conditions can be rewritten when needed. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the method for automating production scheduling within the production line as described in this application. Figure 2 A schematic diagram of the overall information flow architecture for data access, scheduling interaction, and structured output; Figure 3 A schematic diagram illustrating task verification, resource verification, and prediction status; Figure 4 A diagram illustrating priority sorting, multi-item splitting, and EDM multi-task splitting; Figure 5 A diagram illustrating the machine tool equipment participation period, material change time, migration of tasks within the same group, and skipping rules; Figure 6 A readable diagram illustrating the output of timeline progression and task skipping; Figure 7 A schematic diagram illustrating the decoupling of the predicted path from the actual execution path and the recalculation of triggering conditions; Figure 8 This is a schematic diagram of the functional modules of the in-line scheduling automation device of this application; Figure 9 This is a schematic diagram of the hardware structure of the equipment for automating production scheduling within the production line, as described in this application. Detailed Implementation

[0017] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0018] First, some of the technical terms used in this application will be explained to help those skilled in the art understand this application.

[0019] Equipment resources: The equipment required for processing order tasks; equipment resource groups can be configured according to the actual site conditions, and the equipment in the group can handle the same task.

[0020] Equipment production resources: These include the necessary resources required for machining parts, such as production data, work orders, programs, tooling fixtures, cutting tools, and electrodes. Production resource effectiveness refers to the availability of resources, such as whether tool life is sufficient and whether the program is effective.

[0021] Processing tasks: Processing tasks are pre-adjusted and processed according to orders. They support combinations such as multiple pieces, single pieces, or multiple pieces split, and are identified by chips or barcodes as unique information, corresponding to the binding relationship between the pallet and the parts.

[0022] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0023] In a first aspect, embodiments of this application provide a method for automating in-line production scheduling.

[0024] In one embodiment, reference is made to Figure 1 , Figure 1 This is a flowchart illustrating the method for automating production scheduling within the production line as described in this application. Figure 1 As shown, the methods for automating in-line production scheduling include: S1: Obtain production scheduling data and send a production scheduling request to APS, determine the machine tools and pre-adjusted equipment involved in production scheduling, as well as the time period for each piece of equipment to participate in production scheduling, and generate the planned production tasks of parts and the corresponding relationship between each machine tool. S2: Perform task verification and resource verification sequentially. Based on the verification results and the queue that the equipment is currently processing, predict the status of the planned production task of the part. S3: Based on the priority of the planned production tasks of the parts and the APS algorithm, schedule the planned production tasks of each machine tool and estimate the processing time of each planned production task of the parts. S4: When there is a discrepancy between the actual completion time and the estimated processing time of a planned part production task, the subsequent unfinished planned part production tasks on the machine tool where the current planned part production task is located are rescheduled. That is, when there is a discrepancy between the actual completion time and the estimated processing time of a planned part production task, the subsequent unfinished planned part production tasks on the machine tool where the current planned part production task is located are either completely shifted and refreshed for scheduling, or the planned part production tasks scheduled for each machine tool are rescheduled based on the priority of the planned part production tasks and the APS algorithm, and the processing time of each planned part production task is estimated. See also Figure 2 The diagram shown illustrates the overall information flow architecture for data access, scheduling interaction, and structured output. (See also...) Figure 7 The diagram shown illustrates the decoupling of the predicted path from the actual execution path and the recalculation of triggering conditions.

[0025] Specifically, based on the overall workshop scheduling or other production plan data, the time and resources for the automated production line tasks are calculated a second time. First, allocation is performed according to the MES order production plan and available equipment. Then, the feasibility of available equipment, pre-setup resources, and other digital resources on the automated production line is marked. Each planned production task for a part is assigned a predicted status enumeration value and anomaly details, ensuring that unexecutable situations such as "resource shortage" and "no task" are reflected in the prediction, rather than simply allocating tasks based on ideal cycle times. Simultaneously, the prediction is decoupled from the actual machine tool operation; the prediction results can be updated due to actual unforeseen circumstances without affecting the actual machine tool operation.

[0026] Furthermore, in one embodiment, production scheduling-related data is acquired and a production scheduling request is sent to the APS (Advanced Planning and Scheduling System) to determine the machine tools and pre-set equipment participating in the production scheduling, as well as the time period for each piece of equipment to participate in the production scheduling, and to generate the correspondence between the planned production tasks of parts and each machine tool, specifically including: S101: Obtain the planned production tasks of parts associated with the automated production line resources, obtain the machine tool status and equipment resource results reported by the automated production line, obtain the BOM (Bill of Material) according to the mold number and part number, send a scheduling request to APS and receive the scheduling results; S102: Determine the machine tools and pre-adjusted equipment to participate in production scheduling based on the configuration of the automated line, and determine the time period for each piece of equipment to participate in production scheduling. When a piece of equipment does not participate in production scheduling, the planned production task of the parts is allocated to other machine tool resources in the same group to ensure the completion of the production of the ordered parts. S103: Based on the MES order priority and the priority actively specified within the automated line (i.e., the priority is manually specified within the automated line), the corresponding relationship between the planned production task of each part and each machine tool is generated through process-equipment mapping and manufacturability rule matching.

[0027] Furthermore, in one embodiment, task verification is performed as follows: if the part production task in the APS scheduling plan has no parts in the automated line, the task verification fails, the reason for failure is recorded, and resource verification is no longer performed; wherein, task verification is the first layer of verification, and resource verification is the second layer of verification. When the first layer of verification fails, the second layer of verification is no longer performed. The resource verification process involves verifying each planned production task for a part that has passed task verification, item by item, the equipment production resources corresponding to the planned production task are checked according to the process type. If any item fails verification, the reason for failure is recorded, including but not limited to: not pre-tuned, missing program, missing tool, insufficient tool life, and missing discharge electrode. (See also...) Figure 3 The diagram shown illustrates the task verification, resource verification, and prediction status.

[0028] Furthermore, in one embodiment, the status of the planned production task for the part includes processing, processing without resources, waiting to be processed, waiting to be processed without resources, no task, and completed.

[0029] "Processing in progress" means the planned production task for the part is currently being processed; "Processing in progress but lacking resources" means the planned production task for the part has been started but lacks the necessary equipment production resources. For example, in a discharge scenario, some electrodes are not on the production line or are not being processed in complete sets, requiring a notification of the missing equipment production resources; "Pending processing" means the part is already on the shelf and has an automated task, and the equipment production resources are sufficient (tool life, program existence, etc.); "Pending processing but lacking resources" means the part is already on the shelf and has an automated task, but the predicted equipment production resources will not be sufficient when processing the part (tool life is not suitable, no processing program, discharge task has been created but there are insufficient electrodes, etc.); "No task" means there is a production schedule but the processing task has not yet been created (e.g., the part has not arrived at the production line shelf, or the steel part is on the shelf but no discharge task has been created); "Completed" means the planned production task for the part has been completed.

[0030] It should be noted that, under the task verification and resource verification framework, multiple tasks are split based on the verification results: for example, when there are multiple quantities of the same part, they are split into multiple items or merged and displayed according to the status such as pending processing or no task, and are consistent with the Gantt chart color blocks and details.

[0031] Furthermore, in one embodiment, for part production plans with a quantity greater than 1 in the MES plan, the part production plans are split or aggregated according to the status of each part production plan to obtain multiple sub-tasks, and task verification and resource verification are performed on each sub-task. For EDM tasks, the EDM task is broken down into subtasks at the discharge task granularity, and each subtask undergoes task verification and resource verification. See also Figure 4 The diagram shown illustrates priority sorting, multi-item splitting, and EDM multi-task splitting.

[0032] Furthermore, in one embodiment, based on the priority of the planned production tasks for parts and the APS algorithm, the planned production tasks for each machine tool are scheduled and the processing time for each planned production task is estimated, specifically including: S301: Based on the priority of the planned production tasks and the APS algorithm, schedule the planned production tasks for each machine tool. A changeover time is added between adjacent planned production tasks. If a part has not arrived at the production site, the planned production task corresponding to that part is skipped, and the machine tool is released for the next planned production task. See also... Figure 5 The diagram illustrates the machine tool equipment's participation time period, material change time, task migration within the same group, and skipping rules. (See also...) Figure 6 The diagram shown illustrates the readable format of the timeline progression and task skipping output.

[0033] Specifically, each machine tool has its own timeline task (in Gantt chart form, where a machine tool will have multiple planned production tasks for parts in sequence within a certain period of time, and each planned production task for a part is represented by a color block). Therefore, based on the priority of the planned production tasks for parts and the APS algorithm, the planned production tasks for each machine tool are scheduled. The material changeover time is added between adjacent planned production tasks for parts (the same machine tool will schedule multiple planned production tasks for parts, and two planned production tasks for parts in sequence are adjacent tasks). If a part has not arrived at the site, the planned production task for that part is skipped, the machine tool is released for the next planned production task for the part, and the reason why the planned production task for the part was skipped is explained. Before generating the planned production tasks for parts and their correspondence with each machine tool, all planned production tasks for parts are sorted. S302: Based on work order hours, MES system, or standard default data table of the automated production line, estimate the processing time for each part's planned production task. Specifically, when estimating processing time, prioritize work order hours; if none are available, use the MES system's estimated time; if neither is available, use the value from the automated production line's standard default data table. When multiple parts are bound to a pallet, the estimated time can be accumulated or multiplied by a factor. To correct for errors, the estimated time can be brought closer to the actual time by multiplying the time of the parts to be processed by a coefficient based on historical production time records.

[0034] Furthermore, in one embodiment, the in-line scheduling automation implementation method of this application further includes outputting results; the output of results includes outputting the task blocks corresponding to the time axis of each machine tool (each machine tool will be assigned multiple part production plans in sequence, and each part production plan is a task block), estimated duration, prediction status, task verification and resource verification results, as well as the part plan pre-adjustment tasks that need to be pre-adjusted for the pre-adjustment station scheduling.

[0035] Furthermore, the method for automating production scheduling within the production line in this application also includes mapping the APS plan to records bound by chip codes within the automated production line, passing the mapping results through equipment and strategy filters, and outputting reasons if execution fails; when the machine tool will stop and which parts' planned production tasks will be skipped are written into a rejection reason dictionary after steps. Stoppages and skips are deterministically derived from the timeline extrapolator's consumption filtering results and resource verification results.

[0036] The method for automating in-line production scheduling in this application, in practical applications: (1) Solve the problem of disconnect between MES / APS planning and production line executability. Under the premise of existing scheduling, combine the real-time data of the production line to perform secondary scheduling and prediction of machine tool tasks in the line, so that the planned sequence is consistent with the actual processing conditions such as arrival at the production line, availability of programs and tools / electrodes.

[0037] (2) Solve the problem of insufficient predictability under multiple resource constraints. For different process types such as CNC, EDM, and CMM, perform task existence verification and resource validity verification respectively, and output distinguishable prediction status and abnormal reasons.

[0038] (3) Solve the problem of complex rules for order insertion, machine tool loading / unloading, task skipping, and downtime waiting. The prediction reflects business rules such as priority of urgent orders, skipping when workpieces have not entered the line, skipping when EDM electrodes are not fully matched, or downtime waiting, and supports parametric modeling of material change time, machine tool participation in production scheduling, etc.

[0039] (4) Solve the granularity problem of multiple workpieces. When there are multiple quantities of the same module number, the display and production scheduling are broken down according to the status of completed, in process, pending processing, not yet on the line, etc., and are consistent with the Gansu Special Block and Details display.

[0040] (5) Solve the problem of production scheduling refresh and correction. Under the premise that online production scheduling does not interrupt actual control, the forecast can be made to converge with the changes on site through refresh and time correction, such as task migration after machine tool abnormality, and time adjustment after the deviation between actual completion and forecast.

[0041] (6) Solve the problem of project implementation. While adopting the APS scheduling interface, integrate the semi-automatic interface to the chip number dimension, provide reasons for skipping and filtering, support the synchronization of results to the priority of automated tasks, and present indicators such as availability, utilization, and uptime on a Kanban board.

[0042] Secondly, embodiments of this application also provide an in-line production scheduling automation device.

[0043] In one embodiment, reference is made to Figure 8 , Figure 8 This is a schematic diagram of the functional modules of the in-line scheduling automation device of this application. Figure 8 As shown, the in-line production scheduling automation device includes: a matching module, a prediction module, a time estimation module, and a scheduling correction module.

[0044] The matching module acquires scheduling-related data and sends scheduling requests to the APS (Advanced Planning and Scheduling System), determines the machine tools and pre-set equipment participating in scheduling, as well as the time periods for each equipment to participate in scheduling, and generates the correspondence between planned production tasks and machine tools. The prediction module performs task verification and resource verification sequentially, and predicts the status of planned production tasks based on the verification results and the queues currently being processed by the equipment. The time estimation module schedules planned production tasks for each machine tool according to the priority of planned production tasks and the APS algorithm, and estimates the processing time of each planned production task. The scheduling correction module reschedules subsequent unexecuted planned production tasks for the machine tool where the current planned production task is located when there is a deviation between the actual completion time and the estimated processing time of the planned production task.

[0045] Thirdly, embodiments of this application provide an in-line production scheduling automation device, which can be a personal computer (PC), laptop computer, server, or other device with data processing capabilities.

[0046] Reference Figure 9 , Figure 9This is a schematic diagram of the hardware structure of the in-line production scheduling automation device involved in the embodiments of this application. In the embodiments of this application, the in-line production scheduling automation device may include a processor, a memory, a communication interface, and a communication bus.

[0047] The communication bus can be of any type and is used to interconnect the processor, memory, and communication interface.

[0048] Communication interfaces include input / output (I / O) interfaces, physical interfaces, and logical interfaces used for interconnecting components within the automated production scheduling equipment, as well as interfaces used for interconnecting the automated production scheduling equipment with other devices (such as other computing devices or user equipment). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user equipment can be displays, keyboards, etc.

[0049] Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0050] The processor can be a general-purpose processor, which can call the in-line production scheduling automation implementation program stored in the memory and execute the in-line production scheduling automation implementation method provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the in-line production scheduling automation implementation program is called can be referred to in the various embodiments of the in-line production scheduling automation implementation method of this application, and will not be repeated here.

[0051] Those skilled in the art will understand that Figure 9 The hardware structure shown does not constitute a limitation of this application and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0052] Fourthly, embodiments of this application also provide a computer-readable storage medium.

[0053] The present application stores an in-line production scheduling automation implementation program on a computer-readable storage medium, wherein when the in-line production scheduling automation implementation program is executed by a processor, it implements the steps of the in-line production scheduling automation implementation method as described above.

[0054] The method implemented when the in-line production scheduling automation implementation program is executed can be referred to in the various embodiments of the in-line production scheduling automation implementation method of this application, and will not be repeated here.

[0055] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.

[0056] In the description of the embodiments of this application, terms such as "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a concrete manner.

[0057] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0058] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0059] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0060] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for automating in-line production scheduling, characterized in that, The method for automating in-line production scheduling includes: Obtain production scheduling data and send a production scheduling request to APS to determine the machine tools and pre-adjusted equipment involved in production scheduling, as well as the time period for each piece of equipment to participate in production scheduling, and generate the planned production tasks of parts and the correspondence between each machine tool. Task verification and resource verification are performed sequentially. Based on the verification results and the queue currently being processed by the equipment, the status of the planned production task of the part is predicted. Based on the priority of the planned production tasks for parts and the APS algorithm, the planned production tasks for each machine tool are scheduled and the processing time for each planned production task is estimated. When there is a discrepancy between the actual completion time and the estimated processing time of the planned production task for a part, the subsequent unplanned production tasks for the machine tool where the current planned production task is located will be rescheduled.

2. The method for automating in-line production scheduling as described in claim 1, characterized in that, The process of acquiring production scheduling data and sending a production scheduling request to the APS (Advanced Planning and Scheduling System), determining the machine tools and pre-set equipment participating in the production scheduling, the time period for each piece of equipment to participate in the scheduling, and generating the correspondence between planned production tasks and each machine tool specifically includes: Obtain the planned production tasks of parts associated with the automated production line resources, obtain the machine tool status and equipment resource results reported by the automated production line, obtain the BOM according to the mold number and part number, send a scheduling request to the APS and receive the scheduling results; Based on the configuration of the automated line, determine the machine tools and pre-adjusted equipment to participate in production scheduling, and determine the time period for each piece of equipment to participate in production scheduling. When a piece of equipment does not participate in production scheduling, the planned production task of the part is allocated to other machine tool resources in the same group. Based on the proactive specification of MES order priorities and automated line priorities, the corresponding relationship between the planned production tasks of each part and each machine tool is generated through process-equipment mapping and manufacturability rule matching.

3. The method for automating in-line production scheduling as described in claim 1, characterized in that: The task verification is as follows: if the part production task in the APS scheduling plan has no parts in the automated line, the task verification fails, the reason for the failure is recorded, and resource verification is no longer performed. The resource verification process involves verifying the equipment production resources corresponding to the part production plan task according to the process type for the part production plan task. If a certain item fails the verification, the reason for the failure is recorded.

4. The method for automating in-line production scheduling as described in claim 1, characterized in that: The status of the planned production task for the parts includes: in progress, in progress but lacking resources, pending processing, pending processing but lacking resources, no task, and completed. The part being processed is a planned production task that is currently being machined. The lack of resources in the processing refers to the fact that the planned production task of the parts has been started but there is a lack of equipment production resources; The parts to be processed are already on the shelf and there is an automated task, and the equipment production resources are sufficient; The lack of resources to be processed refers to a part that is already on the shelf and has an automated task, but it is predicted that the equipment production resources will not be sufficient when the part is processed. The term "no task" refers to a production schedule that exists but a processing task has not yet been established. The planned production task for the parts has been completed.

5. The method for automating in-line production scheduling as described in claim 1, characterized in that: For part production plans with a quantity greater than 1 in the MES plan, the part production plans are split or aggregated according to the status of each part production plan to obtain multiple sub-tasks, and task verification and resource verification are performed on each sub-task. For EDM tasks, the EDM task is split into subtasks according to the granularity of the discharge task, and task verification and resource verification are performed on each subtask.

6. The method for automating in-line production scheduling as described in claim 1, characterized in that, The process of scheduling planned production tasks for each machine tool and estimating the processing time for each planned production task, based on the priority of the planned production tasks and the APS algorithm, specifically includes: Based on the priority of the planned production tasks and the APS algorithm, the planned production tasks of each machine tool are scheduled. The material change time is added between adjacent planned production tasks. If a part does not arrive at the site, the planned production task corresponding to that part is skipped and the machine tool is released to the next planned production task. Based on work order hours, MES system or standard default data tables of automated production lines, the processing time for each part's planned production task is estimated.

7. The method for automating in-line production scheduling as described in claim 1, characterized in that: The method for automating in-line production scheduling also includes outputting results. The output results include the output of task blocks, estimated duration, prediction status, task verification and resource verification results corresponding to each machine tool time axis, as well as the pre-adjustment tasks of the parts plan that need to be pre-adjusted for pre-adjustment scheduling.

8. An automated production scheduling device, characterized in that, The automated production scheduling device includes: The matching module is used to acquire production scheduling-related data and send production scheduling requests to the APS, determine the machine tools and pre-adjusted equipment participating in production scheduling, as well as the time period for each piece of equipment to participate in production scheduling, and generate the correspondence between the planned production tasks of parts and each machine tool. The prediction module is used to perform task verification and resource verification in sequence, and based on the verification results and the queue that the equipment is currently processing, it predicts the status of the planned production task of the part. The time estimation module is used to schedule the planned production tasks of each machine tool based on the priority of the planned production tasks and the APS algorithm, and to estimate the processing time of each planned production task. The production scheduling correction module is used to reschedule subsequent unfinished production tasks for the machine tool where the current planned production task is located when there is a deviation between the actual completion time and the estimated processing time of the planned production task for the part.

9. An automated production scheduling device, characterized in that, The in-line production scheduling automation implementation device includes a processor, a memory, and an in-line production scheduling automation implementation program stored in the memory and executable by the processor, wherein when the in-line production scheduling automation implementation program is executed by the processor, it implements the steps of the in-line production scheduling automation implementation method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores an in-line production scheduling automation implementation program, wherein when the in-line production scheduling automation implementation program is executed by a processor, it implements the steps of the in-line production scheduling automation implementation method as described in any one of claims 1 to 7.

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