Production line matching method and device, computer device and computer storage medium

Abstract

The application relates to a production line matching method and device, computer equipment and a computer storage medium. The method comprises the following steps: obtaining object design information of a to-be-scheduled object and production line processing capacity information of each production line; performing hard constraint matching of the to-be-scheduled object and each production line according to the object design information and the production line processing capacity information, and obtaining a hard constraint matching result, wherein the hard constraint matching result is used to represent whether there is a preparation production line, and the preparation production line is a production line that meets the hard constraint condition of the to-be-scheduled object; the hard constraint condition comprises that the product parameter range of the production line covers the object design parameter of the to-be-scheduled object. In the case that there is a preparation production line, a target scheduling production line is determined from each preparation production line based on the production line occupation condition of each preparation production line, and a scheduling scheme of the to-be-scheduled object is generated according to the target scheduling production line. The method can improve scheduling efficiency and production line utilization.

Description

Technical Field

[0001] This application relates to the field of intelligent manufacturing technology, and in particular to a production line matching method, apparatus, computer equipment, and computer storage medium. Background Technology

[0002] In traditional iron core production, the selection of production lines and scheduling decisions usually rely on manual experience and verbal communication. Specifically, process engineers first need to ask designers for the dimensional parameters of the iron cores to be produced, and then consult with manufacturers to understand the production status of each production line. Based on the information obtained, they arrange the production sequence and allocate production lines for the iron cores based on their personal experience.

[0003] This method, relying on manual experience and verbal communication, has significant shortcomings: Firstly, the information transmission chain is long and involves many steps, making it prone to parameter omissions, misunderstandings, or transmission delays, affecting the accuracy of production line matching. Secondly, process engineers need to frequently communicate across different positions, and the scheduling results heavily depend on personal experience, leading to lengthy scheduling times and low overall production efficiency. Furthermore, when production line status or order demands change, it is difficult to respond quickly and re-optimize the scheduling plan, which to some extent reduces the utilization rate of existing production lines. Summary of the Invention

[0004] Therefore, it is necessary to provide a production line matching method, apparatus, computer equipment, and computer storage medium that can improve production scheduling efficiency and production line utilization in response to the above-mentioned technical problems.

[0005] Firstly, this application provides a production line matching method, the method comprising:

[0006] Obtain the object design information of the objects to be produced, as well as the processing capacity information of each production line; the object design information is used to describe the physical and technical specifications of the objects to be produced, including at least one object design parameter; the production line processing capacity information is used to describe the range of product parameters and limitations that the corresponding production line can process.

[0007] Based on the object design information and the processing capacity information of each production line, the objects to be scheduled for production are matched with each production line using hard constraints to obtain hard constraint matching results. The hard constraint matching results are used to characterize whether there are any reserve production lines. Reserve production lines are those production lines that meet the hard constraint conditions with the objects to be scheduled for production. The hard constraint conditions include that the product parameter range of the production line covers the object design parameters of the objects to be scheduled for production.

[0008] In the case of a backup production line, the target production line is determined from the backup production line based on the first information; the first information includes the idle time of the backup production line.

[0009] Generate a production schedule plan for the objects to be scheduled based on the target production line.

[0010] In one embodiment, the first information also includes the expected delivery time of the object to be scheduled for production, the historical design information and historical production man-hours of each historical production object;

[0011] Based on the initial information, the target production line is determined from each of the prepared production lines, including:

[0012] Obtain the expected delivery time of the production objects to be scheduled, as well as the historical design information and historical production man-hours of each historical production object;

[0013] Based on the object design information and historical design information, similar production objects that meet the similarity conditions with the object to be scheduled are identified from each historical production object. The similarity conditions include that the feature distance between the object design features represented by the object design information and the historical object design features represented by the corresponding historical design information is less than a preset distance threshold.

[0014] Based on the historical production hours of similar production scheduling objects, determine the estimated production hours of the objects to be scheduled.

[0015] Based on the estimated production man-hours and expected delivery time, determine the expected production scheduling time window for the items to be scheduled.

[0016] Based on the expected production scheduling time window and the idle time of the reserve production line, if it is determined that there is an idle production line among the reserve production lines, the target production line for the object to be scheduled is determined according to the idle production line.

[0017] In one embodiment, the method further includes:

[0018] If there are no idle production lines in the standby production lines, candidate standby tasks are determined from the standby production lines according to the expected production scheduling time window;

[0019] Obtain the pre-task scheduling information of the candidate pre-tasks, as well as the object priority of the objects to be scheduled;

[0020] Based on the pre-task scheduling information and object priority, if the candidate pre-tasks meet the pre-emption conditions, the pre-emption strategy for the candidate pre-tasks is determined based on the pre-task scheduling information.

[0021] The production line to which the candidate preparatory task that meets the preemption conditions belongs is identified as the preemption production line;

[0022] Based on the preemption strategy and the preemption of production lines, a production scheduling plan is generated for the objects to be scheduled.

[0023] In one embodiment, the preparatory task scheduling information includes the preparatory task priority of the candidate preparatory tasks and the remaining delivery time of the tasks;

[0024] The method also includes:

[0025] If the ratio of the priority of the object to be scheduled to the priority of the preparatory task exceeds the preset weight ratio threshold, obtain the minimum remaining delivery time of the candidate preparatory task.

[0026] If the remaining delivery time of a task is greater than or equal to the minimum remaining delivery time, then the candidate pre-task is determined to meet the preemption condition.

[0027] In one embodiment, the method further includes:

[0028] In the absence of a pre-existing production line, at least one design parameter to be adjusted is determined from the object design information based on the hard constraint matching results.

[0029] From multiple candidate parameter adjustment strategies, determine the target parameter adjustment strategy that matches the design parameter to be adjusted;

[0030] Based on the target parameter adjustment strategy and the design parameters to be adjusted, parameter adjustment suggestions are generated and fed back to the user terminal.

[0031] In response to the parameter adjustment instruction triggered by the parameter adjustment suggestion, update the design parameters to be adjusted to obtain the updated object design information;

[0032] Based on the updated object design information, the process returns to the step of performing hard constraint matching between the objects to be scheduled and each production line, according to the object design information and the processing capacity information of each production line, to obtain the hard constraint matching results.

[0033] In one embodiment, determining a target parameter adjustment strategy that matches the design parameter to be adjusted from a plurality of candidate parameter adjustment strategies includes:

[0034] From multiple candidate parameter adjustment strategies, select multiple parameter adjustment strategies that match the design parameters to be adjusted;

[0035] Based on the historical adoption rate of each selected parameter adjustment strategy, the selected parameter adjustment strategy with the highest historical adoption rate is determined as the target parameter adjustment strategy that matches the design parameter to be adjusted.

[0036] In one embodiment, the method further includes:

[0037] Based on the production scheduling plan of the objects to be scheduled, generate a multi-level approval process;

[0038] In response to the approval event triggered by the multi-level approval process, the object to be scheduled for production is set as the production line task of the target production line and bound to the corresponding production time period in the target production line.

[0039] Secondly, this application also provides a production line matching device, the device comprising:

[0040] The information acquisition module is used to acquire the object design information of the objects to be produced, as well as the processing capacity information of each production line. The object design information is used to describe the physical and technical specifications of the objects to be produced, including at least one object design parameter. The production line processing capacity information is used to describe the range of product parameters and limitations that the corresponding production line can process.

[0041] The hard constraint matching module is used to perform hard constraint matching between the object to be scheduled and each production line based on the object design information and the processing capacity information of each production line, and obtain the hard constraint matching result. The hard constraint matching result is used to identify whether there is a backup production line. The backup production line is the production line that meets the hard constraint conditions with the object to be scheduled. The hard constraint conditions include that the product parameter range of the production line covers the object design parameters of the object to be scheduled.

[0042] The target production line determination module is used to determine the target production line from the reserve production lines based on first information when there are reserve production lines; the first information includes the idle time period of the reserve production lines.

[0043] The production scheduling plan generation module is used to generate production scheduling plans for objects to be scheduled based on the target production line.

[0044] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method.

[0045] Fourthly, this application also provides a computer storage medium on which a computer program is stored, a memory storing the computer program, and the computer program, when executed by a processor, implements the steps of the above-described method.

[0046] The aforementioned production line matching method, apparatus, computer equipment, and computer storage medium allow the production line matching system to independently acquire the object design information of the product to be scheduled, as well as the processing capacity information of each production line. Based on the object design information and the processing capacity information of each production line, the system performs hard constraint matching between the product to be scheduled and each production line, obtaining hard constraint matching results. These results indicate the existence of reserve production lines. Reserve production lines are those that meet the hard constraint conditions with the product to be scheduled. These hard constraint conditions include the production line's processable product parameter range covering the object design parameters of the product to be scheduled. If reserve production lines exist, the system determines the target production line based on their idle time periods, and finally generates a production schedule plan for the product to be scheduled based on the target production line. The above method, on the one hand, because the hard constraints include the product parameter range of the production line covering the object design parameters of the object to be scheduled, can replace the traditional subjective judgment method that relies on personal experience with a technical solution that compares the object design parameters with the corresponding production line parameter range. This reduces the repeated communication and trial-and-error costs required for manual scheduling and effectively improves the efficiency of production line matching. On the other hand, even if the production line status or order demand changes, the production line matching system can quickly respond and re-optimize the scheduling plan, effectively improving the utilization rate of existing production lines. Attached Figure Description

[0047] Figure 1 This is a diagram illustrating the application environment of the production line matching method in one embodiment;

[0048] Figure 2 This is a diagram illustrating the application environment of the production line matching method in another embodiment;

[0049] Figure 3 This is a flowchart illustrating a production line matching method in one embodiment;

[0050] Figure 4 This is a flowchart illustrating the process of determining the target production line from each of the preparatory production lines based on their respective production line occupancy status in one embodiment.

[0051] Figure 5 This is a flowchart illustrating the production line matching method in another embodiment;

[0052] Figure 6 This is a flowchart illustrating the production line matching method in another embodiment;

[0053] Figure 7 This is a schematic diagram of the architecture of a production line matching system in one embodiment;

[0054] Figure 8 This is a schematic diagram illustrating the storage information of each database in one embodiment;

[0055] Figure 9 This is a flowchart illustrating the production line matching method in another embodiment;

[0056] Figure 10 This is a flowchart illustrating a multi-level approval process in one embodiment;

[0057] Figure 11 This is a structural block diagram of a production line matching device in one embodiment;

[0058] Figure 12 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0060] The production line matching method provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, the production line matching system 102 communicates with the server 104 via a network. A data storage system can store the data that the production line matching system 102 needs to process. The data storage system can be integrated onto the server 104, or it can be located in the cloud or on another network server. The production line matching system 102 can obtain the object design information of the objects to be scheduled for production, as well as the processing capacity information of each production line, from the server 104. Based on the object design information and the processing capacity information of each production line, it performs hard constraint matching between the objects to be scheduled and each production line to obtain the hard constraint matching results. If the hard constraint matching results indicate that there are reserve production lines among the production lines that meet the hard constraint conditions, the production line matching system 102 can determine the target production line from among the reserve production lines based on their respective production line occupancy status, and generate a production scheduling plan for the objects to be scheduled based on the target production line.

[0061] The production line matching system 102 can be integrated into a user terminal, which can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can be production control equipment. Portable wearable devices can be smartwatches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented using a standalone server or a server cluster composed of multiple servers.

[0062] In another embodiment, taking an actual core production scenario as an example, such as Figure 2As shown, the production line matching system 102 can communicate with the production line control system 202 and the production management system 204 in the core production scenario. The production line control system 202 controls each production line in the core production workshop. In actual use, the production line matching system 102 can obtain the object design information of the objects to be scheduled and the processing capacity information of each production line through the production management system 204. Based on the object design information and the processing capacity information of each production line, it performs hard constraint matching between the objects to be scheduled and each production line to obtain the hard constraint matching results. If the hard constraint matching results indicate that there are reserve production lines that meet the hard constraint conditions, the production line matching system 102 can determine the target scheduling production line from among the reserve production lines based on their respective production line occupancy status, and generate a scheduling plan for the objects to be scheduled based on the target scheduling production line. If it is determined that the scheduling plan will be used to schedule the objects to be scheduled, the scheduling plan can be sent to the production line control system 202, instructing the target scheduling production line to schedule the objects to be scheduled.

[0063] In one embodiment, such as Figure 3 As shown, a production line matching method is provided, which is applied to... Figure 1 Taking the production line matching system 102 as an example, the following steps are included:

[0064] S302, obtain the object design information of the objects to be scheduled for production, as well as the processing capacity information of each production line.

[0065] In this context, "production tasks awaiting scheduling" refers to specific tasks that need to be arranged for production. This means a production task that has not yet been assigned a production line or had its production sequence determined; it typically corresponds to a product, part, or batch of a process. For example, a transformer core.

[0066] Among them, object design information is a set of data describing the physical and technical specifications of the object to be produced. It may include, but is not limited to, attribute information determined in the design phase, such as geometric dimensions, material requirements, and manufacturing precision.

[0067] The object design information includes at least one object design parameter for the object to be produced. An object design parameter is a single measurable attribute in the object design information, corresponding to a design specification of the object to be produced. Taking the core as an example, the object design parameters may include sheet width, hole diameter, hole spacing, centerline, and product weight. The parameter value of the object design parameter refers to the specific numerical value of the object design parameter, that is, the actual value of the object to be produced for a certain design parameter, used as a benchmark for comparison with the capability range. For example, the object design information of the object to be produced can be expressed as: sheet width 180mm, hole diameter 16mm, hole spacing 600mm, centerline 800mm, and product weight 2500kg.

[0068] A production line is a processing unit in a production scenario used to perform production tasks. It is a production line equipped with specific equipment, molds, production tools, and operators, capable of completing the processing steps of a certain type of product. Production line processing capacity information describes the range of product parameters and limitations that the production line can process, such as the upper limit, lower limit, or feasible range of processing allowed by the production line in terms of physics, process, or specifications.

[0069] For example, when it is necessary to assign production lines to objects to be scheduled for production, the production line matching system can obtain the object design information of the objects to be scheduled, as well as the processing capacity information of each production line.

[0070] In one embodiment, the production line matching system can respond to a scheduling command triggered by a user for a production object to be scheduled, and obtain the object design information of the production object to be scheduled. The object design information can be provided by the user, such as a designer.

[0071] In one embodiment, the production line matching system is equipped with a production line information database to store the processing capacity information of each production line. The production line matching system can obtain the processing capacity information of each production line from the production line information database.

[0072] S304. Based on the object design information and the processing capacity information of each production line, the objects to be scheduled for production are matched with each production line using hard constraints to obtain the hard constraint matching results.

[0073] Hard constraint matching is a process of determining whether a production line meets a hard constraint condition. The hard constraint matching result is the output information of the hard constraint matching process, indicating whether each production line meets the hard constraint condition. The hard constraint matching result can include qualitative information, such as whether the production line meets the hard constraint condition or not. It can also include quantitative information, such as the specific deviation between the production line's processing capacity information and the object's design parameters, or the names of the constraints that are not met.

[0074] For example, the production line matching system can perform hard constraint matching between the objects to be scheduled and each production line based on the object design information and the processing capacity information of each production line, and obtain the hard constraint matching result.

[0075] Understandably, the results of hard constraint matching can be used to characterize whether there are reserve production lines. Reserve production lines are those production lines that meet the hard constraint conditions with the production objects to be scheduled.

[0076] Among these, hard constraints are mandatory judgment conditions that determine whether a production line can be used to process the items to be produced. Reserve production lines are candidate production lines selected through hard constraint matching, meaning production lines whose processing capacity information meets the design requirements of the items to be produced.

[0077] In one embodiment, the hard constraint includes the production line's product parameter range covering the object design parameters of the object to be produced. For example, the production line matching system can compare the product parameter range of each production line with the object design parameters of the object to be produced. If the production line's product parameter range covers the object design parameters of the object to be produced, the system determines that the production line meets the hard constraint and is designated as a reserve production line.

[0078] For example, production line A's processing capacity information includes a processable sheet width range of 50-300mm, a maximum processable product weight of 4000kg, processable hole diameters of 14mm, 16mm, and 20mm, processable hole spacing of 300mm, 450mm, 600mm, 750mm, and 900mm, a processable centerline range of 500mm-1250mm, and a production buffer time of 15 minutes. Therefore, the design parameter range for sheet width is 50-300mm, and the design parameter range for product weight is less than or equal to 4000kg, and so on.

[0079] In one embodiment, if the product parameter range of a production line does not cover or does not fully cover the object design parameters, the production line is determined not to meet the hard constraints. By quantitatively comparing the object design parameters with the production line's processing capacity information range item by item, manual experience judgment is replaced, reducing the possibility of incorrect matching due to subjective bias or information omissions. Only when the production line's product parameter range covers all object design parameters is it determined to meet the hard constraints, ensuring the objectivity and physical feasibility of the matching results, thereby improving the accuracy of production line selection and providing a reliable basis for subsequent production scheduling decisions.

[0080] S306, when there is a backup production line, determine the target production line from the backup production line based on the first information.

[0081] Among these, hard constraints are mandatory judgment conditions that determine whether a production line can be used to process the items to be produced. Reserve production lines are candidate production lines selected through hard constraint matching, meaning production lines whose processing capacity information meets the design requirements of the items to be produced.

[0082] The first piece of information is data selected from the standby production lines to determine the final production lines to be scheduled. This data may include the idle periods of the standby production lines. The idle periods can be determined based on the occupancy status of the standby production lines.

[0083] Among them, production line occupancy status is information used to describe the load distribution of the production line in the current and future periods, which may include the time period occupied by the production tasks already assigned to the production line, the load rate, and the remaining available capacity.

[0084] In one embodiment, a timeline can be used to identify the time intervals of scheduled tasks.

[0085] Among them, the target production line refers to the target production line that is finally determined from all the reserve production lines based on the first information and used for actual production.

[0086] For example, the production line matching system can determine whether there are any reserve production lines that meet the hard constraint conditions in each production line based on the hard constraint matching results. If there are, the target production line is determined from the reserve production lines based on the first information.

[0087] In one embodiment, the production line matching system can directly determine the target production line from the reserve production lines based on the idle time of the reserve production lines. For example, the reserve production line with the earliest idle time can be selected as the target production line.

[0088] S308 generates a production schedule plan for the objects to be scheduled based on the target production line.

[0089] The production scheduling plan is specific production task execution information generated for the objects to be scheduled, which may include information such as the target production line, scheduling time, and production process of the objects to be scheduled.

[0090] For example, after determining the target production line for the object to be produced, the production line matching system can generate a production schedule plan for the object based on the target production line.

[0091] In one embodiment, the production line matching system can directly send the production scheduling plan to the manufacturing personnel, instructing them to produce the items to be scheduled according to the production scheduling plan.

[0092] The aforementioned production line matching method allows the system to automatically acquire the object design information of the product to be scheduled, as well as the processing capacity information of each production line. Based on the object design information and the processing capacity information of each production line, the system performs hard constraint matching between the product to be scheduled and each production line, obtaining hard constraint matching results. These results indicate the existence of reserve production lines. Reserve production lines are those that meet the hard constraint conditions with the product to be scheduled. These hard constraint conditions include the production line's processable product parameter range covering the object design parameters of the product to be scheduled. If reserve production lines exist, the target production line is determined from among them based on their idle time periods. Finally, a production schedule for the product to be scheduled is generated based on the target production line. This method, on the one hand, because the hard constraint conditions include the production line's product parameter range covering the object design parameters of the product to be scheduled, allows for the comparison of object design parameters with the corresponding production line parameter range, replacing the traditional subjective judgment method relying on personal experience. This reduces the repeated communication and trial-and-error costs required for manual scheduling, effectively improving production line matching efficiency. On the other hand, even if the production line status or order demand changes, the production line matching system can respond quickly and re-optimize the production scheduling plan, effectively improving the utilization rate of existing production lines.

[0093] In one embodiment, the first information also includes the expected delivery time of the object to be scheduled for production, the historical design information of each historical production object, and the historical production man-hours. For example... Figure 4 As shown, determining the target production line from each pre-production line based on the first information in S306 may include:

[0094] S402, obtain the expected delivery time of the production object to be scheduled, as well as the historical design information and historical production man-hours of each historical production object.

[0095] The expected delivery time refers to the final deadline for the planned completion and delivery of the product to be produced, which can be the production deadline specified by the production plan or customer order.

[0096] Among them, historical production objects are production objects that have been completed in the current production scenario. Each production object has corresponding object design information, i.e., historical design information, and the production time consumed during actual production, i.e., historical production man-hours.

[0097] In one embodiment, the production line matching system can be pre-configured with a historical task database to store production information of historical production objects, such as historical design information and historical production hours.

[0098] For example, when the production line matching system determines that there is a backup production line, it can obtain the expected delivery time of the production object to be scheduled, as well as the historical design information and historical production hours of each historical production object.

[0099] S404. Based on the object design information and historical design information, determine the similar production scheduling objects that meet the similarity conditions with the object to be scheduled from each historical production object.

[0100] The similarity condition is a preset judgment condition used to determine whether the historical production object and the production object to be scheduled are similar in the dimension of design parameters. If the historical production object and the production object to be scheduled meet the similarity condition, it can be considered that the design features of the historical production object are similar to the design features of the production object to be scheduled, and its production time has reference value relative to the production object to be scheduled.

[0101] For example, the production line matching system can determine similar production objects that meet the similarity conditions with the object to be scheduled from each historical production object based on the object design information and each historical design information.

[0102] For example, the design information of the iron core to be produced is: 180mm width, 16mm aperture, 600mm aperture spacing, 800mm centerline, and 2500kg product weight. The historical design information of similar production objects that meet the similarity criteria can be: 170-190mm width, 16mm aperture, and 600mm aperture spacing.

[0103] In one embodiment, the similarity condition includes that the feature distance between the object design features represented by the object design information and the historical object design features represented by the corresponding historical design information is less than a preset distance threshold. For example, the production line matching system can extract features from the object design information of the object to be scheduled, obtain object design features, extract features from each historical design information, obtain each historical object design feature, calculate the feature distance between the object design features and each historical object design feature, and determine the historical production objects corresponding to the historical object design features whose feature distance is less than the preset distance threshold as similar production objects that meet the similarity condition with the object to be scheduled.

[0104] In one embodiment, the production line matching system can determine similar production objects that meet similarity conditions with the object to be scheduled based on the parameter deviation threshold between the design parameters of each object to be scheduled and the historical design parameters of historical production objects. For example, if the parameter deviation values ​​are all less than the corresponding parameter deviation threshold, historical production objects are determined as similar production objects that meet the similarity conditions with the object to be scheduled.

[0105] S406, Based on the historical production hours of similar production objects, determine the estimated production hours of the objects to be scheduled.

[0106] Among them, the estimated production time is the predicted value of the time required for the future production of the object to be scheduled, that is, the processing time expected to be consumed from the start to the end of production of the object to be scheduled, which is calculated based on the historical production time of similar objects.

[0107] For example, a production line matching system can determine the estimated production time of an object to be scheduled based on the historical production time of similar objects.

[0108] In one embodiment, when there are multiple similar production scheduling objects, the average of each historical production time can be determined as the estimated production time of the object to be scheduled.

[0109] In one embodiment, when there is a single similar production scheduling object, the historical production hours of that similar production scheduling object can be determined as the estimated production hours of the object to be scheduled.

[0110] S408 determines the expected production time window for the objects to be scheduled based on the estimated production man-hours and expected delivery time.

[0111] The expected production scheduling time window is a time window calculated by working backward from the expected delivery time and the estimated production man-hours. The objects to be scheduled will be scheduled for production within this time window.

[0112] For example, the production line matching system can determine the expected production time window for objects to be scheduled based on the estimated production man-hours and expected delivery time.

[0113] In one embodiment, the production line matching system can further consider the actual production situation of each production line, such as the production buffer time of each production line, and determine the expected production scheduling time window for the objects to be scheduled for each production line. For example, the production line matching system can calculate the expected production scheduling time window for the objects to be scheduled for each production line based on the estimated production time, expected delivery time, and the production buffer time corresponding to the production line.

[0114] S410: Based on the expected production scheduling time window and the idle time of each reserve production line, if it is determined that there is an idle production line among the reserve production lines, the target production line for the object to be scheduled is determined according to the idle production line.

[0115] Idle production lines refer to standby production lines that have idle periods within the expected production scheduling window. Idle periods refer to the continuous time period during which the corresponding standby production line has not been assigned any production tasks.

[0116] For example, the production line matching system can determine whether there are idle production lines among the prepared production lines based on the expected production scheduling time window and the idle time period of the prepared production lines. If there are, the target production line for the object to be scheduled is determined based on the idle production lines.

[0117] In one embodiment, if each production line in the preparation production line has a desired production scheduling time window for the object to be scheduled, the production line matching system can compare the idle time of each production line with the corresponding desired production scheduling time window. If the idle time of the production line can cover the desired production scheduling time window, then the production line is determined to be an idle production line.

[0118] In one embodiment, if there is only one idle production line, the production line matching system can directly identify the idle production line as the target production line for the object to be scheduled.

[0119] In one embodiment, if multiple idle production lines exist, the target scheduling production line with the best conditions can be selected from among them. For example, the best conditions could be the earliest available, or the production line's idle period being closest to the desired scheduling time window, or the currently scheduled task being the lightest.

[0120] In the above embodiments, the production time of the current production object to be scheduled is estimated by using the historical production time of similar historical objects, which replaces manual experience estimation and improves the accuracy of time prediction. Then, the scheduling window is deduced based on the expected delivery time to ensure that the scheduling plan meets the hard requirement of on-time delivery. Finally, the target production line is selected by combining the idle time of the production line, which reduces the possibility of delay and error in manually inquiring about the status of the production line across positions, thereby improving the feasibility, timeliness and generation efficiency of the scheduling plan.

[0121] In one embodiment, such as Figure 5 As shown, production line matching methods may also include:

[0122] S502, if there are no idle production lines among the prepared production lines, candidate prepared tasks are determined from each prepared production line according to the expected production scheduling time window.

[0123] Each production line in the standby production line has its own standby tasks. Standby tasks are production tasks that have been scheduled but not yet executed or are being executed on the production line. Candidate standby tasks are standby tasks whose scheduling time window overlaps with the expected scheduling time window. It is because of the existence of candidate standby tasks that the corresponding production line cannot become an idle production line.

[0124] For example, if the production line matching system determines that there are no idle production lines among the reserve production lines based on the expected production scheduling time window and the idle time period of the reserve production lines, it can determine candidate reserve tasks from the reserve production lines according to the expected production scheduling time window.

[0125] In one embodiment, there may be multiple candidate pre-production tasks in the pre-production line, and the production line matching system can determine the best task from among the multiple candidate pre-production tasks as the final candidate pre-production task. For example, the candidate pre-production task with the shortest production time or the highest degree of overlap can be selected.

[0126] In one embodiment, the production line matching system can directly perform subsequent preemption judgment steps for multiple candidate preparatory tasks.

[0127] S504, obtain the pre-task scheduling information of the candidate pre-tasks, as well as the object priority of the objects to be scheduled.

[0128] Among them, the preparatory task scheduling information refers to the scheduling information of candidate preparatory tasks, which may include the start time, end time, duration, and priority of the candidate preparatory tasks.

[0129] Among them, object priority is a level parameter used to indicate the importance of the object to be scheduled in the production scenario.

[0130] For example, the production line matching system can obtain the pre-task scheduling information of candidate pre-tasks, as well as the object priority of the objects to be scheduled.

[0131] S506, if the candidate pre-task meets the pre-emption conditions based on the pre-task scheduling information and object priority, the pre-emption strategy for the candidate pre-task is determined based on the pre-task scheduling information.

[0132] Among them, the preemption condition is a preset judgment condition used to determine whether the production time of the candidate preparatory task can be preempted. If the candidate preparatory task meets the preemption condition, it means that the production object to be scheduled can preempt the production time of the candidate preparatory task on the corresponding preparatory production line.

[0133] Among them, the preemption strategy is a specific adjustment plan for the preempted task, which is used to reschedule the preempted candidate reserve task.

[0134] For example, the production line matching system can determine whether a candidate pre-task meets the pre-emption condition based on the pre-task scheduling information and the object priority. If the candidate pre-task meets the pre-emption condition, the system can determine the pre-emption strategy for the candidate pre-task based on the pre-task scheduling information.

[0135] In one embodiment, the preemption strategy may include a migration strategy and a postponement strategy. The migration strategy refers to migrating the preempted task to another production line. The postponement strategy refers to adjusting the preempted task backward within its original production line.

[0136] In one embodiment, the pre-job scheduling information may further include the number of task migrations and the remaining processing time of the candidate pre-job tasks. Determining the pre-job strategy for candidate pre-job tasks based on the pre-job scheduling information may include: if the number of task migrations of a candidate pre-job task has not reached a migration threshold, and there are replaceable production lines for the candidate pre-job tasks in each production line, the production line matching system may determine that the pre-job strategy for the candidate pre-job tasks is a migration strategy.

[0137] If the number of migrations for a candidate pre-task reaches the migration threshold, or if there is no alternative production line for the candidate pre-task in any production line, the production line matching system can obtain the production buffer time of the pre-task production line to which the candidate pre-task belongs, and determine the estimated production end time of the task to be scheduled based on the expected production time window of the task to be scheduled. If the time obtained by adding the remaining processing time of the candidate pre-task and the production buffer time to the estimated production end time is earlier than the task delivery time of the candidate pre-task, then the pre-emption strategy for the candidate pre-task is determined to be the postponement strategy.

[0138] In one embodiment, the production line matching system can obtain the idle time of each replaceable production line and determine the replaceable production line with the longest idle time as the target replaceable production line.

[0139] S508 identifies the production line to which the candidate preparatory task that meets the preemption conditions belongs as the preemption production line.

[0140] For example, the production line matching system can identify the production line to which a candidate preparatory task that meets the preemption conditions belongs as the preemption production line.

[0141] S510 generates a production scheduling plan for pending production tasks based on the preemption strategy and the preempted production line.

[0142] For example, the production line matching system can generate a production scheduling plan for tasks to be scheduled based on the preemption strategy and the preempted production line.

[0143] In one embodiment, the scheduling scheme for tasks to be scheduled may include a preemption scheme for candidate reserve tasks and a task scheduling scheme for tasks to be scheduled. In actual production, the candidate reserve tasks need to be preempted according to the preemption scheme first, and then the tasks to be scheduled are scheduled according to the task scheduling scheme.

[0144] In the above embodiments, when there are no idle production lines, candidate pre-assigned tasks are first selected from the reserve production lines based on the expected production time window, improving decision-making efficiency. Then, the preemption conditions are determined by combining the object priority and the pre-assigned task production information, ensuring that only higher-priority pre-assigned objects have the right to preempt, thereby guaranteeing the timely delivery of urgent tasks. Finally, a specific preemption strategy is determined based on the production information, making the adjustment of the original task justifiable, reducing the risk of delays in the original task due to blind interruption, and realizing the timely insertion of high-priority tasks and the reasonable resolution of production scheduling conflicts.

[0145] In one embodiment, the pre-production task scheduling information includes the pre-production task priority and the remaining delivery time of the candidate pre-production task. The production line matching method may further include: if the ratio of the object priority of the object to be scheduled to the pre-production task priority exceeds a preset weight ratio threshold, obtaining the minimum remaining delivery time of the candidate pre-production task; if the remaining delivery time of the task is greater than or equal to the minimum remaining delivery time, then determining that the candidate pre-production task meets the preemption condition.

[0146] Among them, the priority of the preparatory task is a level parameter used to indicate the importance of the candidate preparatory task in the production scenario. The remaining delivery time of the candidate preparatory task is the remaining time before the task delivery deadline.

[0147] The preset weight ratio threshold is used to determine whether the priority of the task to be scheduled is significantly higher than the priority of the candidate preparatory tasks. The preset weight ratio threshold can be determined according to actual production needs.

[0148] The minimum remaining delivery time is a pre-set threshold for the remaining delivery time of candidate pre-tasks, used to determine whether a candidate pre-task meets the pre-emption conditions. Understandably, the minimum remaining delivery time can be set individually for each candidate pre-task, or it can be set uniformly for the pre-task production line to which the candidate pre-task belongs.

[0149] For example, the production line matching system can calculate the ratio of the object priority of the object to be scheduled to the priority of the candidate task, and compare the ratio with a preset weight ratio threshold. If the ratio exceeds the preset weight ratio threshold, it indicates that the object priority of the object to be scheduled is much higher than the task priority of the candidate candidate task. The production line matching system can obtain the minimum remaining delivery time of the candidate candidate task, compare the remaining delivery time of the candidate candidate task with the minimum remaining delivery time, and if the remaining delivery time of the task is greater than or equal to the minimum remaining delivery time, it is determined that the candidate candidate task meets the preemption condition.

[0150] In one embodiment, if the remaining delivery time of a task is less than the minimum remaining delivery time, it is determined that the candidate pre-assignment task does not meet the pre-assignment conditions. If the number of candidate pre-assignment tasks is only one and the pre-assignment conditions are not met, the production line matching system can generate a production failure prompt message and send the production failure prompt message to the staff to prompt the staff to handle it manually.

[0151] In one embodiment, when there are multiple candidate pre-job tasks, the production line matching system can traverse each candidate pre-job task. If there is a candidate pre-job task that meets the pre-job conditions, the subsequent pre-job strategy judgment steps are executed. If none of the candidate pre-job tasks meet the pre-job conditions, a production failure prompt message is generated and sent to the staff to prompt them to handle the situation manually.

[0152] In the above embodiments, significantly high-priority tasks are selected by using a priority ratio threshold to ensure that only truly important tasks can trigger preemption. Based on this, by comparing the remaining delivery time, only candidate tasks with a remaining time not less than the minimum value are allowed to be preempted, thereby excluding urgent tasks from the preemption scope. This can satisfy the insertion requirements of high-priority tasks while ensuring that the original tasks with urgent delivery are not interrupted, effectively controlling the negative impact of preemption on overall delivery performance, and achieving a synergistic balance between priority and urgency.

[0153] In one embodiment, such as Figure 6 As shown, production line matching methods may also include:

[0154] S602, in the absence of a pre-production line, determine at least one design parameter to be adjusted from the object design information based on the hard constraint matching results.

[0155] Among them, the design parameters to be adjusted are the design parameters identified from the object design information that cause the hard constraint matching failure. That is, the design parameters recorded in the hard constraint matching results that exceed the product parameter range of all production lines, or the key constraint parameters that meet the product parameter range of some production lines but cause the overall hard constraint conditions to fail to match.

[0156] For example, if the hard constraint matching result indicates that there is no pre-existing production line that meets the hard constraint conditions among the production lines, the production line matching system can determine at least one design parameter to be adjusted from the object design information based on the hard constraint matching result.

[0157] S604, determine the target parameter adjustment strategy that matches the design parameter to be adjusted from multiple candidate parameter adjustment strategies.

[0158] The candidate parameter adjustment strategy is a pre-configured parameter adjustment strategy for each design parameter; different design parameters require different adjustment strategies. The target parameter adjustment strategy, on the other hand, is the final parameter adjustment strategy that matches the design parameter to be adjusted. It can be understood that the number of parameter adjustment strategies matching the design parameter to be adjusted can be one or more; that is, designers can pre-set one or more parameter adjustment strategies for the design parameter for selection during adjustment. If there is only one parameter adjustment strategy matching the design parameter to be adjusted, that strategy is directly determined as the target parameter adjustment strategy. If there are multiple parameter adjustment strategies matching the design parameter to be adjusted, the production line matching system can select one from the multiple matching strategies as the target parameter adjustment strategy, or directly use multiple parameter adjustment strategies simultaneously as the target parameter adjustment strategy.

[0159] For example, the production line matching system is pre-configured with a parameter adjustment rule base, which stores parameter adjustment strategies for each design parameter as candidate parameter adjustment strategies. After determining the design parameter to be adjusted, the production line matching system can determine the target parameter adjustment strategy that matches the design parameter to be adjusted from the candidate parameter adjustment strategies stored in the parameter adjustment rule base, based on the parameter identifier of the design parameter to be adjusted.

[0160] S606 generates parameter adjustment suggestions based on the target parameter adjustment strategy and the design parameters to be adjusted, and feeds the parameter adjustment suggestions back to the user terminal.

[0161] Among them, the parameter adjustment suggestion is a parameter modification prompt generated for users, which is used to prompt and guide users to make corresponding adjustments to the design parameters to be adjusted.

[0162] For example, the production line matching system can generate parameter adjustment suggestions based on the target parameter adjustment strategy and the design parameters to be adjusted, and then feed the parameter adjustment suggestions back to the user terminal.

[0163] In one embodiment, the production line matching system can convert the standard parameter adjustment strategy into natural language or structured information based on the design parameters to be adjusted, forming a suggested text containing the original parameter value, suggested parameter value, reason for adjustment, and basis for adjustment strategy.

[0164] S608, in response to a parameter adjustment instruction triggered by a parameter adjustment suggestion, updates the design parameters to be adjusted and obtains the updated object design information.

[0165] Among them, the parameter adjustment command is a signal used to instruct the adjustment of the design parameters to be adjusted. It can be triggered by staff based on parameter adjustment suggestions. The parameter adjustment command contains the target adjustment value of the design parameters to be adjusted.

[0166] For example, staff can receive parameter adjustment suggestions through a user terminal and trigger parameter adjustment instructions for the design parameters to be adjusted in response to these suggestions. The production line matching system, in response to the parameter adjustment instructions triggered by the suggestions, can obtain the target adjustment value for the design parameters to be adjusted, update the design parameters according to the target adjustment value, and obtain the updated object design information.

[0167] S610, based on the updated object design information, returns the step of performing hard constraint matching between the objects to be scheduled and each production line according to the object design information and the processing capacity information of each production line, and obtains the hard constraint matching result.

[0168] For example, after obtaining the updated object design information, the production line matching system can, based on the updated object design information, return to the step of performing hard constraint matching between the objects to be scheduled and each production line, according to the object design information and the processing capacity information of each production line, to obtain the hard constraint matching results. That is, based on the updated object design information and the processing capacity information of each production line, the objects to be scheduled are matched with each production line using hard constraints to obtain the hard constraint matching results. By updating the object design information, the prepared production lines are ensured to be non-empty.

[0169] In the above embodiments, when the design parameters do not match the processing capacity information of all production lines, parameter adjustment suggestions for the design parameters to be adjusted can be pushed to the staff according to the pre-set parameter adjustment rules. After the staff confirms, the parameters are automatically updated and re-matching is triggered, which significantly reduces the time spent on adapting the design parameters to the production line capacity, improves the success rate of matching hard constraints, and thus accelerates the production scheduling efficiency.

[0170] In one embodiment, S604, determining a target parameter adjustment strategy that matches the design parameter to be adjusted from a plurality of candidate parameter adjustment strategies includes: determining a plurality of selected parameter adjustment strategies that match the design parameter to be adjusted from a plurality of candidate parameter adjustment strategies. Based on the historical adoption rate of each selected parameter adjustment strategy, the selected parameter adjustment strategy with the highest historical adoption rate is determined as the target parameter adjustment strategy that matches the design parameter to be adjusted.

[0171] Among them, the selected parameter adjustment strategy is the parameter adjustment strategy that matches the design parameter to be adjusted from the candidate parameter adjustment strategies, that is, the parameter adjustment strategy that is set in advance for the design parameter to be adjusted.

[0172] The historical adoption rate of a selected parameter adjustment strategy is a statistical indicator used to characterize the frequency with which a selected parameter adjustment strategy has been accepted and successfully used by users in the past. It can refer to the proportion of times the strategy was actually adopted by users out of the total number of times the strategy was recommended. For example, if a strategy has been recommended 10 times in history, and was adopted 7 times, then the historical adoption rate is 70%.

[0173] For example, when the production line matching system determines multiple selected parameter adjustment strategies that match the design parameters to be adjusted from multiple candidate parameter adjustment strategies, it can obtain the historical adoption rate of each selected parameter adjustment strategy, sort the selected parameter adjustment strategies in descending order according to each historical adoption rate, and select the selected parameter adjustment strategy with the highest historical adoption rate from the selected parameter adjustment strategies as the target parameter adjustment strategy that matches the design parameters to be adjusted.

[0174] In the above embodiments, the historical adoption rate of each selected parameter adjustment strategy is used as the basis for selection. The strategy most commonly accepted by users in the past is given priority. The operational experience of the group or history can be quantified into an adoption rate indicator, so that the parameter adjustment suggestions output by the system are more in line with user habits and actual site conditions. This reduces the probability of user rejection or modification, shortens the adaptation and iteration cycle between design and production line, and improves the efficiency of production scheduling preparation.

[0175] In one embodiment, the production line matching method may further include: generating a multi-level approval process based on the production scheduling plan of the object to be scheduled. In response to an approval pass event triggered by the multi-level approval process, the object to be scheduled is designated as a production line task of the target production line and bound to the corresponding production time slot in the target production line.

[0176] Among them, the multi-level approval process refers to the review path that requires multiple approval roles to confirm sequentially or in parallel. It consists of multiple approval nodes in a preset order. Each approval node corresponds to an approver or role, who can indicate approval or rejection. The overall approval process is only completed after all nodes have passed.

[0177] In one embodiment, the multi-level approval process includes designers, process engineers, and manufacturing personnel. Designers can verify the accuracy of design parameters at the design parameter review node. Process engineers can fill in the actual working hours of the objects to be scheduled at the process review node. Manufacturing personnel can confirm the feasibility of the production lines and time periods involved in the plan at the plan confirmation node. When the plan involves adjusting the preemption of candidate tasks, the feasibility of the new time period after the adjustment of the preempted tasks needs to be confirmed simultaneously.

[0178] In one embodiment, the production line matching system will automatically verify the actual working hours filled in by the process engineer to determine whether the actual working hours are within the continuous time period reserved in the plan.

[0179] Among them, the approval pass event is an event triggered when the entire multi-level approval process is approved, indicating that the multi-level approval process has been confirmed to have passed all nodes.

[0180] The production time period corresponding to the target production line refers to the time period during which the object to be scheduled is required to be scheduled as a production line task. It can refer to the expected production time window of the object to be scheduled within the corresponding production time period of the target production line.

[0181] For example, the production line matching system can generate a multi-level approval process based on the production scheduling plan of the object to be scheduled. The reviewers at each review node can confirm the review nodes in the multi-level approval process through their respective user terminals. When all review nodes in the multi-level approval process have been confirmed as approved, the production line matching system can treat the object to be scheduled as a production line task of the target production line and bind it to the corresponding production time period in the target production line, that is, add a production line task to the target production line.

[0182] In one embodiment, the production line matching system can set up a production line occupancy database. When a new production line task is added, the production line occupancy database can be updated according to the new production line task, task delivery deadline, task priority, and target production line, and the relevant information of the new production line task can be recorded in the historical task database.

[0183] In one embodiment, when a preemptive task is involved, the production line matching system also needs to update the production line occupancy database according to the preemptive task handling scheme.

[0184] In the above embodiments, by automatically generating multi-level approval processes, production scheduling plans are incorporated into a formalized review process, ensuring that the plans undergo multi-level confirmation before release. This reduces the risk of production line conflicts or resource misallocation caused by single-point decision-making errors. After approval, tasks are automatically bound to production line time slots, eliminating the intermediate steps of manual data entry or notification, reducing operational delays and information transmission errors. This achieves closed-loop automation of the production scheduling plan from preparation to effectiveness, improving the reliability and efficiency of production scheduling execution.

[0185] In one embodiment, a production line matching method is provided, which can be applied to the design, production line selection, and production scheduling of core components in power equipment such as transformers and reactors. This method can be derived from, for example, […]. Figure 7 The production line matching system shown is executed. This system may include a data maintenance module, a matching engine module, a parameter negotiation module, a preemption arbitration module, an approval and task release module, and a history learning module.

[0186] The data maintenance module is responsible for the unified storage and dynamic updating of production line information, production line occupancy status, historical product information, adjustment rules, and preemption rules. It supports process engineers and manufacturing personnel in real-time maintenance of production line processing capacity ranges, occupancy periods, and temporary capacity changes. Specifically, the data maintenance module is responsible for the unified storage and dynamic updating of the production line information database, parameter adjustment rule database, production line occupancy status database, preemption rule database, and historical product database. The relevant storage information for each database is as follows: Figure 8 As shown.

[0187] The production line information database stores the processing capacity range of each production line (minimum / maximum sheet width, hole diameter, hole spacing, etc.). The parameter adjustment rule library stores adjustment rules for negotiable design parameters, including parameter name, allowed adjustment direction, maximum adjustment step size, and the historical adoption rate of the rule. Allowed adjustment directions can include increasing, decreasing, and bidirectional adjustments. The production line occupancy database stores information such as the production task corresponding to each product, the occupied production line, and the real-time occupancy period, supporting process engineers and manufacturing personnel to dynamically update the production line status. The preemption rule library stores the mandatory conditions for allowing production line preemption, including priority weight thresholds, the minimum remaining delivery time threshold for the original task, and relevant regulations for allowing task migration. The historical product library stores the relevant design parameter values ​​and production information of historical production objects, providing historical data references for other modules.

[0188] The matching engine module, as the core decision-making unit, receives the core design parameters and negotiable flags entered by the designer, and performs hard constraint matching by integrating production line processing capacity constraints and continuous idle time constraints. Specifically, the matching engine module is responsible for production line screening and scheme generation. First, it reads the production line information database to obtain the processing capacity information of each production line, performs hard constraint matching based on design parameters and production line processing capacity, filters out a list of reserve production lines with processing capacity, and marks the mismatch parameters of the product on each production line. Second, when the list of reserve production lines is not empty, it reads the historical product database to search for similar products, such as historical products of the same model, brand, and processing requirements, calculates the estimated working hours of the current product, and sets the expected time window. Then, it reads the production line occupancy database and filters out a list of idle production lines with continuous idle time within the expected time window from the list of reserve production lines.

[0189] The parameter negotiation module intervenes when matching fails. Based on the parameter adjustment rule library, it pushes suggestions and recommended adjustment values ​​for adjustable parameters to the designer. Upon confirmation, the parameters are automatically updated, and a re-matching is triggered. Specifically, the parameter negotiation module is triggered when the list of production lines is empty, meaning no production line has processing capacity. First, it reads the parameter adjustment rule library to determine if all mismatched parameters exist in it, i.e., if all have adjustable configuration rules. If any mismatched parameter is not in the library, the design parameter cannot be adjusted through negotiation to meet production requirements, and the system directly prompts for manual intervention, terminating the matching process. If all mismatched parameters exist in the library, the adjustment rules for the mismatched parameters are pushed to the designer according to their historical adoption rate from high to low. After the designer confirms the adjustment, the parameters are automatically updated, and the matching engine module is called again; if the designer refuses the adjustment, the matching process terminates. Regardless of adoption or rejection, the adjustment suggestion and decision result are recorded in the historical product library for subsequent rule adoption rate updates.

[0190] The preemption arbitration module handles production line time slot conflicts, making safe preemption or migration decisions based on priority and delivery date. Specifically, the preemption arbitration module receives a list of idle production lines generated by the system based on the reserve production line list. It is triggered when the list of idle production lines is empty, meaning no production line has processing time. This module reads all enabled rules from the preemption rule base and determines which rules allow preemption based on the new task priority, the original task priority, and the original task delivery date. The conditions for allowing preemption are: the ratio of the new task priority to the original task priority exceeds the priority weight ratio threshold, and the remaining delivery date of the original task is not less than the minimum remaining delivery date threshold. If these preconditions are not met, preemption is not allowed, and the system prompts the designer for manual intervention.

[0191] If the preconditions for allowing preemption are met, the system further determines which preemption strategy to adopt, specifically the following two:

[0192] First strategy: migrate the original task to other production lines.

[0193] The system determines whether the cumulative migration count of the original task has reached the preset migration count limit in the preemption rule base. If it has not reached the limit, and the list of replaceable production lines corresponding to the original task in the production line occupancy status database is not empty, and there is at least one production line in the replaceable production line list that is in an idle state, then the migration strategy is executed: the original task is migrated to the production line in the replaceable production line list that has been idle for the longest time, the new task occupies the expected period of the current production line, and the migration count of the existing task is increased by one.

[0194] Second strategy: Postpone the original task within the original production line.

[0195] If the migration conditions are not met—that is, the original task has reached its migration limit, the list of replaceable production lines is empty, or there are no idle production lines in the list—then a further determination is made as to whether the postponement conditions are met. The system calculates the sum of the new task's estimated end time, the original task's remaining processing time, and the current production line's production buffer time, and compares this sum with the original task's delivery date. If the sum is less than the original task's delivery date, a postponement strategy is implemented: the original task is moved to the next consecutive idle period within the current production line, and the new task occupies the expected time slot of the current production line. If the sum is greater than or equal to the original task's delivery date, preemption is deemed not allowed.

[0196] The system iterates through all enabled rules in the preemption rule base and evaluates them one by one according to the logic described above. If no rule can generate a preemption plan, the system prompts for manual intervention.

[0197] The approval and task release module initiates a multi-level approval process. Process engineers dynamically fill in the actual working hours based on the specific product. After approval, the task is locked to the corresponding production line's continuous time period. Specifically, the approval and task release module receives production line plans output by the preemption arbitration module or the system, including production line plans directly generated by the system when no preemption is needed, preemption plans generated by the preemption arbitration module using a postponement strategy, or preemption plans generated by the preemption arbitration module using a migration strategy. It initiates a multi-level approval process involving designers, process engineers, and manufacturing personnel. Designers confirm the design parameters are correct, process engineers fill in the actual working hours for the product, and manufacturing personnel confirm the feasibility of the production lines and time periods involved in the plan. When the plan involves adjustments to the original task, manufacturing personnel must also confirm the feasibility of the new time period after the original task is adjusted. The system automatically verifies whether the actual working hours are within the continuous time period reserved in the plan. After approval, the new task is locked to the corresponding production line's continuous time period. At the same time, the adjusted plan for the original task and the prepared production line list, delivery date, and priority for the new task are updated to the production line occupancy database, and relevant details of the new product are recorded in the historical product database.

[0198] The historical learning module records each parameter adjustment decision and uses frequency statistics to optimize the subsequent recommendation order. Specifically, by monitoring system execution, the historical learning module identifies the designer's adjustment operations in the parameter negotiation module and dynamically updates the historical adoption rate of each rule in the parameter adjustment rule base, enabling the system to prioritize adjustment schemes with high adoption rates in subsequent parameter negotiations. Simultaneously, this module records the decision results of the preemption arbitration module and updates the historical adoption rate of each rule in the preemption rule base to optimize the parameter configuration of the preemption rule base.

[0199] Each module communicates via a unified data bus to ensure status synchronization and real-time response. For example... Figure 9 As shown, the specific execution flow of the production line matching system when executing the production line matching method is as follows:

[0200] S901, System initialization, build production line information database, parameter adjustment rule database, production line occupancy database, preemption rule database and historical product database.

[0201] For example, suppose a factory has three iron core production lines with the following processing capacities:

[0202] Production Line A: It can process sheet widths ranging from 50 to 300 mm, with a maximum product weight of 4000 kg. It can process hole diameters of 14 mm, 16 mm, and 20 mm, hole spacings of 300 mm, 450 mm, 600 mm, 750 mm, and 900 mm, and a centerline range of 500 mm to 1250 mm. The production buffer time is 15 minutes.

[0203] Production Line B: Can process sheet widths ranging from 60-280mm, can process products with a maximum weight of 3500kg, can process hole diameters of 12mm, 14mm, and 18mm, can process hole spacings of 250mm, 400mm, 550mm, 700mm, and 850mm, can process centerline ranges of 450mm-1150mm, and has a production buffer time of 20min.

[0204] Production Line C: Can process sheet widths ranging from 40-250mm, can process products with a maximum weight of 3000kg, can process hole diameters of 10mm, 14mm, and 16mm, can process hole spacings of 200mm, 350mm, 500mm, 650mm, and 800mm, can process centerline ranges of 400mm-1000mm, and has a production buffer time of 15min.

[0205] The parameter adjustment rule base has only one rule: the chip width can be reduced by a maximum of 5mm, the aperture can be adjusted within the range of 14-20mm, and other parameters cannot be adjusted. The preemption rule base has only one rule: the priority weight ratio threshold is 1.2, the remaining delivery time threshold is 7 days, and the maximum number of migrations is 2.

[0206] The system administrator imports basic information and working hours data of past products into the historical product database. Workshop manufacturing personnel simultaneously maintain the real-time occupancy tables for each production line.

[0207] S902 receives core design parameters, current product priority, and product delivery date entered by the designer.

[0208] For example, a designer enters a new core design task: 180mm core width, 16mm aperture, 600mm hole spacing, 800mm centerline, 2500kg product weight, task priority 1.2, and expected delivery date April 15, 2026.

[0209] S903 reads the production line information database to obtain the processing capacity information of each production line, performs hard constraint matching based on design parameters and production line processing capacity information, and filters out a list of reserve production lines with processing capacity.

[0210] For example, the matching engine module reads the production line information database to obtain the processing capacity information of each production line, performs hard constraint matching based on the design parameters and production line processing capacity information, and filters out a list of pre-production lines with processing capacity.

[0211] S904, determine if the preparation production line list is empty. If yes, proceed to S905; otherwise, proceed to S910.

[0212] In one embodiment, the matching engine filters out a list of potential production lines with processing capabilities based on hard constraint matching conditions (piece width, hole diameter, hole spacing, centerline, and weight all fall within the production line's processing capacity range). Production line A: piece width 180mm within the range of 50-300mm, hole diameter 16mm within the allowable range, hole spacing 600mm within the allowable range, centerline 800mm within the range of 500-1250mm, weight 2500kg≤4000kg, meeting the conditions. Production lines B and C also meet the conditions. The list of potential production lines is [production line A, production line B, production line C], with no mismatch parameters. At this point, S910 can continue to be executed.

[0213] In another embodiment, if the designer enters a new core design task: 320mm lamination width, 16mm aperture, 600mm hole spacing, 800mm centerline, 2500kg product weight, task priority 1.5, and expected delivery date April 15, 2026, and the maximum lamination width for all three production lines is 300mm (line A is 300mm, line B is 280mm, and line C is 250mm), then the 320mm requirement for the new core design task exceeds the lamination width processing capacity limit of all production lines, and the list of prepared production lines is empty. At this point, S905 can be executed.

[0214] S905 marks the mismatch parameters in the core design parameters.

[0215] For example, a mismatch parameter can be marked as [piece width].

[0216] S906, determine whether all mismatched parameters exist in the parameter adjustment rule base. If not, proceed to S907. If yes, proceed to S908.

[0217] S907 indicates a manual intervention is required to terminate the matching process.

[0218] S908 reads the parameter adjustment rule library and pushes parameter adjustment rules that do not match the parameters from high to low according to their historical adoption rate.

[0219] S909, in response to the parameter update operation, obtains the updated core design parameters and returns to execute S903.

[0220] For example, the system reads the parameter adjustment rule base and determines whether the mismatched parameter [piece width] exists in the rule base. Upon querying, the piece width parameter is found to have an adjustment rule configured in the rule base, allowing reduction with a maximum step size of 5mm, thus meeting the condition. The system pushes the adjustment rule based on historical adoption rates: "Currently, no production line meets the piece width requirement of 320mm. Production line A supports piece width ≤ 300mm; it is recommended to adjust to 300mm (adoption rate 85%)." The designer confirms, the parameter is updated to 300mm, and the system returns to S903 for re-matching.

[0221] S910 reads the historical product database, retrieves similar products, calculates the estimated working hours for the current product, and sets the desired time window.

[0222] For example, the system reads the historical product database, retrieves similar products with a width of 170-190mm, a hole diameter of 16mm, and a hole spacing of 600mm, and takes the average actual working time of 2.5 hours from the historical similar products as the estimated working time.

[0223] S911, read the production line occupancy database and filter out the list of idle production lines with continuous idle periods within the expected time window from the list of prepared production lines.

[0224] S912, determine if the list of idle production lines is empty. If yes, proceed to S913; otherwise, proceed to S916.

[0225] S913, read the preemption rule base.

[0226] S914: Determine whether to execute the preemption strategy. If not, execute S907. If yes, execute S915.

[0227] For example, by combining the list of idle production lines, the priority of the original production line tasks, the original task delivery date, and the current product priority, it is determined whether to execute the preemption strategy.

[0228] S915 generates a preemption plan based on the preemption strategy.

[0229] For example, the system first determines whether the migration conditions are met. If they are met, a migration plan is generated, which migrates the original task to the production line with the longest idle time in the list of replaceable production lines. If the migration conditions are not met, the system determines whether the postponement conditions are met. If they are met, a postponement plan is generated, which adjusts the original task to a later position within the current production line. If neither condition is met, the system continues to iterate through the next rule, generating a preemption plan that includes the production line occupancy status of the original task after adjustment and the production line occupancy status of the new task.

[0230] In one embodiment, assuming the current date is April 12, 2026, if the designer enters a new core production task with the following specifications: lamination width 180mm, aperture 16mm, aperture spacing 600mm, centerline 800mm, product weight 2500kg, task priority 1.5, and expected delivery date April 12, 2026, then production lines A, B, and C all meet the hard constraints, and the prepared production line list is [production line A, production line B, production line C]. The system reads the historical product database, searches for similar products, and calculates the estimated working time of 2.5 hours. It then reads the production line occupancy database, and for each production line in the prepared production line list, it calculates the expected time window for that production line by working backward from the expected delivery date (April 12, 2026) to determine the expected working time (2.5 hours) and the production buffer time of that production line, and then determines whether the production line is idle within that time window.

[0231] Production Line A: Production buffer time is 15 minutes. The expected time window is from 8:00 to 10:45 on April 12th. This time period is occupied by task T1 (priority 1.0, delivery date April 20th, remaining processing time 2 hours, migration count 0, replaceable production line list is [Production Line B]), and it will not be added to the list of idle production lines.

[0232] Production Line B: Production buffer time is 20 minutes. The expected time window is from 8:00 to 10:50 on April 12th. This period is occupied by task T2 (priority 1.1, delivery date April 18th, remaining processing time 1.5 hours, migration count 1, replaceable production line list is [Production Line C]), and it will not be added to the list of idle production lines.

[0233] Production Line C: Production buffer time is 15 minutes. The expected time window is from 8:00 to 10:45 on April 12th. This time period is occupied by task T3 (priority 0.9, delivery date April 17th, remaining processing time 2.5 hours, migration count 0, replaceable production line list is [Production Line A, Production Line B]), and it will not be added to the list of idle production lines.

[0234] Therefore, the list of idle production lines is empty, triggering the preemption arbitration module.

[0235] The system reads the preemption rule base: priority weight ratio threshold 1.2, remaining delivery date threshold 7 days, maximum migration times 2, and adjustment additional time 30 minutes. The system iterates through each production line in the prepared production line list and performs preemption condition checks on each one:

[0236] First, determine the preconditions for allowing preemption. For production line A: the priority ratio 1.5 / 1.0 = 1.5 ≥ 1.2, which meets the condition; the current date is April 12th, and there are 8 days or more remaining until the T1 delivery date of April 20th, which also meets the condition. For production line B: the priority ratio 1.5 / 1.1 ≈ 1.36 ≥ 1.2, which meets the condition; there are 6 days or less remaining until the T2 delivery date of April 18th, which does not meet the condition. For production line C: the priority ratio 1.5 / 0.9 ≈ 1.67 ≥ 1.2, which meets the condition; there are 13 days or more remaining until the T3 delivery date of April 25th, which also meets the condition.

[0237] Therefore, both production lines A and C meet the preconditions for allowing preemption. The system prioritizes determining the migration strategy:

[0238] For production line A: The list of replaceable production lines for T1 is [Production Line B]. Checking the status of production line B at the current time (April 12th, 7:00 AM): Production line B is occupied by T2 and is not idle. Therefore, there are no idle production lines in the list of replaceable production lines, and the migration conditions are not met.

[0239] For production line C: The list of replaceable production lines for T3 is [Production Line A, Production Line B]. Checking the status of production lines A and B at the current time point: Production line A is occupied by T1, and production line B is occupied by T2; neither is idle. Therefore, there are no idle production lines in the list of replaceable production lines, and the migration conditions are not met.

[0240] Since neither production line A nor production line C meets the migration conditions, the system continues to determine the postponement conditions:

[0241] For production line A: The sum of the new task's production end time (April 12th, 8:00-10:45, i.e., 2.75 hours), the remaining processing time of T1 (2 hours), and the production buffer time of production line A (0.25 hours) is 5 hours. The delivery date of T1 is April 20th, with 8 days (192 hours) remaining. Since 5 hours < 192 hours, the delay condition is met.

[0242] For production line C: The sum of the new task's production completion time (2.75 hours), the remaining processing time of T3 (2.5 hours), and the production buffer time of production line C (0.25 hours) is 5.5 hours. The delivery date of T3 is April 25th, with 13 days (312 hours) remaining. Since 5.5 hours < 312 hours, the delay condition is met.

[0243] Both production lines A and C meet the postponement conditions. The system selects the optimal solution from the production lines that meet the postponement conditions. In this embodiment, the priority of the preempted task is compared. The priority of T3 (0.9) is lower than that of T1 (1.0). Therefore, production line C is selected to execute the postponement strategy, and the preemption solution is generated: T3 is postponed to the next consecutive idle period in the current production line C. The new task occupies the period of April 12th from 8:00 to 10:45 in production line C.

[0244] S916 selects the idle production line with the longest time since the last production end from the list of idle production lines to generate a production line plan, including the production line required for the current product and the time period occupied.

[0245] For example, the system reads the production line occupancy database, and for each production line in the list of prepared production lines, it calculates the expected time window for that production line by working backwards from the expected delivery date and the production buffer time of that production line, and determines whether the production line is in an idle state within that time window:

[0246] Production Line A: Production buffer time is 15 minutes. The expected time window is from 8:00 to 10:45 on April 15th. During this period, it is in an idle state. The last production end time was 16:00 on April 14th. Add it to the list of idle production lines.

[0247] Production Line B: Production buffer time is 20 minutes. The expected time window is from 8:00 to 10:50 on April 15th. During this period, it is in an idle state. The last production end time was 20:00 on April 14th. Add it to the list of idle production lines.

[0248] Production Line C: Production buffer time is 15 minutes. The expected time window is from 8:00 to 10:45 on April 15th. This time period is occupied by existing tasks and will not be added to the list of idle production lines.

[0249] With an expected delivery date of April 15, 2026, and an estimated working time of 2.5 hours, the list of available production lines is [Production Line A, Production Line B]. Select the available production line from the list that has been idle for the longest time since the last production run (Production Line A has been idle for 19 hours, Production Line B for 11 hours), and generate a production line plan that includes the current production line occupancy status (Production Line A, April 15, 8:00 AM to April 15, 10:45 AM, including a 15-minute production buffer time).

[0250] S917, the approval and task release module receives the production line plan or the preemption plan output by the preemption arbitration module, and initiates a multi-level approval process.

[0251] For example, the system initiates a multi-level approval process. The designer confirms that the design parameters are correct, the process engineer fills in the actual working hours of 2.6 hours, the manufacturing personnel confirm that the production line A time period is feasible, the approval is granted, and the task is locked to production line A.

[0252] In one embodiment, a multi-level approval process is as follows: Figure 10 As shown, the system first receives the production line plan and automatically initiates an approval process. At the first-level approval node, the reviewer, i.e., the designer, checks the design parameters and delivery deadline to determine whether to reject the plan. If rejected, the process terminates. If not rejected, the process is transferred to the second-level approval node, where the process engineer enters the actual working hours. The system verifies the actual working hours to determine if they are within the reserved time. If not, the process terminates, and the estimated working hours are updated to the actual working hours, and a new plan is generated. If they are within the reserved time, the process is transferred to the third-level approval node, where the intelligent manufacturing engineer confirms the feasibility of the plan and production details to determine whether to reject it. If rejected, the process terminates, the manufacturing engineer adjusts the data, and a new plan is generated. If not rejected, the plan is approved.

[0253] S918: After approval, the task is locked and each database is updated. At the same time, the historical learning module calculates the adoption rate of the updated rules.

[0254] The above production line matching method can achieve the following effects:

[0255] First, it significantly improves production line matching efficiency. Traditional production line matching methods typically rely on designers or process engineers manually inquiring about the processing capacity and occupancy status of each production line. This is not only time-consuming but also prone to matching errors due to information lag. This invention automatically performs hard constraint matching, idle production line screening, and estimated working time calculation through a matching engine module, transforming production line selection from manual inquiry to parameter-driven automatic matching, significantly shortening the design-to-production scheduling cycle.

[0256] Second, it achieves closed-loop parameter negotiation and historical self-optimization. Existing technologies lack clear parameter adjustment rules and reverse negotiation mechanisms, often leading to repeated communication via phone or conference when production line matching fails. This invention reads the parameter adjustment rule library through the parameter negotiation module, pushes suggestions and recommended adjustment values ​​for adjustable parameters to designers, and automatically updates the parameters and re-matches after designer confirmation. Simultaneously, the historical learning module records each negotiation decision and uses frequency statistics to optimize the subsequent recommendation order, enabling the system to have continuous self-optimization capabilities, eliminating the need for repeated negotiation for similar problems.

[0257] Third, ensuring the safety and transparency of production line conflict handling. Existing systems lack a safe preemption and migration mechanism based on task priority and original product delivery date; blind preemption can easily lead to delayed delivery of the original product. This invention uses a preemption arbitration module to judge based on priority weight ratio threshold, remaining delivery date threshold, and maximum migration count, and generates a preemption plan using migration or postponement strategies. Simultaneously, all preemption actions are manually verified through a three-level approval system to ensure transparent and controllable conflict handling.

[0258] Fourth, it meets the actual needs of continuous production on the core production line. Existing technology allows for fragmented scheduling, which seriously conflicts with the fast-paced nature of core production and the need for continuous operation of the core production line. This invention mandates that idle periods be continuous time intervals in the matching engine, prohibits fragmented time matching, and requires manufacturing personnel to confirm the continuous time interval occupancy in the approval and task release modules, ensuring that the scheduling results meet the actual production process requirements.

[0259] Fifth, the working hours data accurately reflects the actual production load. Existing information systems pre-bind working hours to production lines or equipment, failing to accurately reflect the true load. This invention obtains estimated working hours from a historical product database for time period prediction, and the actual working hours are dynamically entered by process engineers during the approval process, ensuring that scheduling deviations are minimized.

[0260] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0261] Based on the same inventive concept, this application also provides a production line matching apparatus for implementing the production line matching method described above. The solution provided by this apparatus is similar to the implementation described in the above method; therefore, the specific limitations in one or more production line matching apparatus embodiments provided below can be found in the limitations of the production line matching method described above, and will not be repeated here.

[0262] In one embodiment, such as Figure 11 As shown, a production line matching device 1100 is provided, including: an information acquisition module 1101, a hard constraint matching module 1102, a target production line determination module 1103, and a production scheduling scheme generation module 1104, wherein:

[0263] The information acquisition module 1101 is used to acquire the object design information of the object to be produced, as well as the production line processing capacity information of each production line. The object design information is used to describe the physical and technical specifications of the object to be produced, including at least one object design parameter. The production line processing capacity information is used to describe the range of product parameters and limitations that the corresponding production line can process.

[0264] The hard constraint matching module 1102 is used to perform hard constraint matching between the object to be scheduled and each production line according to the object design information and the processing capacity information of each production line, and obtain the hard constraint matching result. The hard constraint matching result is used to characterize whether there is a reserve production line. The reserve production line is the production line that meets the hard constraint conditions with the object to be scheduled. The hard constraint conditions include that the product parameter range of the production line covers the object design parameters of the object to be scheduled.

[0265] The target production line determination module 1103 is used to determine the target production line from the reserve production lines based on first information when there are reserve production lines; the first information includes the idle time period of the reserve production lines.

[0266] The production scheduling plan generation module 1104 is used to generate a production scheduling plan for the object to be scheduled based on the target production line.

[0267] In one embodiment, the first information further includes the expected delivery time of the object to be scheduled, the historical design information and historical production hours of each historical production object. The target production line determination module 1103 is used to obtain the expected delivery time of the object to be scheduled, as well as the historical design information and historical production hours of each historical production object; based on the object design information and each historical design information, it determines similar production objects from each historical production object that meet similarity conditions with the object to be scheduled; the similarity conditions include that the feature distance between the object design features represented by the object design information and the historical object design features represented by the corresponding historical design information is less than a preset distance threshold; based on the historical production hours of similar production objects, it determines the estimated production hours of the object to be scheduled; based on the estimated production hours and the expected delivery time, it determines the expected production scheduling time window of the object to be scheduled; and if, based on the expected production scheduling time window and the idle time period of the reserve production line, it determines that there is an idle production line among the reserve production lines, it determines the target production line of the object to be scheduled based on the idle production line.

[0268] In one embodiment, the production scheduling module 1104 is configured to: if there are no idle production lines in the preparation production lines, determine candidate preparation tasks from the preparation production lines according to the expected production scheduling time window; obtain the preparation task scheduling information of the candidate preparation tasks and the object priority of the objects to be scheduled; if it is determined that the candidate preparation tasks meet the preemption conditions according to the preparation task scheduling information and the object priority, determine the preemption strategy of the candidate preparation tasks based on the preparation task scheduling information; determine the production line to which the candidate preparation tasks that meet the preemption conditions belong as the preemption production line; and generate a production scheduling plan for the objects to be scheduled according to the preemption strategy and the preemption production line.

[0269] In one embodiment, the pre-job scheduling information includes the pre-job priority of candidate pre-job tasks and the remaining delivery time of the tasks. The production line matching device 1100 further includes:

[0270] The priority comparison module is used to obtain the minimum remaining delivery time of candidate pre-tasks when the ratio of the object priority of the object to be scheduled to the priority of the pre-task exceeds a preset weight ratio threshold.

[0271] The preemption condition judgment module is used to determine whether a candidate preparatory task meets the preemption condition if the remaining delivery time of the task is greater than or equal to the minimum remaining delivery deadline.

[0272] In one embodiment, the production line matching device 1100 further includes:

[0273] The module for determining design parameters to be adjusted is used to determine at least one design parameter to be adjusted from the object design information based on the hard constraint matching results when there is no pre-production line.

[0274] The adjustment strategy determination module is used to determine the target parameter adjustment strategy that matches the design parameter to be adjusted from multiple candidate parameter adjustment strategies.

[0275] The adjustment suggestion feedback module is used to generate parameter adjustment suggestions based on the target parameter adjustment strategy and the design parameters to be adjusted, and then feed the parameter adjustment suggestions back to the user terminal.

[0276] The adjustment instruction response module is used to respond to parameter adjustment instructions triggered by parameter adjustment suggestions, update the design parameters to be adjusted, and obtain the updated object design information.

[0277] The return to the execution module is used to return the execution based on the updated object design information. Based on the object design information and the processing capacity information of each production line, the steps of matching the objects to be scheduled with each production line according to hard constraints are performed to obtain the hard constraint matching results.

[0278] In one embodiment, the adjustment strategy determination module is used to: determine multiple selected parameter adjustment strategies that match the design parameter to be adjusted from multiple candidate parameter adjustment strategies; and determine the selected parameter adjustment strategy with the highest historical adoption rate as the target parameter adjustment strategy that matches the design parameter to be adjusted based on the historical adoption rate of each selected parameter adjustment strategy.

[0279] In one embodiment, the production line matching device 1100 further includes:

[0280] The multi-level approval process generation module is used to generate multi-level approval processes based on the production scheduling plan of the objects to be scheduled.

[0281] The task scheduling module is used to respond to approval events triggered by multi-level approval processes, and to bind the objects to be scheduled as production line tasks of the target production line to the corresponding production time period in the target production line.

[0282] Each module in the aforementioned production line matching device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0283] In one embodiment, a computer device is provided, which may be a server integrated with a production line matching system, and its internal structure diagram may be as follows: Figure 12 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores data related to the production line matching method. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements a production line matching method.

[0284] Those skilled in the art will understand that Figure 12 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0285] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the specific steps of the above-described production line matching method embodiment.

[0286] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the specific steps of the above-described production line matching method embodiment.

[0287] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the specific steps of the above-described production line matching method embodiment.

[0288] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the acquisition, storage, processing, and transmission of the data all comply with relevant laws and regulations.

[0289] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. 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 embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0290] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0291] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A production line matching method, characterized in that, The method includes: Obtain the object design information of the object to be produced, as well as the production line processing capacity information of each production line; the object design information is used to describe the physical and technical specifications of the object to be produced, including at least one object design parameter; the production line processing capacity information is used to describe the range and limitations of the product parameters processed by the corresponding production line. Based on the object design information and the processing capacity information of each production line, the object to be scheduled for production is matched with each production line using hard constraints to obtain hard constraint matching results. The hard constraint matching results are used to characterize whether there is a reserve production line. The reserve production line is the production line among the production lines that meets the hard constraint conditions with the object to be scheduled for production. The hard constraint conditions include that the range of product parameters that the production line can process covers the object design parameters of the object to be scheduled for production. In the presence of the prepared production line, obtain the expected delivery time of the production object to be scheduled, as well as the historical design information and historical production man-hours of each historical production object; Based on the object design information and each of the historical design information, similar production scheduling objects that meet the similarity conditions with the object to be scheduled are determined from each of the historical production objects; the similarity conditions include that the feature distance between the object design features represented by the object design information and the historical object design features represented by the corresponding historical design information is less than a preset distance threshold. Based on the historical production hours of the similar production scheduling objects, the estimated production hours of the object to be scheduled are determined; Based on the estimated production man-hours and the expected delivery time, determine the expected production scheduling time window for the object to be scheduled. If, based on the expected production scheduling time window and the idle time period of the prepared production line, it is determined that there is an idle production line among the prepared production lines, the target production line for the object to be scheduled is determined according to the idle production line. Generate a production scheduling plan for the object to be scheduled based on the target production line; If there is no idle production line among the prepared production lines, candidate preparatory tasks are determined from the prepared production lines according to the expected production scheduling time window; Obtain the pre-task scheduling information of the candidate pre-tasks, and the object priority of the objects to be scheduled; If the candidate pre-task meets the pre-emption conditions based on the pre-task scheduling information and the object priority, the pre-emption strategy for the candidate pre-task is determined based on the pre-task scheduling information. The production line to which the candidate preparatory task that meets the preemption conditions belongs is determined as the preemption production line; The production scheduling plan for the objects to be scheduled is generated based on the preemption strategy and the preemption production line.

2. The method according to claim 1, characterized in that, The preparatory task scheduling information includes the preparatory task priority and remaining delivery time of the candidate preparatory tasks; The method further includes: If the ratio of the object priority of the object to be scheduled to the priority of the preparatory task exceeds a preset weight ratio threshold, the minimum remaining delivery time of the candidate preparatory task is obtained. If the remaining delivery time of the task is greater than or equal to the minimum remaining delivery time, then the candidate preparatory task is determined to meet the preemption condition.

3. The method according to claim 1 or 2, characterized in that, The method further includes: If the hard constraint matching result indicates that there is no pre-production line that meets the hard constraint conditions among the production lines, at least one design parameter to be adjusted is determined from the object design information based on the hard constraint matching result. From multiple candidate parameter adjustment strategies, determine the target parameter adjustment strategy that matches the design parameter to be adjusted; Based on the target parameter adjustment strategy and the design parameters to be adjusted, parameter adjustment suggestions are generated and fed back to the user terminal. In response to the parameter adjustment instruction triggered by the parameter adjustment suggestion, the design parameters to be adjusted are updated to obtain the updated object design information; Based on the updated object design information, the process of performing hard constraint matching between the object to be scheduled and each of the production lines, according to the object design information and the processing capacity information of each production line, is returned to obtain the hard constraint matching result.

4. The method according to claim 3, characterized in that, The step of determining the target parameter adjustment strategy that matches the design parameter to be adjusted from multiple candidate parameter adjustment strategies includes: From multiple candidate parameter adjustment strategies, determine a number of selected parameter adjustment strategies that match the design parameter to be adjusted; Based on the historical adoption rate of each of the selected parameter adjustment strategies, the selected parameter adjustment strategy with the highest historical adoption rate is determined as the target parameter adjustment strategy that matches the design parameter to be adjusted.

5. The method according to claim 1 or 2, characterized in that, The method further includes: Based on the production scheduling plan of the objects to be scheduled, a multi-level approval process is generated; In response to the approval approval event triggered by the multi-level approval process, the production object to be scheduled is designated as a production line task of the target production line and bound to the corresponding production time period in the target production line.

6. A production line matching device, characterized in that, The device includes: The information acquisition module is used to acquire the object design information of the object to be produced and the processing capacity information of each production line; the object design information is used to describe the physical and technical specifications of the object to be produced, including at least one object design parameter; the production line processing capacity information is used to describe the range of product parameters and limitations that the corresponding production line can process. The hard constraint matching module is used to perform hard constraint matching between the object to be scheduled and each of the production lines based on the object design information and the processing capacity information of each production line, and obtain hard constraint matching results. The hard constraint matching results are used to characterize whether there is a reserve production line. The reserve production line is the production line among the production lines that meets the hard constraint conditions with the object to be scheduled. The hard constraint conditions include that the product parameter range of the production line covers the object design parameters of the object to be scheduled. The target production line determination module is used to, when the preparatory production line exists, acquire the expected delivery time of the object to be scheduled, as well as the historical design information and historical production hours of each historical production object; based on the object design information and the historical design information, determine similar production objects from the historical production objects that meet similarity conditions with the object to be scheduled; the similarity conditions include that the feature distance between the object design features represented by the object design information and the historical object design features represented by the corresponding historical design information is less than a preset distance threshold; based on the historical production hours of the similar production objects, determine the estimated production hours of the object to be scheduled; based on the estimated production hours and the expected delivery time, determine the expected production time window of the object to be scheduled; and, if based on the expected production time window and the idle time period of the preparatory production line, determine that there is an idle production line among the preparatory production lines, determine the target production line of the object to be scheduled based on the idle production line. The production scheduling module is used to generate a production scheduling plan for the object to be scheduled based on the target production line; if there is no idle production line among the reserve production lines, candidate reserve tasks are determined from the reserve production lines according to the expected production scheduling time window; the reserve task scheduling information of the candidate reserve tasks and the object priority of the object to be scheduled are obtained; if it is determined that the candidate reserve task meets the preemption condition based on the reserve task scheduling information and the object priority, the preemption strategy of the candidate reserve task is determined based on the reserve task scheduling information; the production line to which the candidate reserve task that meets the preemption condition belongs is determined as the preemption production line; and the production scheduling plan for the object to be scheduled is generated according to the preemption strategy and the preemption production line.

7. The apparatus according to claim 6, characterized in that, The pre-task scheduling information includes the pre-task priority and remaining delivery time of the candidate pre-tasks; the device further includes: The priority comparison module is used to obtain the minimum remaining delivery time of the candidate pre-task when the ratio of the object priority of the object to be scheduled to the priority of the pre-task exceeds a preset weight ratio threshold. The preemption condition judgment module is used to determine that the candidate preparatory task meets the preemption condition if the remaining delivery time of the task is greater than or equal to the minimum remaining delivery time.

8. The apparatus according to claim 6 or 7, characterized in that, The device further includes: The module for determining design parameters to be adjusted is used to determine at least one design parameter to be adjusted from the object design information based on the hard constraint matching results when the hard constraint matching results indicate that there is no pre-production line that meets the hard constraint conditions among the production lines. The adjustment strategy determination module is used to determine the target parameter adjustment strategy that matches the design parameter to be adjusted from multiple candidate parameter adjustment strategies. The adjustment suggestion feedback module is used to generate parameter adjustment suggestions based on the target parameter adjustment strategy and the design parameters to be adjusted, and to feed the parameter adjustment suggestions back to the user terminal. The adjustment instruction response module is used to respond to the parameter adjustment instruction triggered in response to the parameter adjustment suggestion, update the design parameters to be adjusted, and obtain the updated object design information; The return execution module is used to return to the execution step based on the updated object design information, and to perform hard constraint matching between the object to be scheduled and each of the production lines according to the object design information and the processing capacity information of each production line, so as to obtain the hard constraint matching result.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

10. A computer storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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