A Method and System for Responding to Order Insertion in Garment Production Based on Dynamic Simulation

Abstract

This invention relates to the field of intelligent manufacturing and production scheduling technology in the garment industry, and discloses a method and system for responding to order insertions in garment production based on dynamic simulation. The method includes: constructing a digital twin model of the production environment; initiating order insertion simulation on the digital twin model based on a preset disturbance factor, and capturing each dynamic event caused by resource contention during the simulation; quantifying the impact of order insertion based on all dynamic events captured throughout the simulation cycle; and reorganizing the process route based on the quantified evaluation report to obtain the process route after the occurrence of order insertion. This invention can improve the efficiency of order insertion response in garment production based on dynamic simulation.

Description

Technical Field

[0001] This invention relates to the field of intelligent manufacturing and production scheduling technology for clothing, and in particular to a method and system for responding to order insertions in clothing production based on dynamic simulation. Background Technology

[0002] In the field of intelligent manufacturing for apparel, urgent order insertions during the production process are one of the core factors causing scheduling disruptions. Current technologies, when handling order insertions, typically rely on manual experience for rough allocation of equipment resources or static scheduling adjustments, lacking the ability to perceive and quantitatively assess dynamic changes in the production environment in real time. Such methods cannot accurately capture the complex resource conflicts arising from the insertion tasks and the original production plan in terms of equipment resources, worker skill levels, and work-in-process flow. They also struggle to predict the delayed impact of order insertions on the delivery cycle of existing orders. When the priority of the insertion task is high, static adjustment strategies often forcibly interrupt the existing continuous workflow of the production line, leading to disrupted production rhythms, a significant decline in overall output efficiency, and a substantial increase in the risk of unplanned equipment downtime and work-in-process inventory buildup.

[0003] Traditional scheduling mechanisms generally neglect the real-time degradation of production resources and its disturbance effect on scheduling results. Dynamic factors such as the dynamic changes in equipment reliability under different load ranges and the standard working hour deviation caused by worker fatigue during continuous operation are not included as key variables in the order insertion response model. Existing systems cannot construct a high-fidelity production line mapping model based on actual data flow from the production floor to simulate the dynamic game process of multiple tasks under order insertion disturbances, nor can they quantify the production line stability disturbances and resource efficiency losses caused by order insertions using multi-dimensional indicators. This lack of dynamic simulation and closed-loop feedback optimization means that process route reorganization always lags behind the evolution of actual production status, resulting in low reliability of order insertion execution plans, high costs of repeated adjustments, and difficulty in efficiently inserting urgent tasks while ensuring the reliability of existing order delivery. Therefore, improving the order insertion response efficiency in garment production has become an urgent problem to be solved. Summary of the Invention

[0004] This invention provides a method and system for responding to order insertions in garment production based on dynamic simulation, in order to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a garment production order insertion response method based on dynamic simulation, comprising:

[0006] Build a digital twin model of the production environment;

[0007] Based on a preset disturbance factor, an insertion simulation is initiated on the digital twin model, and every dynamic event caused by resource contention during the simulation is captured.

[0008] The impact of order insertion is quantified based on all dynamic events captured throughout the entire simulation period.

[0009] Based on the quantified evaluation report, the process route is reorganized to obtain the process route after the insertion of orders.

[0010] In a preferred embodiment, the construction of the digital twin model of the production environment includes:

[0011] The system acquires information about the RFID tags attached to the work-in-process products to obtain the product inventory data. At the same time, it acquires the spindle load rate, operating speed, and temperature signals from the programmable logic controller of the equipment to obtain the equipment operating status parameters.

[0012] Retrieve historical operation records bound to the worker's login ID, perform statistical processing on the historical operation records, and generate skill proficiency tags for the worker's proficiency in specific work processes;

[0013] The inventory data of the products, the operating status parameters of the equipment, and the skill proficiency tags are mapped to the corresponding workstation nodes in the three-dimensional layout diagram of the production line to generate a digital twin model that uses node-connection relationships to represent the process flow logic and node attributes to represent real-time load and production capacity.

[0014] In a preferred embodiment, the disturbance factor includes: an equipment reliability disturbance factor and a human fatigue disturbance factor, wherein the equipment reliability disturbance factor includes:

[0015] Retrieve historical fault repair records corresponding to each device currently participating in the simulation, wherein the historical fault repair records include the timestamp of the fault occurrence and a description of the fault cause;

[0016] Based on the historical fault repair records, the frequency of faults occurring in each device within different load state ranges is statistically analyzed to generate a device load-fault correlation graph.

[0017] Based on the load state range of the spindle load rate, the corresponding equipment performance degradation trend is extracted from the equipment load-fault correlation graph to generate the equipment reliability disturbance factor.

[0018] In a preferred embodiment, the human-caused fatigue disturbance factor includes:

[0019] Real-time retrieval of each worker's current shift's cumulative working hours;

[0020] Based on the skill proficiency tags, determine the standard operating rhythm benchmark for each worker;

[0021] When the cumulative working hours of the current shift exceed the preset fatigue accumulation threshold, the deviation of the worker's actual operating rhythm from the standard operating rhythm benchmark is analyzed to obtain the human fatigue disturbance factor.

[0022] In a preferred embodiment, initiating a single-order insertion simulation on the digital twin model based on a preset perturbation factor includes:

[0023] The process of inserting a task is broken down into semi-finished products to be processed, and the semi-finished products to be processed are sequentially placed into the corresponding workstation nodes in the digital twin model according to the process flow logic.

[0024] When the semi-finished products to be processed arrive at each workstation node, the available time window of the equipment in the simulation cycle is dynamically adjusted according to the equipment performance degradation trend characterized by the equipment reliability disturbance factor, so as to obtain the equipment availability status after the available time window is reduced.

[0025] When the semi-finished products to be processed are allocated to each worker, the standard processing time per piece corresponding to the skill proficiency label is dynamically adjusted according to the probability of operation efficiency decay represented by the human fatigue disturbance factor, so as to obtain the fatigue-corrected processing time per piece.

[0026] Based on the available status of the equipment and the single-piece processing time after fatigue correction, the semi-finished products of the inserted task and the semi-finished products of the existing production task are driven to simulate resource contention at each workstation node.

[0027] When multiple semi-finished products compete for the same equipment or the same worker, they are sorted according to the priority labels of the tasks to obtain the simulated scheduling results.

[0028] During the simulated process, the changes in the number of semi-finished products in front of each workstation node, the actual operation and shutdown status switching events of the equipment, and the actual processing time of each semi-finished product unit are monitored and recorded in real time to obtain dynamic events.

[0029] In a preferred embodiment, during the simulated advancement process, the changes in the number of semi-finished products queuing at each workstation node, the actual operation and shutdown status switching events of the equipment, and the actual processing time of each semi-finished product unit are monitored and recorded in real time to obtain dynamic events, including:

[0030] According to the rhythm of the analog clock, the length of the queue of semi-finished products to be processed in the input buffer is periodically collected. When the difference between the queue lengths of two adjacent sampling times exceeds the preset queue fluctuation sensitivity threshold, a queue quantity change event is generated.

[0031] When the simulation progresses to the beginning boundary of the device unavailable time window, a device shutdown event is generated. When the simulation progresses to the end boundary of the unavailable time window, a device operation and shutdown state switching event is obtained.

[0032] When the semi-finished product enters the workstation node, the entry time is recorded. When the semi-finished product unit completes the current process and leaves the workstation node, the departure time is recorded. The difference between the departure time and the entry time is taken as the actual processing time of the semi-finished product at the current workstation node.

[0033] The events of queue quantity change, equipment operation and shutdown state switching, and actual processing time are timestamped according to the simulated time of the event to obtain a dynamic event set.

[0034] In a preferred embodiment, quantifying the impact of the insertion based on all dynamic events captured throughout the simulation period includes:

[0035] Extract a first subset of events semantically associated with the insertion task and a second subset of events semantically associated with each existing production task;

[0036] From the first event subset and the second event subset, the completion time of the last process corresponding to each task is extracted respectively. The original planned delivery time of the existing production task is compared with the corresponding completion time to generate the delivery deviation value of the inserted task.

[0037] The number of existing production tasks with delivery delays is counted, and the existing order delay ratio is generated based on the proportion of the number of tasks with delivery delays to the total number of existing production tasks.

[0038] The order delivery reliability index is obtained by comprehensively evaluating the existing order delay rate and the delivery deviation value of the inserted order.

[0039] In a preferred embodiment, quantifying the impact of the insertion based on all dynamic events captured throughout the simulation cycle further includes:

[0040] Throughout the entire simulation period, the length of the semi-finished product queue at each sampling time of each workstation node is statistically analyzed, and the degree of dispersion relative to their respective mean is used to obtain the queue fluctuation value of each workstation node.

[0041] The frequency of downtime events for each piece of equipment during the simulation cycle is statistically analyzed, and the correlation analysis between the frequency of downtime events for each piece of equipment and the queue fluctuation value of each workstation node is performed to obtain the production line stability disturbance index.

[0042] Obtain the planned effective working time of each worker within the simulation period. Extract the waiting time corresponding to the worker waiting task event caused by equipment downtime from the dynamic event set. Divide the waiting time by the planned effective working time to obtain the proportion of unplanned downtime for each worker.

[0043] By aggregating the percentage of unplanned downtime for all workers, the overall efficiency of the production line before and after the introduction of supplementary tasks is assessed, resulting in a resource efficiency loss index.

[0044] In a preferred embodiment, the step of reorganizing the process route based on the quantified evaluation report to obtain the process route after the order insertion occurs includes:

[0045] When the order delivery reliability index is lower than the preset delivery reliability threshold, or the resource efficiency loss index is higher than the preset efficiency loss tolerance threshold, a query request for alternative process routes is initiated to the process knowledge base.

[0046] Obtain alternative process combinations that match the process type identifier;

[0047] The alternative process combinations are mapped to the corresponding workstation nodes in the digital twin model, and the available equipment capable of performing the alternative processes and the available workers with corresponding skill proficiency tags are identified to obtain a set of candidate alternative workstation nodes.

[0048] From the set of candidate alternative workstation nodes, extract the current equipment reliability disturbance factor and the corresponding worker's human fatigue disturbance factor corresponding to each candidate alternative workstation node, and screen out the available workstation nodes that meet the processing requirements of the alternative process combination to obtain the executable alternative workstation nodes.

[0049] Using the executable alternative workstation nodes and their corresponding processing capacity parameters, the process flow of the order insertion task is reconstructed to generate a recombined process flow. The recombined process flow is then re-injected into the digital twin model, and simulation is performed to recalculate the order delivery reliability index and the resource efficiency loss index. Finally, the adjusted order insertion execution plan is output.

[0050] To address the above problems, the present invention also provides a garment production order insertion response system based on dynamic simulation, the system comprising:

[0051] The digital twin building block is used to build digital twin models of the production environment;

[0052] The order insertion disturbance and event capture module is used to initiate order insertion simulation on the digital twin model based on a preset disturbance factor, and capture every dynamic event caused by resource contention during the simulation.

[0053] The order insertion impact quantification module is used to quantify the impact of order insertion based on all dynamic events captured throughout the entire simulation cycle.

[0054] The process route reorganization module is used to reorganize the process route based on the quantified evaluation report to obtain the process route after the insertion of orders.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] 1. This invention constructs a high-fidelity digital twin model of the production environment by introducing equipment reliability disturbance factors and human fatigue disturbance factors. This model accurately maps real-time changes in work-in-process inventory, equipment operating status, and worker skill proficiency. Based on this, the simulation of order insertion disturbances can dynamically adjust the equipment availability window and correct the processing time per piece, thereby accurately reproducing the dynamic game between order insertion tasks and existing production tasks in the resource competition process. This dynamic simulation mechanism improves the predictive accuracy of scheduling simulations, enabling the complete capture of complex events such as changes in queue size and equipment downtime caused by order insertion, providing a comprehensive data foundation for subsequent impact assessments.

[0057] 2. Based on the dynamic event set captured throughout the entire simulation cycle, this invention achieves multi-dimensional quantification of the impact of order insertion. The generated order delivery reliability index integrates the delivery deviation of the insertion task with the delay ratio of existing orders, while the resource efficiency loss index is related to production line stability disturbances and the proportion of unplanned worker downtime. When the above indicators trigger the process route reorganization condition, the system can obtain alternative process combinations from the process knowledge base, and perform matching and screening based on the current equipment reliability and human fatigue status of the workstation nodes. The process flow is then reconstructed and re-injected into the digital twin model for closed-loop verification. This process improves the adaptability of process route reorganization to dynamic production states, and the output adjusted order insertion execution plan effectively reduces the resource efficiency loss introduced by order insertion while ensuring delivery reliability. Attached Figure Description

[0058] Figure 1 A flowchart illustrating a dynamic simulation-based garment production order insertion response method according to an embodiment of the present invention;

[0059] Figure 2 A functional block diagram of a garment production order insertion response system based on dynamic simulation provided in an embodiment of the present invention;

[0060] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0061] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0062] This application provides a method for responding to order insertions in garment production based on dynamic simulation. The execution entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cluster of cloud servers. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0063] Reference Figure 1 The diagram shown is a flowchart illustrating a garment production order insertion response method based on dynamic simulation, according to an embodiment of the present invention. In this embodiment, the garment production order insertion response method based on dynamic simulation includes:

[0064] Build a digital twin model of the production environment.

[0065] In this embodiment of the invention, the construction of the digital twin model of the production environment includes:

[0066] The system acquires information about the RFID tags attached to the work-in-process products to obtain the product inventory data. At the same time, it acquires the spindle load rate, operating speed, and temperature signals from the programmable logic controller of the equipment to obtain the equipment operating status parameters.

[0067] Retrieve historical operation records bound to the worker's login ID, perform statistical processing on the historical operation records, and generate skill proficiency tags for the worker's proficiency in specific work processes;

[0068] The inventory data of the products, the operating status parameters of the equipment, and the skill proficiency tags are mapped to the corresponding workstation nodes in the three-dimensional layout diagram of the production line to generate a digital twin model that uses node-connection relationships to represent the process flow logic and node attributes to represent real-time load and production capacity.

[0069] The disturbance factors include: equipment reliability disturbance factors and human fatigue disturbance factors, wherein the equipment reliability disturbance factors include:

[0070] Retrieve historical fault repair records corresponding to each device currently participating in the simulation, wherein the historical fault repair records include the timestamp of the fault occurrence and a description of the fault cause;

[0071] Based on the historical fault repair records, the frequency of faults occurring in each device within different load state ranges is statistically analyzed to generate a device load-fault correlation graph.

[0072] Based on the load state range of the spindle load rate, the corresponding equipment performance degradation trend is extracted from the equipment load-fault correlation graph to generate the equipment reliability disturbance factor.

[0073] The human-caused fatigue disturbance factors include:

[0074] Real-time retrieval of each worker's current shift's cumulative working hours;

[0075] Based on the skill proficiency tags, determine the standard operating rhythm benchmark for each worker;

[0076] When the cumulative working hours of the current shift exceed the preset fatigue accumulation threshold, the deviation of the worker's actual operating rhythm from the standard operating rhythm benchmark is analyzed to obtain the human fatigue disturbance factor.

[0077] The RFID tags attached to the surface of the workpiece store a unique identifier for the corresponding semi-finished product and information on its current process status. RFID readers deployed at the entrance of each workstation activate the tags by emitting electromagnetic waves of a specific frequency and read the stored identifier and process status information. After reading, the identifier and the current station location information are transmitted to the data processing terminal. The data processing terminal groups and counts the received identifiers according to process type, and calculates the number of products currently awaiting processing, being processed, and completed for each process, thus forming workpiece inventory data. This workpiece inventory data includes the real-time distribution of workpieces at each workstation node.

[0078] The equipment's programmable logic controller continuously collects real-time load rate of the spindle drive motor, spindle rotation speed, and temperature values ​​at key internal temperature measurement points. The controller packages the collected load rate, rotation speed, and temperature values ​​into equipment status data frames at fixed time intervals and sends these frames to the data processing terminal via the equipment network interface. The data processing terminal parses the received data frames, extracts three types of values—spindle load rate, operating speed, and temperature—and integrates these three types of values ​​into equipment operating status parameters that characterize the current operating status of the equipment.

[0079] The system retrieves historical operation records linked to the worker's login ID from the attendance and work assignment database. These records contain the start and end times of each specific process performed by the worker. The data processing terminal extracts the execution time of the same process by the same worker from the historical operation records, removes abnormal execution times caused by equipment failure or material waiting, and calculates the average of the remaining valid execution time. This average is used as the worker's skill proficiency tag for performing the process, which reflects the worker's level of proficiency in performing the specific process.

[0080] The equipment layout of the production line, the material flow direction between workstations, and the spatial relationship between workstations are drawn into a 3D layout diagram of the production line. In the 3D layout diagram of the production line, each piece of equipment and its associated worker operation position constitute a workstation node. The workstation nodes are connected in a directed manner according to the actual flow path of semi-finished products. The work-in-process quantity corresponding to each workstation node in the work-in-process inventory data is filled into the inventory attribute field of the corresponding workstation node. The spindle load rate, operating speed, and temperature signal of a certain piece of equipment in the equipment operation status parameters are filled into the equipment attribute field of the workstation node. The skill proficiency tag of the worker belonging to the workstation node is filled into the personnel attribute field of the workstation node. In this way, a digital twin model is generated, which represents the process flow logic with node-connection relationship and represents the real-time load and production capacity with node attributes.

[0081] Historical fault maintenance records corresponding to each piece of equipment currently participating in the simulation are retrieved from the equipment management archive. Each record in the historical fault maintenance records contains a timestamp of the fault occurrence and a description of the fault cause. After retrieval, all historical fault maintenance records for each piece of equipment are sorted according to the fault occurrence timestamp. The sorted fault records are then marked one by one on the equipment operation timeline with time as the horizontal axis. At the same time, the spindle load rate at different times on the equipment operation timeline is included in the statistical range. Based on the spindle load rate value, three load state intervals are divided: low load interval, medium load interval, and high load interval. Each fault record is assigned to the corresponding load state interval according to the spindle load rate at the time of its occurrence. The number of faults occurring in each load state interval is counted. The number of faults occurring in each load state interval is divided by the total running time of that interval to obtain the equipment fault occurrence frequency under each load state interval. The fault occurrence frequencies corresponding to the three load state intervals are compiled into an equipment load-fault correlation graph.

[0082] Extract the current spindle load rate value from the equipment operating status parameters, determine which specific interval (low load, medium load, or high load) the spindle load rate value falls into, and then find the corresponding fault occurrence frequency in the equipment load-fault correlation graph. Based on the frequency of the fault occurrence, determine whether the performance degradation trend of the equipment under the current load condition is upward or stable. Use this performance degradation trend as the equipment reliability disturbance factor, which characterizes the degree of tendency of the equipment to experience performance degradation or failure under the current load condition.

[0083] The system retrieves the cumulative working time of each worker in the current shift from the time they clock in to the current time from the attendance and work assignment database. Based on the type of work process recorded in the worker's skill proficiency tag, it searches the standard work cycle time corresponding to the work process in the work process standard work database as the standard work rhythm benchmark. The standard work rhythm benchmark specifies the standard time interval that the worker should take to complete a single work process operation under normal conditions.

[0084] The cumulative working hours of the current shift are compared with a pre-set fatigue accumulation threshold. The fatigue accumulation threshold is a continuous working time limit pre-defined according to the labor intensity level of the job. When the cumulative working hours exceed the fatigue accumulation threshold, the deviation detection of the worker's operation rhythm is triggered. At this time, the timing device configured at the workstation continuously records the actual time taken by the worker to complete each semi-finished product processing in subsequent operations. The actual time is compared with the standard operation rhythm benchmark one by one, and the deviation direction and deviation magnitude of the actual time relative to the standard operation rhythm benchmark are calculated. The deviation direction and deviation magnitude constitute the human fatigue disturbance factor, which reflects the degree of decline in the worker's operating efficiency due to fatigue accumulation.

[0085] The beneficial effects are as follows: By reading the identification code and process status information from RFID tags and grouping and counting by process type, work-in-process inventory data containing the real-time distribution of work-in-process at each workstation node is directly generated, ensuring that the acquisition of work-in-process inventory data corresponds synchronously with the actual situation. Spindle load rate, operating speed, and temperature signals are continuously collected from the equipment's programmable logic controller and integrated into equipment operating status parameters, so that the description of equipment operating status is composed of real-time collected multi-dimensional values ​​rather than static setpoints. Historical operation records are retrieved from the attendance and dispatch database, and after removing abnormal durations, the average effective duration is calculated to generate skill proficiency tags reflecting the worker's proficiency in specific process operations, allowing for the quantitative differentiation of differences in worker operational abilities. Work-in-process inventory data, equipment operating status parameters, and skill proficiency tags are respectively filled into the inventory attribute field, equipment attribute field, and personnel attribute field of the corresponding workstation node in the production line's 3D layout diagram, forming a digital twin model where node-connection relationships represent process flow logic and node attributes represent real-time load and production capacity, enabling a complete mapping of the production line status in virtual space. Historical fault repair records are retrieved, and the frequency of faults in each of the low-load, medium-load, and high-load intervals is statistically analyzed based on the spindle load rate. This data is compiled into an equipment load-fault correlation graph, clearly showing the equipment's fault tendencies under different load conditions. Based on the current spindle load rate, corresponding frequencies are extracted from the equipment load-fault correlation graph, and performance degradation trends are determined to generate an equipment reliability disturbance factor. This allows the degree of equipment performance degradation to be dynamically determined based on real-time load conditions. By comparing the cumulative working hours of the current shift with a fatigue accumulation threshold and continuously recording the actual operation time after exceeding the threshold, the direction and magnitude of the deviation of the actual time relative to the standard operating rhythm benchmark are calculated, generating a human fatigue disturbance factor. This allows for real-time measurement of the degree of operational efficiency decline caused by worker fatigue accumulation, rather than relying on fixed experience values.

[0086] Based on a preset disturbance factor, a single-entry simulation is initiated on the digital twin model, and every dynamic event caused by resource contention during the simulation is captured.

[0087] In this embodiment of the invention, initiating a single-order insertion simulation on the digital twin model based on a preset perturbation factor includes:

[0088] The process of inserting a task is broken down into semi-finished products to be processed, and the semi-finished products to be processed are sequentially placed into the corresponding workstation nodes in the digital twin model according to the process flow logic.

[0089] When the semi-finished products to be processed arrive at each workstation node, the available time window of the equipment in the simulation cycle is dynamically adjusted according to the equipment performance degradation trend characterized by the equipment reliability disturbance factor, so as to obtain the equipment availability status after the available time window is reduced.

[0090] When the semi-finished products to be processed are allocated to each worker, the standard processing time per piece corresponding to the skill proficiency label is dynamically adjusted according to the probability of operation efficiency decay represented by the human fatigue disturbance factor, so as to obtain the fatigue-corrected processing time per piece.

[0091] Based on the available status of the equipment and the single-piece processing time after fatigue correction, the semi-finished products of the inserted task and the semi-finished products of the existing production task are driven to simulate resource contention at each workstation node.

[0092] When multiple semi-finished products compete for the same equipment or the same worker, they are sorted according to the priority labels of the tasks to obtain the simulated scheduling results.

[0093] During the simulated process, the changes in the number of semi-finished products in front of each workstation node, the actual operation and shutdown status switching events of the equipment, and the actual processing time of each semi-finished product unit are monitored and recorded in real time to obtain dynamic events.

[0094] During the simulated process, the changes in the number of semi-finished products queuing at each workstation node, the actual operation and shutdown status switching events of the equipment, and the actual processing time of each semi-finished product unit are monitored and recorded in real time to obtain dynamic events, including:

[0095] According to the rhythm of the analog clock, the length of the queue of semi-finished products to be processed in the input buffer is periodically collected. When the difference between the queue lengths of two adjacent sampling times exceeds the preset queue fluctuation sensitivity threshold, a queue quantity change event is generated.

[0096] When the simulation progresses to the beginning boundary of the device unavailable time window, a device shutdown event is generated. When the simulation progresses to the end boundary of the unavailable time window, a device operation and shutdown state switching event is obtained.

[0097] When the semi-finished product enters the workstation node, the entry time is recorded. When the semi-finished product unit completes the current process and leaves the workstation node, the departure time is recorded. The difference between the departure time and the entry time is taken as the actual processing time of the semi-finished product at the current workstation node.

[0098] The events of queue quantity change, equipment operation and shutdown state switching, and actual processing time are timestamped according to the simulated time of the event to obtain a dynamic event set.

[0099] After receiving the order insertion task, all the processes included in the order insertion task are decomposed into independent semi-finished product units one by one according to the preset process sequence. Each semi-finished product unit corresponds to a process to be processed. The process flow logic represented by the connection sequence of each workstation node is read from the digital twin model. According to the process flow logic, the order of workstation nodes that each semi-finished product needs to go through is determined. The decomposed semi-finished products are then placed one by one into the input buffer of the first workstation node in the digital twin model.

[0100] When a semi-finished product of an insert task arrives at a certain workstation node, the equipment reliability disturbance factor corresponding to that equipment is retrieved from the equipment attribute field of that workstation node. This equipment reliability disturbance factor records whether the performance degradation trend of the equipment under the current load state is upward or stable. When the performance degradation trend is upward, the expected failure time corresponding to this trend is extracted from the equipment load-failure correlation graph. The time period before the expected failure time is determined as the equipment availability state after the reduced available time window. When the performance degradation trend is stable, all time periods of the equipment within the simulation cycle are taken as the equipment availability state after the reduced available time window. The equipment availability state is clearly defined by the start and end times of the available time window, which specifies the time range during which the equipment can undertake processing tasks within the simulation cycle.

[0101] When a semi-finished product of an interim task is assigned to a worker at a specific workstation, the worker's human fatigue disturbance factor is retrieved from the worker's attribute field at that workstation. This factor records the direction and magnitude of the worker's actual operation time relative to the standard operating rhythm baseline. The probability of operational efficiency decay is directly represented by this human fatigue disturbance factor. Specifically, when the deviation is positive, the probability of operational efficiency decay is the ratio corresponding to the deviation magnitude. This ratio indicates the degree to which the worker's actual operation time exceeds the standard operating rhythm baseline. In this case, the standard processing time per piece recorded in the skill proficiency tag is multiplied by this ratio and increased. The increased value is used as the fatigue-corrected processing time per piece. When the deviation is negative, the probability of operational efficiency decay is zero, indicating that the worker's actual operation time has not exceeded the standard operating rhythm baseline, and operational efficiency has not decayed. In this case, the standard processing time per piece is reduced proportionally to the deviation magnitude. The reduced value is used as the fatigue-corrected processing time per piece.

[0102] The start and end times of the available time window determined by the equipment's availability status, along with the fatigue-corrected single-piece processing time, are loaded into the simulation process. This drives the semi-finished products of the inserted task and the semi-finished products of the existing production task to compete for equipment processing time and worker operation time at each workstation node. When each semi-finished product enters a workstation node, it must check whether the current time is within the available time window of the target equipment and whether the target worker is in an idle state. When both conditions are met, the semi-finished product occupies the equipment and the worker and is processed according to the fatigue-corrected single-piece processing time. If either condition is not met, the semi-finished product queues in the input buffer of the workstation node until both conditions are met.

[0103] When multiple semi-finished products are simultaneously present in the input buffer of the same workstation node and both the equipment and the worker become available, the task priority tag of the task to which each semi-finished product belongs is retrieved. The task priority tag is a priority level identifier that is pre-set when the task is issued. All semi-finished products in the input buffer are sorted from high to low according to the task priority tag. The semi-finished product with the highest priority tag level gets priority to obtain the right to occupy the equipment and worker and enter the processing state. The remaining semi-finished products continue to be queued in the input buffer. The processing order of the semi-finished products after sorting is the simulation scheduling result.

[0104] The analog clock advances in equal steps, collecting the quantity of semi-finished products to be processed in the input buffer of each workstation at each clock tick, and recording the queue length collected at the current tick. The queue fluctuation sensitivity threshold is a pre-determined threshold for judging queue length changes based on the standard operating cycle time of the process processed at that workstation and the average flow time of a single semi-finished product through that workstation under normal conditions. This threshold defines the boundary between natural queue length fluctuations caused by normal processing rhythms and abnormal queue length fluctuations caused by order insertion disturbances. The queue length collected at the current tick is compared with the queue length collected at the previous tick. When the absolute value of the difference exceeds the preset queue fluctuation sensitivity threshold, it is determined that a queue quantity change event has occurred at that workstation. The queue quantity change event records the analog clock tick point to which it occurred and the specific amount of change in queue length.

[0105] When the analog clock advances to the beginning of a certain available time window within the equipment's available state after the equipment's available time window has been reduced, the equipment begins to enter a period where it can undertake processing tasks. When the analog clock continues to advance to the beginning boundary of a certain unavailable time window, a equipment shutdown event is generated. The equipment shutdown event records the simulated time when the shutdown occurred. When the analog clock advances to the end boundary of the unavailable time window, a equipment running and shutdown state switching event is generated. This switching event records the simulated time when the equipment resumes running from the shutdown state.

[0106] Record the moment when each semi-finished product enters the workstation node and begins processing as the entry moment. Record the moment when the analog clock advances to the moment when the semi-finished product completes the current process processing according to the fatigue-corrected single-piece processing time and is ready to leave the workstation node as the exit moment. Subtract the entry moment from the exit moment time value to obtain the actual processing time of the semi-finished product at the current workstation node.

[0107] The simulated clock tick points recorded in the queue number change event, the simulated time recorded in the equipment operation and shutdown state switching event, and the start and end simulated times corresponding to the actual processing time of each semi-finished product unit are arranged in the chronological order of the simulated time. Events occurring at the same simulated time are merged into the same record, and events at different simulated times are arranged in the order of time progression, forming a dynamic event set that is timestamped according to the simulated time of the event occurrence.

[0108] The beneficial effect is that by decomposing each process of the order insertion task into independent semi-finished product units and sequentially deploying them according to the process flow logic represented by the workstation node connection sequence in the digital twin model, the deployment path of the order insertion task perfectly matches the actual flow direction of the production line. When the semi-finished product of the order insertion task arrives at the workstation node, the equipment availability status after the reduction of the available time window is determined based on whether the performance degradation trend recorded by the equipment reliability disturbance factor is upward or stable. This makes the definition of the equipment availability period directly related to its failure tendency under load. When the semi-finished product of the order insertion task is assigned to the worker, the standard processing time per piece in the skill proficiency label is adjusted upward or downward according to the offset direction and offset magnitude recorded by the human fatigue disturbance factor to obtain the fatigue-corrected processing time per piece, so that the processing time benchmark is adjusted synchronously with the worker's fatigue level. The start and end times of the available time window of the equipment availability status and the fatigue-corrected processing time per piece jointly drive the semi-finished products of the order insertion task and the existing production task semi-finished products to compete for resources at each workstation node, incorporating both equipment availability and worker operating efficiency into the competition condition judgment. When multiple semi-finished products compete for the same resource simultaneously, task priority tags are used to sort them from high to low to form a simulated scheduling result, making the urgency of tasks directly determine the resource allocation order. By simulating clock increments and comparing the queue length difference between adjacent beat points to see if it exceeds the queue fluctuation sensitivity threshold, queue quantity change events are generated, providing clear triggering conditions for capturing queue changes. When the simulation progresses to the start and end boundaries of the unavailable time window, equipment shutdown events and equipment operation / shutdown state switching events are generated respectively, providing precise time markers for equipment state transitions. By recording the times when semi-finished products enter and leave workstation nodes and taking the difference as the actual processing time, queue quantity change events, equipment operation / shutdown state switching events, and actual processing time are timestamped according to the simulated time to form a dynamic event set, integrating all events within the simulation cycle under a unified time base.

[0109] The impact of order insertion is quantified based on all dynamic events captured throughout the simulation period.

[0110] In this embodiment of the invention, quantifying the impact of order insertion based on all dynamic events captured throughout the entire simulation period includes:

[0111] Extract a first subset of events semantically associated with the insertion task and a second subset of events semantically associated with each existing production task;

[0112] From the first event subset and the second event subset, the completion time of the last process corresponding to each task is extracted respectively. The original planned delivery time of the existing production task is compared with the corresponding completion time to generate the delivery deviation value of the inserted task.

[0113] The number of existing production tasks with delivery delays is counted, and the existing order delay ratio is generated based on the proportion of the number of tasks with delivery delays to the total number of existing production tasks.

[0114] The order delivery reliability index is obtained by comprehensively evaluating the existing order delay rate and the delivery deviation value of the inserted order.

[0115] The method of quantifying the impact of order insertion based on all dynamic events captured throughout the entire simulation period also includes:

[0116] Throughout the entire simulation period, the length of the semi-finished product queue at each sampling time of each workstation node is statistically analyzed, and the degree of dispersion relative to their respective mean is used to obtain the queue fluctuation value of each workstation node.

[0117] The frequency of downtime events for each piece of equipment during the simulation cycle is statistically analyzed, and the correlation analysis between the frequency of downtime events for each piece of equipment and the queue fluctuation value of each workstation node is performed to obtain the production line stability disturbance index.

[0118] Obtain the planned effective working time of each worker within the simulation period. Extract the waiting time corresponding to the worker waiting task event caused by equipment downtime from the dynamic event set. Divide the waiting time by the planned effective working time to obtain the proportion of unplanned downtime for each worker.

[0119] By aggregating the percentage of unplanned downtime for all workers, the overall efficiency of the production line before and after the introduction of supplementary tasks is assessed, resulting in a resource efficiency loss index.

[0120] The dynamic event set is used to filter out all event records directly related to the order insertion task. The filtering method is to check whether the semi-finished product identification code associated with each event record belongs to the task number range assigned when the order insertion task was issued. All event records belonging to the order insertion task number range are extracted as the first event subset. At the same time, the remaining semi-finished product identification codes in the dynamic event set are classified according to the task numbers of the existing production tasks. The event records belonging to the number range of each existing production task are extracted and merged as the second event subset.

[0121] In the first event subset, find all the processing completion events of the semi-finished product units included in the insert task at each workstation node. From these completion events, determine the completion event corresponding to the last process according to the process flow logic. Extract the departure time recorded in the completion event as the completion time of the last process of the insert task. In the second event subset, find the completion events of the last process of all semi-finished product units included in each existing production task in the same way. Extract the completion time of the last process of each existing production task. Compare the original planned delivery time of each existing production task with the completion time of the last process corresponding to the existing production task item by item. If the completion time is later than the original planned delivery time, it is counted as a delivery delay of the existing production task. Record the difference between the completion time and the original planned delivery time as the delivery deviation of the existing production task. Compare the completion time of the last process of the insert task with the original planned delivery time of the insert task, and use the difference between the two as the delivery deviation value of the insert task.

[0122] The number of existing production tasks with delivery delays in the second event subset is counted one by one. The counting method is to check whether the completion time of each existing production task is later than the original planned delivery time and add up the count of the tasks that are judged to be delayed to obtain the number of tasks with delivery delays. The total number of existing production tasks included in the entire production schedule is retrieved, and the number of tasks with delivery delays is divided by the total number of existing production tasks to obtain the existing order delay ratio.

[0123] The existing order delay rate and the delivery deviation value of the inserted order are jointly included in the comprehensive evaluation of order delivery reliability. The evaluation method is to map the existing order delay rate to a preset delay rate judgment range and the delivery deviation value of the inserted order to a preset deviation value judgment range. The two mapping results correspond to two position points on the delivery reliability judgment scale. The reliability levels corresponding to these two position points are combined and judged to obtain the order delivery reliability index.

[0124] Throughout the simulation cycle, the queue length of the semi-finished products to be processed recorded at each sampling time for each workstation node is backtracked. The queue length values ​​of a certain workstation node at all sampling times are summed and divided by the total number of sampling times for that workstation node to obtain the average queue length of that workstation node. The deviation between the queue length value of that workstation node at each sampling time and the average queue length is calculated one by one. The deviation values ​​at each sampling time are summed and divided by the total number of sampling times to obtain the queue fluctuation value of that workstation node.

[0125] The number of equipment downtime events recorded for each device in the dynamic event set is counted. The number of equipment downtime events recorded for the same device within the simulation cycle is taken as the downtime event frequency of that device. The downtime event frequency of each device is mapped to the queue fluctuation value of the workstation node where that device is located. The correspondence between devices with high downtime event frequency and workstation nodes with high queue fluctuation values, as well as the correspondence between devices with low downtime event frequency and workstation nodes with low queue fluctuation values, are summarized as a whole. The summary result is used as the production line stability disturbance index.

[0126] Retrieve the shift schedules of each worker within the simulated period from the attendance and dispatch database. Extract the total duration of production operations that the worker should perform from the shift schedules as the planned effective working time. Extract the event records of the worker being in a waiting task state during the period when the equipment downtime event occurred from the dynamic event set. Add up the duration between the start and end times of the waiting task event records to obtain the waiting time corresponding to the worker's waiting task event caused by the equipment downtime. Divide the waiting time by the planned effective working time to obtain the percentage of the worker's unplanned downtime.

[0127] The unplanned downtime percentages of all workers are summarized by adding up the percentages of each worker's unplanned downtime. The total downtime percentage is then divided by the total number of workers to obtain the overall production line efficiency performance value after the introduction of the order insertion task. The baseline downtime percentage of the production line under the same simulation duration without the introduction of the order insertion task is retrieved. The difference between the overall production line efficiency performance value after the introduction of the order insertion task and the baseline downtime percentage is calculated, and the difference result is used as the resource efficiency loss index.

[0128] The beneficial effect is that by checking whether the semi-finished product identification code associated with each event record belongs to the task number range assigned when the order insertion task was issued, the first event subset is extracted. The remaining events are then categorized according to their respective task numbers in the existing production tasks and merged into the second event subset, making the event data on which the order insertion impact analysis is based clearly correspond to the affiliation of each task. From the first and second event subsets, the completion event of the last process is determined according to the process flow logic, and the departure time is extracted as the completion time of the last process of each task. The original planned delivery time of the existing production tasks is compared with the corresponding completion time to obtain the delivery delay judgment and delivery deviation amount. At the same time, the delivery deviation value of the order insertion task is obtained, so that the determination of the delivery deviation is directly based on the difference between the simulated completion time and the planned time. The number of tasks with delivery delays is counted by checking whether the completion time of each existing production task is later than the original planned delivery time and accumulating the number of delayed tasks. Then, the number is divided by the total number of existing production tasks to obtain the existing order delay ratio, so that the delay ratio accurately reflects the proportion of orders affected by the order insertion. By mapping existing order delay rates to a preset delay rate evaluation range and mapping the delivery deviation value of inserted orders to a preset deviation value evaluation range, the order delivery reliability index is obtained through merging and judging. This allows the impact of inserted orders on order delivery to be measured by both the delay range and the deviation magnitude. By backtracking the length of the waiting semi-finished product queue at all sampling times of each workstation node and calculating the deviation from the mean for each, the average value is taken to obtain the queue fluctuation value, thus quantifying the queue dispersion at each workstation node. The frequency of each equipment downtime event is correlated with the queue fluctuation value of its respective workstation node to obtain the production line stability disturbance index, allowing the correlation between equipment downtime and queue fluctuation to directly reflect the degree to which production line stability is affected. After extracting the planned effective working time from the scheduling plan, the waiting time caused by equipment downtime is extracted from the dynamic event set and divided to obtain the proportion of unplanned downtime, directly linking worker efficiency loss to the cause of equipment downtime. The overall production line efficiency index after the introduction of the order insertion task is obtained by summing up the proportion of unplanned downtime of all workers and dividing it by the total number of workers. The difference between this value and the baseline value of downtime proportion before the introduction of the order insertion task is calculated to obtain the resource efficiency loss index, so that the resource efficiency loss is presented in the form of the difference in efficiency before and after the order insertion task.

[0129] Based on the quantified evaluation report, the process route is reorganized to obtain the process route after the insertion of orders.

[0130] In this embodiment of the invention, the step of reorganizing the process route based on the quantified evaluation report to obtain the process route after the order insertion occurs includes:

[0131] When the order delivery reliability index is lower than the preset delivery reliability threshold, or the resource efficiency loss index is higher than the preset efficiency loss tolerance threshold, a query request for alternative process routes is initiated to the process knowledge base.

[0132] Obtain alternative process combinations that match the process type identifier;

[0133] The alternative process combinations are mapped to the corresponding workstation nodes in the digital twin model, and the available equipment capable of performing the alternative processes and the available workers with corresponding skill proficiency tags are identified to obtain a set of candidate alternative workstation nodes.

[0134] From the set of candidate alternative workstation nodes, extract the current equipment reliability disturbance factor and the corresponding worker's human fatigue disturbance factor corresponding to each candidate alternative workstation node, and screen out the available workstation nodes that meet the processing requirements of the alternative process combination to obtain the executable alternative workstation nodes.

[0135] Using the executable alternative workstation nodes and their corresponding processing capacity parameters, the process flow of the order insertion task is reconstructed to generate a recombined process flow. The recombined process flow is then re-injected into the digital twin model, and simulation is performed to recalculate the order delivery reliability index and the resource efficiency loss index. Finally, the adjusted order insertion execution plan is output.

[0136] The order delivery reliability index recorded in the quantified evaluation report is compared with a preset delivery reliability threshold. This threshold is a fixed numerical limit set in advance based on the minimum acceptable standard of the combined delivery indicators of the inserted order and existing orders as specified in the company's scheduling strategy. Below this threshold, order delivery reliability has fallen below the tolerance limit. Simultaneously, the resource efficiency loss index is compared with a preset efficiency loss tolerance threshold. This threshold is an upper limit of acceptable efficiency loss determined based on the historical average efficiency fluctuation range of the production line. This upper limit defines the dividing point where production line efficiency loss caused by inserted orders moves from the tolerable range to the intolerable range. When the order delivery reliability index is lower than the delivery reliability threshold, the impact of the current inserted order execution plan on order delivery is determined to exceed the acceptable range. When the resource efficiency loss index is higher than the efficiency loss tolerance threshold, the production line efficiency loss caused by the current inserted order execution plan is determined to exceed the acceptable range. If either condition is met, a process route reorganization process is triggered, initiating a query request for alternative process routes to the process knowledge base.

[0137] The process knowledge base stores the identifier of each process type and its corresponding multiple alternative process combinations. Each alternative process combination consists of a group of processes that can achieve the same processing purpose but use different processing paths. After receiving an alternative process route query request, the process type identifier of each process of the current insertion task is extracted from the query request. The process type identifiers are entered into the process knowledge base one by one for matching and retrieval. The alternative process combinations that completely correspond to the process type identifiers are extracted from the process knowledge base.

[0138] The node attributes of each workstation are read from the digital twin model. The node attributes include the equipment type and the range of processes that the workstation is configured with, as well as the operable process type corresponding to the skill proficiency tag of the worker bound to the workstation. Each alternative process in the alternative process combination is compared with the range of processes that each workstation can process and the operable process type. When the equipment type configured for a workstation can perform the alternative process and the process type recorded in the skill proficiency tag of the worker bound to the workstation is consistent with the alternative process, the workstation is marked as a candidate alternative workstation. After all comparisons are completed, all marked workstations are summarized to form a set of candidate alternative workstations.

[0139] The system retrieves the current equipment reliability disturbance factor stored in the equipment attribute field of each candidate alternative workstation from the candidate alternative workstation set. Simultaneously, it retrieves the human fatigue disturbance factor of the corresponding worker stored in the personnel attribute field of the candidate alternative workstation. For each candidate alternative workstation, a processing feasibility check is performed. The check involves comparing the equipment performance degradation trend recorded in the equipment reliability disturbance factor with the processing accuracy requirements of the alternative process combination for that process, and comparing the offset direction and offset magnitude recorded in the human fatigue disturbance factor with the operation cycle time requirements of the alternative process combination for that process. If the equipment performance degradation trend is stable and the offset magnitude of the human fatigue disturbance factor is within the allowable cycle time tolerance range of the alternative process combination, then the candidate alternative workstation passes the check and is determined as an executable alternative workstation.

[0140] Retrieve the processing capacity parameters recorded in the node attributes of the executable alternative workstation nodes in the digital twin model. The processing capacity parameters include the standard processing time per piece of equipment at the workstation node and the process operation speed level recorded in the skill proficiency tag of the worker at the workstation node. Establish a correspondence between each process in the alternative process combination and the executable alternative workstation nodes one by one according to the process sequence. Assign each process to an executable alternative workstation node and associate it with the processing capacity parameters of that workstation node. Connect the executable alternative workstation nodes in sequence according to the order of the processes to form a complete process chain. This process chain is the reconstructed and reorganized process flow.

[0141] The correspondence between each process and its corresponding executable alternative workstation node in the reorganized process flow is rewritten into the digital twin model, replacing the original process flow path of the insertion task. After injecting the reorganized process flow into the digital twin model, a new simulation is initiated. During the simulation, semi-finished products are delivered according to the workstation node sequence specified in the reorganized process flow, and the equipment reliability disturbance factor and human fatigue disturbance factor of the corresponding workstation node are applied to drive the resource contention process. After the simulation is completed, the first and second event subsets are re-extracted from the dynamic event set, and the order delivery reliability index is recalculated. At the same time, the production line stability disturbance index and the proportion of unplanned downtime of each worker are recalculated, and the resource efficiency loss index is recalculated. The recalculated order delivery reliability index and resource efficiency loss index are compared again with the delivery reliability threshold and efficiency loss tolerance threshold. After the comparison is passed, the current reorganized process flow, the corresponding insertion task delivery order, workstation node allocation scheme, and processing time arrangement are packaged and output as the adjusted insertion execution scheme.

[0142] The beneficial effect is that by comparing the order delivery reliability index with the delivery reliability threshold and the resource efficiency loss index with the efficiency loss tolerance threshold, a query request for alternative process routes is initiated to the process knowledge base when either condition is triggered. This allows the initiation of process route reorganization to be jointly controlled by the dual indicators of delivery reliability and efficiency loss. Alternative process combinations are retrieved from the process knowledge base by matching and retrieving them one by one according to the process type identifier, ensuring that alternative process solutions directly originate from the preset process type correspondence. Alternative process combinations are mapped to workstation nodes in the digital twin model. Candidate alternative workstation nodes are marked and aggregated into a candidate alternative workstation node set by comparing the range of process processes that the equipment can process with the operable process types corresponding to the worker's skill proficiency tags. This ensures that the initial screening of alternative workstations is entirely based on the matching of equipment and worker capabilities. From the candidate alternative workstation node set, the current equipment reliability disturbance factor and human fatigue disturbance factor are retrieved for processing feasibility verification. A stable trend in equipment performance degradation and a human fatigue deviation within the cycle time tolerance are used as the passing conditions to determine executable alternative workstation nodes, ensuring that the final selection of alternative workstations simultaneously satisfies the dual constraints of equipment reliability and personnel status. The processing capacity parameters of executable alternative workstation nodes are retrieved, and each process in the alternative process combination is sequentially linked with the workstation nodes to form a reconstructed process flow. This reconstructed process chain directly relates to the standard working hours of the equipment at each workstation node and the worker's operating speed level. The reconstructed process flow is then reinjected into the digital twin model to initiate simulation and recalculate the order delivery reliability index and resource efficiency loss index. After successful comparison, an adjusted order insertion execution plan is output, including the process flow, delivery order, workstation allocation scheme, and processing time arrangement. This ensures that the final order insertion plan undergoes closed-loop verification and fully corresponds to the reconstructed process flow.

[0143] like Figure 2 The diagram shown is a functional block diagram of a garment production order insertion response system based on dynamic simulation provided in an embodiment of the present invention.

[0144] The garment production order insertion response system 100 based on dynamic simulation described in this invention can be installed in an electronic device. Depending on the functions implemented, the garment production order insertion response system 100 based on dynamic simulation may include a digital twin construction module 101, an order insertion disturbance and event capture module 102, an order insertion impact quantification module 103, and a process route reorganization module 104. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and is stored in the memory of the electronic device.

[0145] In this embodiment, the functions of each module / unit are as follows:

[0146] The digital twin construction module 101 is used to construct a digital twin model of the production environment;

[0147] The order insertion disturbance and event capture module 102 is used to initiate order insertion simulation on the digital twin model based on a preset disturbance factor, and capture each dynamic event caused by resource contention during the simulation.

[0148] The order insertion impact quantification module 103 is used to quantify the impact of order insertion based on all dynamic events captured throughout the entire simulation cycle.

[0149] The process route reorganization module 104 is used to reorganize the process route according to the quantified evaluation report to obtain the process route after the insertion of orders.

[0150] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0151] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0152] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0153] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0154] This application embodiment can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0155] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for responding to order insertions in garment production based on dynamic simulation, characterized in that, The method includes: Build a digital twin model of the production environment; Based on a preset disturbance factor, an insertion simulation is initiated on the digital twin model, and every dynamic event caused by resource contention during the simulation is captured. The impact of order insertion is quantified based on all dynamic events captured throughout the entire simulation period. Based on the quantified evaluation report, the process route is reorganized to obtain the process route after the insertion of orders.

2. The garment production order insertion response method based on dynamic simulation as described in claim 1, characterized in that, The digital twin model for constructing the production environment includes: The system acquires information about the RFID tags attached to the work-in-process products to obtain the product inventory data. At the same time, it acquires the spindle load rate, operating speed, and temperature signals from the programmable logic controller of the equipment to obtain the equipment operating status parameters. Retrieve historical operation records bound to the worker's login ID, perform statistical processing on the historical operation records, and generate skill proficiency tags for the worker's proficiency in specific work processes; The inventory data of the products, the operating status parameters of the equipment, and the skill proficiency tags are mapped to the corresponding workstation nodes in the three-dimensional layout diagram of the production line to generate a digital twin model that uses node-connection relationships to represent the process flow logic and node attributes to represent real-time load and production capacity.

3. The garment production order insertion response method based on dynamic simulation as described in claim 2, characterized in that, The disturbance factors include: equipment reliability disturbance factors and human fatigue disturbance factors, wherein the equipment reliability disturbance factors include: Retrieve historical fault repair records corresponding to each device currently participating in the simulation, wherein the historical fault repair records include the timestamp of the fault occurrence and a description of the fault cause; Based on the historical fault repair records, the frequency of faults occurring in each device within different load state ranges is statistically analyzed to generate a device load-fault correlation graph. Based on the load state range of the spindle load rate, the corresponding equipment performance degradation trend is extracted from the equipment load-fault correlation graph to generate the equipment reliability disturbance factor.

4. The garment production order insertion response method based on dynamic simulation as described in claim 3, characterized in that, The human-caused fatigue disturbance factors include: Real-time retrieval of each worker's current shift's cumulative working hours; Based on the skill proficiency tags, determine the standard operating rhythm benchmark for each worker; When the cumulative working hours of the current shift exceed the preset fatigue accumulation threshold, the deviation of the worker's actual operating rhythm from the standard operating rhythm benchmark is analyzed to obtain the human fatigue disturbance factor.

5. The garment production order insertion response method based on dynamic simulation as described in claim 4, characterized in that, The step of initiating a single-order insertion simulation on the digital twin model based on a preset perturbation factor includes: The process of inserting a task is broken down into semi-finished products to be processed, and the semi-finished products to be processed are sequentially placed into the corresponding workstation nodes in the digital twin model according to the process flow logic. When the semi-finished products to be processed arrive at each workstation node, the available time window of the equipment in the simulation cycle is dynamically adjusted according to the equipment performance degradation trend characterized by the equipment reliability disturbance factor, so as to obtain the equipment availability status after the available time window is reduced. When the semi-finished products to be processed are allocated to each worker, the standard processing time per piece corresponding to the skill proficiency label is dynamically adjusted according to the probability of operation efficiency decay represented by the human fatigue disturbance factor, so as to obtain the fatigue-corrected processing time per piece. Based on the available status of the equipment and the single-piece processing time after fatigue correction, the semi-finished products of the inserted task and the semi-finished products of the existing production task are driven to simulate resource contention at each workstation node. When multiple semi-finished products compete for the same equipment or the same worker, they are sorted according to the priority labels of the tasks to obtain the simulated scheduling results. During the simulated process, the changes in the number of semi-finished products in front of each workstation node, the actual operation and shutdown status switching events of the equipment, and the actual processing time of each semi-finished product unit are monitored and recorded in real time to obtain dynamic events.

6. The garment production order insertion response method based on dynamic simulation as described in claim 5, characterized in that, During the simulated process, the changes in the number of semi-finished products queuing at each workstation node, the actual operation and shutdown status switching events of the equipment, and the actual processing time of each semi-finished product unit are monitored and recorded in real time to obtain dynamic events, including: According to the rhythm of the analog clock, the length of the queue of semi-finished products to be processed in the input buffer is periodically collected. When the difference between the queue lengths of two adjacent sampling times exceeds the preset queue fluctuation sensitivity threshold, a queue quantity change event is generated. When the simulation progresses to the beginning boundary of the device unavailable time window, a device shutdown event is generated. When the simulation progresses to the end boundary of the unavailable time window, a device operation and shutdown state switching event is obtained. When the semi-finished product enters the workstation node, the entry time is recorded. When the semi-finished product unit completes the current process and leaves the workstation node, the departure time is recorded. The difference between the departure time and the entry time is taken as the actual processing time of the semi-finished product at the current workstation node. The events of queue number change, equipment operation and shutdown state switching, and actual processing time are timestamped according to the simulated time of the event to obtain a dynamic event set.

7. The garment production order insertion response method based on dynamic simulation as described in claim 6, characterized in that, The impact of order insertion is quantified based on all dynamic events captured throughout the entire simulation period, including: Extract a first subset of events semantically associated with the insertion task and a second subset of events semantically associated with each existing production task; From the first event subset and the second event subset, the completion time of the last process corresponding to each task is extracted respectively. The original planned delivery time of the existing production task is compared with the corresponding completion time to generate the delivery deviation value of the inserted task. The number of existing production tasks with delivery delays is counted, and the existing order delay ratio is generated based on the proportion of the number of tasks with delivery delays to the total number of existing production tasks. The order delivery reliability index is obtained by comprehensively evaluating the existing order delay rate and the delivery deviation value of the inserted order.

8. The garment production order insertion response method based on dynamic simulation as described in claim 7, characterized in that, The method of quantifying the impact of order insertion based on all dynamic events captured throughout the entire simulation period also includes: Throughout the entire simulation period, the length of the semi-finished product queue at each sampling time of each workstation node is statistically analyzed, and the degree of dispersion relative to their respective mean is used to obtain the queue fluctuation value of each workstation node. The frequency of downtime events for each piece of equipment during the simulation cycle is statistically analyzed, and the correlation analysis between the frequency of downtime events for each piece of equipment and the queue fluctuation value of each workstation node is performed to obtain the production line stability disturbance index. Obtain the planned effective working time of each worker within the simulation period. Extract the waiting time corresponding to the worker waiting task event caused by equipment downtime from the dynamic event set. Divide the waiting time by the planned effective working time to obtain the proportion of unplanned downtime for each worker. By aggregating the percentage of unplanned downtime for all workers, the overall efficiency of the production line before and after the introduction of supplementary tasks is assessed, resulting in a resource efficiency loss index.

9. The garment production order insertion response method based on dynamic simulation as described in claim 8, characterized in that, The process route is reorganized based on the quantified evaluation report to obtain the process route after the order insertion, including: When the order delivery reliability index is lower than the preset delivery reliability threshold, or the resource efficiency loss index is higher than the preset efficiency loss tolerance threshold, a query request for alternative process routes is initiated to the process knowledge base. Obtain alternative process combinations that match the process type identifier; The alternative process combinations are mapped to the corresponding workstation nodes in the digital twin model, and the available equipment capable of performing the alternative processes and the available workers with corresponding skill proficiency tags are identified to obtain a set of candidate alternative workstation nodes. From the set of candidate alternative workstation nodes, extract the current equipment reliability disturbance factor and the corresponding worker's human fatigue disturbance factor corresponding to each candidate alternative workstation node, and screen out the available workstation nodes that meet the processing requirements of the alternative process combination to obtain the executable alternative workstation nodes. Using the executable alternative workstation nodes and their corresponding processing capacity parameters, the process flow of the order insertion task is reconstructed to generate a recombined process flow. The recombined process flow is then re-injected into the digital twin model, and simulation is performed to recalculate the order delivery reliability index and the resource efficiency loss index. Finally, the adjusted order insertion execution plan is output.

10. A garment production order insertion response system based on dynamic simulation, used to implement the garment production order insertion response method based on dynamic simulation as described in claim 1, the system comprising: The digital twin building block is used to build digital twin models of the production environment; The order insertion disturbance and event capture module is used to initiate order insertion simulation on the digital twin model based on a preset disturbance factor, and capture every dynamic event caused by resource contention during the simulation. The order insertion impact quantification module is used to quantify the impact of order insertion based on all dynamic events captured throughout the entire simulation cycle. The process route reorganization module is used to reorganize the process route based on the quantified evaluation report to obtain the process route after the insertion of orders.

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