A method and system for optimizing hosiery workshop scheduling based on twin data

Abstract

The application provides a kind of based on twin data optimization hosiery workshop production scheduling method and system, it is related to textile intelligent manufacturing technical field, method includes: based on order data, equipment data and random disturbance data, the processing adaptation relationship between order process and hosiery equipment is established;Based on order completion data, equipment data and processing adaptation relationship, the remaining completion time of order process on hosiery equipment is predicted;Combine remaining completion time, order data and equipment data, calculate the dynamic priority index of order process;According to dynamic priority index and processing adaptation relationship, the equipment resource group corresponding to order process is matched, and the production scheduling scheme is generated;Based on random disturbance data and production scheduling scheme, the dynamic priority index is iteratively updated, and the updated production scheduling scheme is generated;The updated production scheduling scheme is input to digital twin system for simulation verification, and the dynamic production scheduling result of hosiery workshop is output.

Description

Technical Field

[0001] This invention relates to the field of intelligent manufacturing technology in textiles, and in particular to a method and system for optimizing production scheduling in a sock knitting workshop based on twin data. Background Technology

[0002] As a typical flexible assembly line operation, the hosiery workshop's production organization revolves around order processing. Order delivery, equipment utilization, and production rhythm coordination are key to improving operational efficiency. Facing the demands of multi-variety, variable-batch production, optimizing equipment resource allocation and efficiently managing the production process has become an important direction for high-quality development in the knitting manufacturing industry. Based on digital twin-based virtual-real mapping and real-time simulation technology, support can be provided for workshop production status perception, data interaction, and dynamic decision-making. The twin-data-driven scheduling model can provide a stable and reliable technical path for the production operation of the hosiery workshop.

[0003] Existing production scheduling technologies have undergone extensive research focusing on scheduling problems in flexible workshops. Static scheduling schemes can complete the basic allocation of orders and equipment in the initial stage, while intelligent optimization methods such as genetic algorithms and particle swarm optimization can solve and optimize scheduling schemes. The application of technologies such as order remaining processing time prediction, equipment maintenance plan collaboration, and real-time interactive production scheduling further enriches the workshop scheduling system and has positive significance for stabilizing production rhythm, standardizing production processes, and promoting the digital transformation of workshops.

[0004] However, the existing static scheduling model has limited adaptability to dynamic disturbances. In scenarios such as emergency order insertion, order cancellation, and regular equipment maintenance, the scheduling plan is prone to deviating from the actual working conditions, and the real-time performance of resource allocation and disturbance response needs to be improved. Summary of the Invention

[0005] To address the technical issues that existing static scheduling models have limited adaptability to dynamic disturbances, and that scheduling schemes are prone to becoming out of sync with actual working conditions in scenarios such as emergency order insertion, order cancellation, and regular equipment maintenance, the real-time performance of resource allocation and disturbance response needs to be improved.

[0006] The technical solution provided by this invention is as follows: A first aspect of this invention proposes a method for optimizing production scheduling in a hosiery workshop based on twin data, comprising: S1: Acquire order data, equipment data, order completion data, and random disturbance data from the hosiery workshop and synchronize them to the digital twin system; S2: Based on order data, equipment data, and random disturbance data, establish the processing compatibility relationship between order processes and hosiery knitting equipment; S3: Based on order completion data, equipment data, and processing compatibility, predict the remaining completion time of the order process on the hosiery knitting equipment; S4: Calculate the dynamic priority index of order processes by combining the remaining completion time, order data, and equipment data; S5: Based on dynamic priority indicators and processing compatibility, match the equipment resource groups corresponding to the order processes to generate a production scheduling plan; S6: Based on random disturbance data and production scheduling scheme, iteratively update the dynamic priority indicators and generate the updated production scheduling scheme; S7: Input the updated production scheduling plan into the digital twin system for simulation verification, and output the dynamic production scheduling results of the hosiery workshop.

[0007] A second aspect of this invention proposes a production scheduling system for a hosiery workshop based on twin data optimization, comprising: a processor and a memory; The memory stores programs or instructions that can run on the processor, and when the programs or instructions are executed by the processor, they implement the steps of the method for optimizing the production scheduling of the hosiery workshop based on twin data as described in the first aspect.

[0008] The beneficial effects of the technical solution provided by this invention include: In this embodiment of the invention, to address the limitations of existing production scheduling models in adapting to dynamic disturbances and their insufficient real-time response to disturbances, the invention acquires and synchronizes multi-source data from the workshop to a digital twin system, establishes processing adaptation relationships, accurately predicts remaining completion times, calculates and iteratively updates dynamic priority indicators, completes equipment resource group matching and scheduling scheme generation, and outputs the final production scheduling result after simulation verification. This system can respond in real-time to order insertions, order cancellations, and equipment maintenance disturbances, ensuring that the production scheduling scheme is consistent with actual working conditions, improving resource allocation and the real-time response to disturbances, ensuring balanced equipment load, and enhancing the stability and adaptability of the scheduling scheme. Attached Figure Description

[0009] Figure 1 This is a flowchart illustrating a method for optimizing production scheduling in a hosiery workshop based on twin data, provided by an embodiment of the present invention. Figure 2 This is a schematic diagram of a production scheduling system for a hosiery workshop based on twin data, provided as an embodiment of the present invention. Detailed Implementation

[0010] Reference manual attached Figure 1 The diagram illustrates a flowchart of a method for optimizing production scheduling in a hosiery workshop based on twin data, provided by an embodiment of the present invention.

[0011] This invention provides a method for optimizing production scheduling in a hosiery workshop based on twin data, which may include the following steps: S1: Obtain order data, equipment data, order completion data, and random disturbance data from the hosiery workshop and synchronize them to the digital twin system.

[0012] The order data is used to represent the order processing needs in the hosiery workshop, specifically including order arrival information, order process information, and order delivery deadline information. Order arrival information indicates the time when the order enters the hosiery workshop. Order process information indicates the processing task for the corresponding hosiery variety. Order delivery deadline information indicates the deadline by which the order needs to be completed.

[0013] Among them, equipment data is used to characterize the operating status and processing capacity of the hosiery knitting equipment.

[0014] Specifically, the equipment data includes sock knitting equipment operating status data, sock knitting equipment processing capacity data, and sock knitting equipment availability status data. The sock knitting equipment operating status data characterizes the current processing and operating status of the sock knitting equipment. The sock knitting equipment processing capacity data characterizes the range of sock varieties that the sock knitting equipment can process. The sock knitting equipment availability status data characterizes whether the sock knitting equipment is in idle, processing, or maintenance status.

[0015] The order completion data is used to characterize the processing completion status of the corresponding order process, specifically including the processing time of the order process, the current completion status, and the remaining processing status. The random disturbance data is used to characterize the random working condition changes in the hosiery workshop, specifically including order insertion data, order cancellation data, and equipment maintenance data.

[0016] Specifically, the physical hosiery workshop collects order data, equipment data, order completion data, and random disturbance data through real-time sensing technology, and synchronizes them to the digital twin system through the twin data layer, so that the virtual workshop can map the production status in the physical workshop in real time.

[0017] In this embodiment of the invention, by synchronizing order data, equipment data, order completion data, and random disturbance data to the digital twin system, a real-time data foundation can be provided for establishing the processing adaptation relationship between subsequent order processes and sock knitting equipment, predicting the remaining completion time, and dynamic production scheduling, thereby improving the real-time performance and accuracy of random production scheduling in the sock knitting workshop.

[0018] S2: Based on order data, equipment data, and random disturbance data, establish the processing adaptation relationship between order processes and hosiery knitting equipment.

[0019] Among them, the processing adaptation relationship is used to characterize the processable correspondence between order processes and hosiery knitting equipment.

[0020] Specifically, because different types of socks require different processing techniques, not all sock knitting equipment can complete all order processing steps.

[0021] In one possible implementation, S2 specifically includes sub-steps S201 to S204: S201: Extract order process information based on order data.

[0022] Among them, the order process information is used to characterize the processing task of the sock variety corresponding to the order.

[0023] Specifically, each order includes multiple processing steps for different types of socks, and different orders correspond to different processing requirements for different types of socks.

[0024] S202: Based on equipment data, extract the processing capacity information corresponding to the hosiery knitting equipment.

[0025] Among them, the processing capacity information is used to characterize the processing capacity of the socks corresponding to the sock knitting equipment.

[0026] Specifically, different sock knitting machines can process different types of socks, so different order processes correspond to different sets of sock knitting machines.

[0027] S203: Based on order process information and processing capacity information, establish the initial processing adaptation relationship between the order process and the hosiery knitting equipment.

[0028] Specifically, based on the sock variety requirements corresponding to the order process and the processing capacity of the sock knitting equipment, the processing correspondence between the order process and the sock knitting equipment is matched to generate an initial processing adaptation relationship.

[0029] In this embodiment, Boolean constraint variables are used to represent the processing compatibility between the order process and the hosiery knitting equipment, and their expressions are as follows: in, S i,j,k No. i The first order j The order process and the first k The processing compatibility between Taiwanese sock knitting equipment.

[0030] Specifically, S i,j,k =1 indicates that the corresponding hosiery knitting equipment has the processing capability for this order's process. S i,j,k =0 indicates that the corresponding sock knitting equipment does not have the processing capability for this order process.

[0031] S204: Update the initial processing adaptation relationship based on the random perturbation data to obtain the processing adaptation relationship.

[0032] Specifically, when order insertion, order cancellation, or equipment maintenance occurs in the sock knitting workshop, the processing relationships of some order processes corresponding to the sock knitting equipment change. Therefore, it is necessary to update the initial processing adaptation relationship based on random disturbance data.

[0033] In this embodiment of the invention, by establishing a processing compatibility relationship between order processes and sock knitting equipment, basic constraints can be provided for predicting the remaining completion time of subsequent order processes and matching equipment resource groups, thereby avoiding mismatch problems between order processes and sock knitting equipment and improving the executability of production scheduling schemes.

[0034] S3: Based on order completion data, equipment data, and processing compatibility, predict the remaining completion time of the order process on the sock knitting equipment.

[0035] The remaining completion time is used to characterize the remaining processing time of the order process on the corresponding hosiery knitting equipment.

[0036] Specifically, the remaining completion time serves as an important input parameter for subsequent dynamic priority index calculation and equipment resource group matching, and is used to characterize the current processing urgency of the order process.

[0037] In one possible implementation, S3 specifically includes sub-steps S301 to S304: S301: Extract the completion status information corresponding to the order process based on the order completion data.

[0038] The completion status information is used to characterize the current processing completion level of the order process, specifically including the processing time and the unprocessed time corresponding to the order process.

[0039] Specifically, the completion status information includes the processing time, unprocessed time, and current processing status of the corresponding order process.

[0040] S302: Extract the operating status information of the sock knitting equipment based on the equipment data.

[0041] Among them, the operating status information is used to characterize the current operating status of the hosiery knitting equipment, specifically including the equipment processing status, the equipment idle status, and the equipment maintenance status.

[0042] Specifically, the operating status information includes the equipment processing status, the equipment idle status, and the equipment maintenance status.

[0043] S303: Based on the completion status information, operation status information, and processing adaptation relationship, generate the predicted processing time corresponding to the order process.

[0044] Furthermore, based on the real-time workshop operation data collected by the digital twin system, a process-equipment predictive processing matrix is ​​constructed, the expression of which is as follows: in, T p This represents the process-equipment predictive processing matrix. T p(11,1) This indicates the predicted processing time for the first process of the first order on the first hosiery knitting machine. T p(11,2) This indicates the predicted processing time for the first process of the first order on the second hosiery knitting machine. T p(11,M) This indicates that the process of the first order is in the first order. M Predicted processing time on Taiwanese sock knitting equipment T p(12,1) This indicates the predicted processing time for the second order process of the first order on the first hosiery knitting machine. T p(12,2) This indicates the predicted processing time for the second step of the first order on the second hosiery knitting machine. T p(12,M) This indicates that the process of the second order in the first order is in the... M Predicted processing time on Taiwanese sock knitting equipment T p(ij,1) Indicates the first i The first order j The predicted processing time of the first process on the first hosiery knitting machine. T p(ij,2) Indicates the first i The first order j The predicted processing time of the next step on the second hosiery knitting machine. T p(ij,M) Indicates the first i The first order j The first process is in the M Predicted processing time on Taiwanese sock knitting equipment i Indicates the order number. j Indicates the order process number. M This indicates the total number of hosiery knitting machines.

[0045] Specifically, the process-equipment prediction processing matrix is ​​used to characterize the expected processing time of different order processes on different hosiery knitting machines, and serves as the basis for calculating the remaining completion time.

[0046] It should be noted that by constructing a process-equipment predictive processing matrix, the processing time relationship between different order processes and sock knitting equipment can be dynamically reflected, thereby improving the accuracy of subsequent remaining completion time prediction.

[0047] S304: Based on the predicted processing time, calculate the remaining completion time of the order process on the hosiery knitting equipment.

[0048] Specifically, the remaining processing time for the task can be calculated using the following formula: in, Indicates order n At any moment t The corresponding maximum remaining completion time, Indicates order n In hosiery knitting equipment k The predicted completion time is as follows. t Indicates the current time. n Indicates the order number. k Indicates the serial number of the sock knitting equipment. x This indicates the process number for the type of socks being knitted corresponding to the order.

[0049] Specifically, as the current scheduling time approaches the predicted completion time of the order, the remaining completion time of the corresponding order process gradually decreases.

[0050] It should be noted that by calculating the remaining completion time for each order process, the current urgency of the order process can be dynamically reflected, thus providing a time constraint basis for subsequent production scheduling priority calculation.

[0051] Specifically, the remaining processing time for each order process on the sock-knitting machine is calculated based on the processing time already completed for that process.

[0052] In this embodiment, the expression for calculating the remaining completion time of the order process on the sock-knitting machine is as follows: in, T r(ij,k) Indicates the first i The first order j The order process is in the first... k The remaining completion time on the Taiwanese sock-knitting equipment T p(ij,k) Indicates the first i The first order j The order process is in the first... k Predicted processing time on Taiwanese sock knitting equipment t Indicates the current time.

[0053] In this embodiment of the invention, by predicting the remaining completion time corresponding to the order process, a time constraint basis can be provided for the subsequent calculation of dynamic priority indicators, thereby improving the real-time response capability of random production scheduling in the hosiery workshop.

[0054] S4: Calculate the dynamic priority index of order processes by combining the remaining completion time, order data, and equipment data.

[0055] The dynamic priority index is used to characterize the current processing urgency of an order's process. The higher the dynamic priority index, the higher the scheduling priority of the corresponding order's process.

[0056] Specifically, the dynamic priority index is determined by the remaining completion time of the order process, the order delivery deadline, and the availability of the sock knitting equipment. When the delivery pressure for an order process is high and the sock knitting equipment is available, the dynamic priority index of the order process increases, thereby increasing the priority of that order process in subsequent production scheduling.

[0057] In one possible implementation, S4 specifically includes sub-steps S401 to S404: S401: Extract the delivery deadline information corresponding to the order based on the order data.

[0058] The delivery deadline information is used to characterize the deadline by which an order needs to be delivered.

[0059] Specifically, the delivery deadline information comes from the order delivery constraints in the order data and is used to subsequently calculate the delivery time leeway and dynamic priority indicators corresponding to the order process.

[0060] S402: Extract the available status information of the hosiery knitting equipment based on the equipment data.

[0061] The available status information is used to characterize whether the hosiery knitting equipment is currently available for order processing.

[0062] Specifically, the availability status information includes the idle status, processing status, and maintenance status of the sock knitting equipment, and is used to calculate the equipment availability of the sock knitting equipment.

[0063] S403: Generate urgency information for the corresponding order process based on the remaining completion time and delivery deadline information.

[0064] Specifically, the urgency of delivery for each order process is determined by comparing the remaining completion time of that process with the order delivery deadline.

[0065] Furthermore, the formula for calculating the delivery time margin for each order process is as follows: in, E ij Indicates the first i The first order j Delivery time margin for each order process D ij Indicates the first i The first order j Delivery deadlines for each order's process. T p(ij,k) Indicates the first i The first order j The order process is in the first... k Predicted processing time on Taiwanese sock knitting equipment.

[0066] Specifically, when E ij A value greater than 0 indicates that the process can be completed on schedule. E ij <0 When this occurs, it indicates that there is a risk of delay in the process.

[0067] S404: Calculate the dynamic priority index corresponding to the order process based on urgency information and availability status information.

[0068] Furthermore, based on the delivery time margin corresponding to the order process and the equipment availability corresponding to the sock knitting equipment, the basic priority index corresponding to the order process is calculated.

[0069] In this embodiment, the basic priority index for the order process is calculated using the following formula: in, P b ( ij ) indicates the first The first order The basic priority corresponding to each order process, the first i The first order j Delivery time margin for each order process D ij Indicates the first i The first order j Delivery deadlines for each order's process. g This indicates the preset scaling factor.

[0070] It should be noted that those skilled in the art can set the size of the preset ratio coefficient according to actual needs, and this invention does not limit this.

[0071] Specifically, the smaller the base priority value, the greater the delivery pressure for the order process, and the higher the corresponding scheduling priority.

[0072] Furthermore, to quantify the current operating status of the sock-knitting equipment and construct the overall availability of the equipment, its expression is as follows: in, B k Indicates the first k The overall availability of Taiwanese sock knitting equipment α This represents the weighting coefficient for device idle time. T 1k Indicates the first k Idle time corresponding to Taiwanese sock knitting equipment T lk Indicates the first k The load occupancy time of the Taiwanese sock knitting machine. β This indicates the weighting coefficient for the impact of equipment maintenance. T m This indicates the time required for equipment maintenance. T c This indicates the complete operating cycle of the equipment.

[0073] Specifically, overall equipment availability is used to characterize the current capacity of hosiery knitting equipment to participate in order processing.

[0074] It should be noted that by constructing a comprehensive equipment availability system, the impact of equipment idle status and equipment maintenance status on production scheduling can be comprehensively considered.

[0075] Furthermore, the formula for calculating the dynamic priority index corresponding to the order process is as follows: in, U n This indicates the weighting coefficient for the impact of equipment maintenance. p b This indicates the dynamic priority index corresponding to the order process. T r This indicates the remaining completion time for each process in the order.

[0076] Specifically, when the delivery time margin for an order process is small and the availability of the knitting equipment is high, the dynamic priority index for the order process increases, thereby increasing the priority of the order process in subsequent production scheduling.

[0077] Furthermore, the priority index for production scheduling can be calculated using the following formula: in, Indicates ordern Corresponding sock knitting process x In hosiery knitting equipment k Production scheduling priority indicators on the platform or This represents the production scheduling priority coefficient. Indicates order n At any moment t The corresponding maximum remaining completion time.

[0078] Specifically, the production scheduling priority index reflects the current urgency of an order's processing steps. When the remaining processing time for an order's processing step is short, the corresponding order's processing step tends to prioritize production resource groups with shorter remaining processing time.

[0079] It should be noted that by constructing production scheduling priority indicators, the order of order processes can be dynamically adjusted according to the current remaining processing status of the order processes, thereby reducing the risk of order delays.

[0080] In this embodiment of the invention, by combining the remaining completion time and delivery deadline of the order process with the equipment availability status of the sock knitting equipment to calculate the dynamic priority index, the production scheduling scheme of the sock knitting workshop can be dynamically changed with the order delivery pressure and equipment operating status, thereby improving the dynamic scheduling capability and order delivery stability of the sock knitting workshop under random working conditions.

[0081] S5: Based on the dynamic priority indicators and processing adaptation relationship, match the equipment resource groups corresponding to the order process to generate a production scheduling plan.

[0082] Among them, the equipment resource group is used to characterize the set of hosiery knitting equipment that meets the processing requirements of the order process.

[0083] Specifically, different order processes correspond to different processing capacities of sock-knitting equipment, and therefore different order processes correspond to different equipment resource groups. The production scheduling scheme is used to characterize the processing sequence, processing time, and allocation relationship of sock-knitting equipment corresponding to the order processes.

[0084] In one possible implementation, S5 specifically includes sub-steps S501 to S504: S501: Prioritize order processes based on dynamic priority indicators.

[0085] Specifically, based on the dynamic priority indicators corresponding to the order process, the order processes are sorted from high to low priority.

[0086] The higher the dynamic priority index, the higher the scheduling priority of the order process.

[0087] S502: Based on the priority ranking results and processing compatibility, select candidate hosiery knitting equipment corresponding to the order process.

[0088] Among them, candidate hosiery knitting equipment is used to characterize the set of hosiery knitting equipment that meets the processing capability requirements of order processes.

[0089] Specifically, based on the priority ranking of the order process and the processing compatibility between the order process and the sock knitting equipment, candidate sock knitting equipment that can complete the corresponding order process processing task is selected.

[0090] S503: Match the equipment resource groups corresponding to the order process based on the availability status information of the candidate sock knitting equipment.

[0091] Among them, equipment resource group matching is used to determine the allocation relationship of sock knitting equipment corresponding to the order process.

[0092] Specifically, the equipment resource groups corresponding to the order process are matched based on the idle status, processing status, and maintenance status of the candidate sock knitting equipment.

[0093] In one possible implementation, S503 specifically includes sub-steps S5031 to S5033: S5031: Determine the scheduling buffer time corresponding to the order process based on the available status information and dynamic priority indicators of the candidate hosiery knitting equipment.

[0094] The scheduling buffer time is used to characterize the time buffer reserved during the matching of equipment resource groups for order processes.

[0095] Specifically, when the dynamic priority of an order process is high, the scheduling buffer time is smaller. When the dynamic priority of an order process is low, the scheduling buffer time is larger.

[0096] Furthermore, to reduce the random interference caused by equipment parameter adjustments and personnel movement on production scheduling, a scheduling buffer time is introduced, the expression of which is as follows: in, T b(i,j,t) Indicates the first i The first of the orders j Each type of sock is available in t The corresponding scheduling buffer time at each moment, c Indicates the buffer correction factor. U n,q+1 Indicates the first q Orders in +1 iteration n Updated dynamic priority metrics T c This indicates the average time taken to adjust equipment parameters. l 1 indicates the influence coefficient of equipment parameter adjustment.T j Indicates the time taken for personnel to move around. l 2 represents the impact coefficient of personnel mobility.

[0097] Specifically, the scheduling buffer time is used to reserve a time buffer during the matching of equipment resource groups, thereby providing time redundancy for equipment parameter adjustment and personnel movement.

[0098] It should be noted that by introducing a scheduling buffer time, the impact of random disturbances on the stability of production scheduling can be reduced, thereby improving the feasibility of the production scheduling scheme.

[0099] S5032: Based on the scheduling buffer time, adjust the scheduling time of the equipment resource group corresponding to the candidate sock knitting equipment to obtain the adjusted equipment resource group.

[0100] Specifically, in order to construct the time segment intervals corresponding to the order processes, the total theoretical completion time for the order is calculated, and the expression is as follows: in, TC i Indicates the first i The total theoretical completion time for each order. J i Indicates the first i Each order contains a set of processes for knitting various types of socks. E Represents the mathematical expectation operation. T r(ij,k) Indicates the first i The first order j The order process is in the first... k The remaining completion time on the Taiwanese sock-knitting equipment ∑ This represents the summation operation. It indicates that it belongs to.

[0101] Specifically, the total theoretical completion time is used to characterize the theoretical processing time required for an order to complete all the sock-knitting processes under the current scheduling state.

[0102] It should be noted that by calculating the total theoretical completion time of the order, an overall time constraint basis can be provided for subsequent time segmentation processing.

[0103] Furthermore, the total theoretical completion time of the order is differentially divided into equal parts, and the expression is as follows: in, ΔT i Indicates the first i The average time length of the difference between each order Q This indicates the order of the difference piecewise division.

[0104] Specifically, the differential segmentation order is used to control the segmentation accuracy of order processing time. Q As the size increases, the corresponding time segments become more refined.

[0105] It should be noted that by differentially dividing the total theoretical completion time of orders, the time matching accuracy in the subsequent idle time insertion scheduling process can be improved.

[0106] Furthermore, a differential time node sequence is constructed based on the differential equalization time length, and its expression is as follows: in, T i,q Indicates the first i Differential time points corresponding to each order t Indicates the current time. q This indicates the sequence number of the differential time node.

[0107] Specifically, the differential time node sequence is used to construct insertable scheduling nodes for order processes in different time intervals.

[0108] It should be noted that by constructing a differential time node sequence, the matching ability between the idle time window of the equipment and the order process can be improved.

[0109] Furthermore, in order to dynamically adjust the order insertion interval width based on the device's idle status, an adaptive insertion time interval width based on idle status is constructed, the expression of which is as follows: in, ΔT i,k This represents the idle time range of the i-th order on the hosiery machine, with an adaptive insertion time width. f Represents the idle sensitivity coefficient. oh Indicates the range scaling factor. B k Indicates the first k Overall availability of Taiwanese sock knitting equipment.

[0110] S5033: Match the equipment resource groups corresponding to the order processes according to the adjusted equipment resource groups.

[0111] Specifically, based on the adjusted equipment resource group according to the scheduling time, the sock knitting equipment resources corresponding to the order process are re-matched.

[0112] S504: Generate a production scheduling plan based on the equipment resource group matching results.

[0113] Specifically, based on the matching results of the equipment resource groups corresponding to the order process, the processing sequence, processing time, and allocation relationship of the sock knitting equipment corresponding to the order process are determined, thereby generating a production scheduling plan.

[0114] Furthermore, based on the differential time node, the insertion time interval width, and the scheduling buffer time, the actual completion time of the order is determined, and its expression is as follows: in, C i Indicates the first i The actual completion time for each order. max This indicates the operation of retrieving the maximum value.

[0115] Specifically, the actual completion time of an order is determined by the final completion time of all the sock-knitting processes in the order.

[0116] It should be noted that by constructing an actual order completion time model, the impact of equipment idle status and scheduling buffer mechanism on order completion time can be comprehensively considered.

[0117] Furthermore, to evaluate the overall production scheduling results of the hosiery workshop, the maximum global completion time of the workshop is constructed, and its expression is as follows: in, C max This indicates the maximum completion time for the entire hosiery workshop. minutes This indicates the operation of finding the minimum value. C 1. C 2,..., C n These represent the actual completion time for each order.

[0118] Specifically, the maximum completion time of the entire workshop is used to characterize the final completion efficiency of the overall production scheduling plan for the hosiery workshop.

[0119] It should be noted that by introducing a scheduling buffer time mechanism during the equipment resource group matching process, the random interference caused by equipment parameter adjustments and personnel movement on the production scheduling of the sock knitting workshop can be reduced, thereby improving the stability and feasibility of the production scheduling plan.

[0120] In this embodiment of the invention, by constructing a device resource group matching mechanism based on dynamic priority indicators and adjusting the scheduling time of the device resource group in conjunction with the scheduling buffer time, dynamic resource allocation between order processes and sock knitting equipment is realized, thereby improving the utilization rate of equipment resources in the sock knitting workshop and the stability of dynamic scheduling under random working conditions.

[0121] S6: Based on random disturbance data and production scheduling scheme, the dynamic priority index is iteratively updated, and the updated production scheduling scheme is generated.

[0122] Among them, random disturbance data is used to characterize dynamic events that cause changes in the original production scheduling plan during the production process of the hosiery workshop, specifically including order insertion data, order cancellation data, and equipment maintenance data.

[0123] Specifically, order insertion data represents the processing demand generated after new orders enter the hosiery workshop. Order cancellation data represents the resource release demand generated after allocated orders are cancelled. Equipment maintenance data represents the equipment unavailability demand generated after hosiery equipment is taken out of normal production during a preset maintenance period.

[0124] Furthermore, the iterative update of the dynamic priority index is used to readjust the scheduling order of order processes based on random disturbance events and the remaining completion time of the order process, so that the production scheduling scheme can adapt to the dynamic random working conditions in the hosiery workshop.

[0125] In one possible implementation, S6 specifically includes sub-steps S601 to S604: S601: Based on random perturbation data, identify order insertion events, order cancellation events, and equipment maintenance events.

[0126] Among them, the order insertion event is used to characterize the event that after a new order arrives in the hosiery workshop, the new order needs to be inserted into the original production scheduling plan; the order cancellation event is used to characterize the event that after the original order is cancelled, the hosiery equipment resources occupied by the order need to be released; and the equipment maintenance event is used to characterize the event that the hosiery equipment exits the normal production state according to the preset maintenance plan.

[0127] Specifically, order insertion events will change the processing sequence of existing order processes and the allocation relationship of sock knitting equipment. Therefore, it is necessary to re-determine the processing compatibility relationship between the new order process and the sock knitting equipment based on the order insertion event, and recalculate the dynamic priority index of the corresponding order process.

[0128] Specifically, order cancellation events will release some of the processing time slots of the original production scheduling plan. Therefore, it is necessary to reallocate the remaining order processes according to the order cancellation events so that the remaining order processes can fill the idle time slots of the released equipment.

[0129] Specifically, when a maintenance event occurs, the corresponding hosiery knitting equipment cannot continue to undertake order processing tasks during the maintenance period. Therefore, the maintenance time needs to be included as a constraint in the production scheduling adjustment process.

[0130] It should be noted that by identifying order insertion events, order cancellation events, and equipment maintenance events, the production scheduling plan can be adjusted in a timely manner when random disturbances occur, thereby preventing the original production scheduling plan from becoming ineffective due to changes in random operating conditions.

[0131] S602: Adjust the production scheduling plan based on order insertion events, order cancellation events, and equipment maintenance events.

[0132] Among them, the production scheduling scheme adjustment is used to redetermine the processing sequence, processing time and allocation relationship of hosiery knitting equipment corresponding to the order process based on random disturbance events.

[0133] Specifically, when an order insertion event occurs, the new order's process is incorporated into the production scheduling plan based on the corresponding order process information, delivery deadline information, and processing compatibility. When an order cancellation event occurs, the processing time slots occupied by the cancelled order on the sock knitting equipment are released based on the order process information corresponding to the cancelled order. When an equipment maintenance event occurs, the processing arrangements for order processes related to the sock knitting equipment under maintenance are adjusted based on the maintenance status information of the sock knitting equipment.

[0134] In one possible implementation, S602 specifically includes sub-steps S6021 to S6024: S6021: Based on a digital twin system, monitor the order process start status corresponding to order insertion events, order cancellation events, and equipment maintenance events.

[0135] The order process start status is used to indicate whether the order process has started processing.

[0136] Specifically, the digital twin system captures the start status of order processes in real time through a virtual workshop. When the sock-knitting equipment in the virtual workshop starts executing the current order process, the digital twin system synchronously obtains the start information of the order process and uses this start information as the basis for triggering subsequent production scheduling.

[0137] S6022: Based on the order process start status, trigger the production scheduling corresponding to the next order process to be scheduled.

[0138] Specifically, when the order process start status indicates that the current order process has started processing, the digital twin system triggers the scheduling process for the next order process to be scheduled.

[0139] Furthermore, the next order process to be scheduled is either the remaining processing task in the current order that has not yet been scheduled, or the processing process corresponding to a randomly arriving new order.

[0140] S6023: Based on the dynamic priority index, re-match the equipment resource group corresponding to the next scheduled order process to obtain the re-matching result.

[0141] The rematching result is used to characterize the reassignment relationship of the hosiery equipment after the next scheduled order process is re-determined following a random disturbance.

[0142] Specifically, the digital twin system re-matches the equipment resource groups corresponding to the next order process to be scheduled based on the dynamic priority index and processing adaptation relationship, so that the order process with higher dynamic priority can obtain the appropriate sock knitting equipment resources first.

[0143] S6024: Adjust the production scheduling plan based on the rematching results.

[0144] Specifically, based on the rematching results of the equipment resource groups, the processing sequence, processing time, and allocation relationship of the sock knitting equipment in the production scheduling plan are adjusted.

[0145] Furthermore, the adjusted production scheduling scheme is used for subsequent dynamic priority indicator updates and the generation of updated production scheduling schemes.

[0146] It should be noted that by monitoring the order process start status based on the digital twin system and triggering the scheduling of the next order process to be scheduled according to the order process start status, an event-driven dynamic scheduling process can be formed. This enables the production scheduling of the hosiery workshop to be updated in real time with the order processing progress and random disturbance events, thereby improving the response speed of dynamic production scheduling.

[0147] S603: Update the dynamic priority indicators based on the adjusted production scheduling plan and the remaining completion time.

[0148] The adjusted production scheduling scheme is used to characterize the processing arrangements for orders regenerated after a random disturbance event. The remaining completion time is used to characterize the processing time that an order's process still needs to complete under the current scheduling state.

[0149] Specifically, during the random scheduling simulation, the completion time obtained from the previous simulation is used as the basis for updating the scheduling priority index in the next round, and the dynamic priority index corresponding to the order process is corrected through iteration.

[0150] Specifically, after a random disturbance event occurs, the dynamic priority indicator needs to be iteratively updated based on the updated production scheduling plan and the actual completion status of the order process. Its expression is as follows: in, U n,q+1Indicates the first q Orders in +1 iteration n Updated dynamic priority metrics t Represents the normalization coefficient. T r(ij,k) Indicates the first i The first order j The order process is in the first... k The remaining completion time on the Taiwanese sock-knitting equipment t q Indicates the first q The next iteration triggers the scheduling time. s This represents the deviation correction factor. ACT n,q Indicates the first q Orders in the next iteration n The corresponding actual completion time.

[0151] Specifically, when the previous simulation results show that the actual completion time of an order exceeds the delivery deadline, the dynamic priority of the corresponding order in the next round of scheduling is increased by the deviation term, so that orders with the risk of delay can obtain higher scheduling priority in subsequent production scheduling.

[0152] It should be noted that by iteratively updating the dynamic priority index based on the adjusted production scheduling plan and the remaining completion time, the production scheduling plan of the hosiery workshop can be continuously corrected based on the simulation results, thereby reducing the risk of order delays and enhancing the adaptability of the production scheduling plan to random disturbances.

[0153] Furthermore, based on the predicted completion time corresponding to the order process, the dynamic priority update model is modified, and its expression is as follows: in, OSDT n,k Indicates order n In the k Predicted completion time on Taiwanese sock knitting equipment This represents the absolute value of the deviation between the actual completion time and the predicted completion time of an order.

[0154] S604: Generate an updated production scheduling plan based on the updated dynamic priority indicators.

[0155] The updated dynamic priority index is used to characterize the latest scheduling priority of order processes after a random disturbance occurs.

[0156] Specifically, based on the updated dynamic priority indicators, the order processes are reordered, and the equipment resource groups corresponding to the order processes are rematched according to the processing adaptation relationship to obtain the updated production scheduling plan.

[0157] Furthermore, the updated production scheduling scheme is input into the digital twin system for simulation verification to determine the effectiveness of the scheme in the virtual workshop.

[0158] It should be noted that by generating updated production scheduling schemes based on updated dynamic priority indicators, the hosiery workshop can quickly form new production scheduling arrangements under random conditions such as order insertion, order cancellation, and equipment maintenance, thereby improving the continuity and stability of the production process.

[0159] S7: Input the updated production scheduling plan into the digital twin system for simulation verification, and output the dynamic production scheduling results of the hosiery workshop.

[0160] Among them, the updated production scheduling scheme is used to characterize the processing sequence, processing time, and allocation relationship of the sock knitting equipment for orders regenerated after random disturbances occur.

[0161] Specifically, the digital twin system uses a virtual workshop to simulate and verify the updated production scheduling scheme, and determines whether the updated production scheduling scheme can meet the production scheduling needs of the hosiery workshop based on the simulation verification results.

[0162] In one possible implementation, S7 specifically includes sub-steps S701 to S704: S701: Synchronize the updated production scheduling plan to the digital twin system.

[0163] The digital twin system is used to receive the updated production scheduling plan and map the plan to the virtual hosiery workshop.

[0164] Specifically, after the updated production scheduling plan is synchronized to the digital twin system, the virtual hosiery workshop updates the order process sequence, hosiery equipment operating status, and order completion status according to the plan.

[0165] S702: Based on the digital twin system, the updated production scheduling scheme is simulated and verified.

[0166] Among them, simulation verification is used to simulate the execution process of the updated production scheduling scheme in a virtual hosiery workshop.

[0167] Specifically, the digital twin system uses order data, equipment data, order completion data, and random disturbance data to simulate random production scheduling based on the updated production scheduling plan, in order to verify the effectiveness of the plan in a virtual hosiery workshop.

[0168] S703: Based on the simulation verification results, determine the completion result corresponding to the order process.

[0169] The completion result is used to characterize the processing completion status of the order process under the updated production scheduling scheme.

[0170] Specifically, the completion results include the completion sequence of the order's processes, the completion time, and the allocation results of the corresponding sock knitting equipment.

[0171] S704: Based on the completion results, output the dynamic production scheduling results of the hosiery workshop.

[0172] Among them, the dynamic production scheduling results of the hosiery workshop are used to characterize the final production scheduling scheme after simulation verification by the digital twin system.

[0173] Specifically, based on the completion results corresponding to the order process, the dynamic production scheduling results of the sock knitting workshop are output, and the dynamic production scheduling results are fed back to the physical sock knitting workshop for execution.

[0174] It should be noted that by inputting the updated production scheduling plan into the digital twin system for simulation verification, the plan can be simulated and verified before execution in the physical sock knitting workshop. This reduces the impact of order delays, equipment conflicts, and maintenance conflicts on actual production and improves the reliability of the dynamic production scheduling results in the sock knitting workshop.

[0175] In this embodiment of the invention, by combining random disturbance event identification, order process start-up status triggering, dynamic priority index iterative update and digital twin simulation verification, a closed-loop dynamic production scheduling process is formed for order insertion, order cancellation and equipment maintenance scenarios. This enables the hosiery workshop to quickly adjust the production scheduling plan under random working conditions, improve equipment resource utilization, order delivery stability and production scheduling continuity.

[0176] Reference manual attached Figure 2 The diagram shows a schematic representation of a production scheduling system for a hosiery workshop based on twin data, provided by an embodiment of the present invention.

[0177] This invention provides a production scheduling system 20 for a hosiery workshop based on twin data optimization, comprising: a processor 201 and a memory 202; The memory 202 stores programs or instructions that can run on the processor 201. When the program or instructions are executed by the processor 201, they implement the steps of the above-mentioned method for optimizing the production scheduling of the hosiery workshop based on twin data and achieve the same technical effect. To avoid repetition, the present invention will not elaborate further.

[0178] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for optimizing production scheduling in a hosiery workshop based on twin data, characterized in that, include: S1: Obtain order data, equipment data, order completion data, and random disturbance data from the hosiery workshop and synchronize them to the digital twin system; S2: Based on the order data, the equipment data, and the random disturbance data, establish a processing adaptation relationship between the order process and the hosiery knitting equipment; S3: Based on the order completion data, the equipment data, and the processing adaptation relationship, predict the remaining completion time of the order process on the sock knitting equipment; S4: Calculate the dynamic priority index of the order process by combining the remaining completion time, the order data, and the equipment data; S5: Based on the dynamic priority index and the processing adaptation relationship, match the equipment resource groups corresponding to the order process to generate a production scheduling plan; S6: Based on the random disturbance data and the production scheduling scheme, iteratively update the dynamic priority index and generate an updated production scheduling scheme; S7: Input the updated production scheduling scheme into the digital twin system for simulation verification, and output the dynamic production scheduling results of the hosiery workshop.

2. The method for optimizing production scheduling in a hosiery workshop based on twin data according to claim 1, characterized in that, S2 specifically includes: S201: Extract order process information based on the order data; S202: Based on the equipment data, extract the processing capacity information corresponding to the hosiery knitting equipment; S203: Based on the order process information and the processing capacity information, establish an initial processing adaptation relationship between the order process and the hosiery knitting equipment; S204: Update the initial processing adaptation relationship based on the random perturbation data to obtain the processing adaptation relationship.

3. The method for optimizing production scheduling in a hosiery workshop based on twin data according to claim 1, characterized in that, S3 specifically includes: S301: Extract the completion status information corresponding to the order process based on the order completion data; S302: Extract the operating status information corresponding to the sock knitting equipment based on the equipment data; S303: Based on the completion status information, the operation status information, and the processing adaptation relationship, generate the predicted processing time corresponding to the order process; S304: Based on the predicted processing time, calculate the remaining completion time of the order process on the sock knitting machine.

4. The method for optimizing production scheduling in a hosiery workshop based on twin data according to claim 1, characterized in that, S4 specifically includes: S401: Extract the delivery deadline information corresponding to the order based on the order data; S402: Extract the available status information corresponding to the sock knitting equipment based on the equipment data; S403: Generate urgency information corresponding to the order process based on the remaining completion time and the delivery deadline information; S404: Calculate the dynamic priority index corresponding to the order process based on the urgency information and the availability status information.

5. The method for optimizing production scheduling in a hosiery workshop based on twin data according to claim 1, characterized in that, S5 specifically includes: S501: Prioritize the order processes according to the dynamic priority index; S502: Based on the priority ranking result and the processing adaptation relationship, filter the candidate sock knitting equipment corresponding to the order process; S503: Match the equipment resource group corresponding to the order process according to the availability status information of the candidate sock knitting equipment; S504: Generate a production scheduling plan based on the equipment resource group matching results.

6. The method for optimizing production scheduling in a hosiery workshop based on twin data according to claim 5, characterized in that, S503 specifically includes: S5031: Determine the scheduling buffer time corresponding to the order process based on the available status information and the dynamic priority index corresponding to the candidate sock knitting equipment; S5032: Based on the scheduling buffer time, adjust the scheduling time of the equipment resource group corresponding to the candidate sock knitting equipment to obtain the adjusted equipment resource group; S5033: Match the equipment resource group corresponding to the order process according to the adjusted equipment resource group.

7. The method for optimizing production scheduling in a hosiery workshop based on twin data according to claim 1, characterized in that, S6 specifically includes: S601: Based on the random disturbance data, identify order insertion events, order cancellation events, and equipment maintenance events; S602: Adjust the production scheduling plan based on the order insertion event, the order cancellation event, and the equipment maintenance event; S603: Update the dynamic priority index according to the adjusted production scheduling plan and the remaining completion time; S604: Generate the updated production scheduling scheme based on the updated dynamic priority index.

8. The method for optimizing production scheduling in a hosiery workshop based on twin data according to claim 7, characterized in that, Specifically, S602 includes: S6021: Based on the digital twin system, monitor the order process start status corresponding to the order insertion event, the order cancellation event, and the equipment maintenance event; S6022: Based on the order process start status, trigger the production scheduling corresponding to the next order process to be scheduled; S6023: Based on the dynamic priority index, the equipment resource group corresponding to the next scheduled order process is re-matched to obtain the re-matching result; S6024: Adjust the production scheduling scheme according to the rematching result.

9. The method for optimizing production scheduling in a hosiery workshop based on twin data according to claim 1, characterized in that, Specifically, S7 includes: S701: Synchronize the updated production scheduling scheme to the digital twin system; S702: Based on the digital twin system, the updated production scheduling scheme is simulated and verified; S703: Based on the simulation verification results, determine the completion result corresponding to the order process; S704: Based on the completion results, output the dynamic production scheduling results for the hosiery workshop.

10. A production scheduling system for a hosiery workshop based on twin data optimization, characterized in that, include: Processor and memory; The memory stores programs or instructions that can run on the processor, and when the programs or instructions are executed by the processor, they implement the steps of the method for optimizing the production scheduling of a hosiery workshop based on twin data as described in any one of claims 1 to 9.

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