A data feedback task flow dynamic optimization method and system

Abstract

The application provides a data feedback task flow dynamic optimization method and system, comprising: obtaining process feedback data, determining process execution time consumption and processing results through a production monitoring system log, identifying current process processing capacity, and simultaneously obtaining downstream assembly node pending workpiece quantity and upstream process output workpiece frequency fluctuation amplitude; obtaining each process queuing workpiece quantity and real-time load quantity corresponding to the rearranged process processing execution sequence, analyzing to obtain a process processing capacity matching degree index, and determining a global workpiece flow transfer stability evaluation result; obtaining total workpiece quantity change data of each process from a dynamic optimization implementation scheme, judging system processing throughput improvement degree, and determining a workpiece flow transfer process load balancing state.

Description

A method and system for dynamic optimization of task flow based on data feedback Technical Field

[0001] This invention relates to the field of information technology, and in particular to a method and system for dynamic optimization of task flow based on data feedback. Background Technology

[0002] Dynamic task flow optimization is a key research area for improving efficiency and resource utilization in modern complex systems, especially in high-concurrency, highly dynamic task processing. Optimizing the stability and efficiency of the task flow directly determines the overall system performance and responsiveness. Task flow optimization not only concerns the processing speed of individual nodes but also involves the coordination and balance of the entire system under dynamic changes. Existing methods in task flow optimization often focus on improving the performance of individual nodes, such as increasing resource investment or optimizing algorithms to improve the processing efficiency of a particular node. However, these methods typically ignore the interdependencies between nodes, leading to the possibility that local optimization can cause overall system instability. For example, if a node adjusts its processing speed based on feedback data, it may cause downstream nodes to become overloaded due to a surge in tasks, or disrupt the output rhythm of upstream nodes. This imbalance between nodes means that the system sacrifices overall coordination in the pursuit of local optima. The dynamism of task flow is reflected in the continuous changes in the real-time load, queued tasks, and task completion status of each node, and these changes can propagate rapidly from local adjustments at a single node, triggering a chain reaction. Global balancing requires real-time acquisition of the operational status of each node and dynamic adjustment of the execution order and resource allocation ratio of nodes when local optimization causes system imbalance. However, existing technologies struggle to quickly establish effective coordination mechanisms between nodes, especially given the complexity and instantaneous changes in feedback data, making accurate capture and processing difficult. For example, in a smart manufacturing system, a machine in a certain process optimizes its processing speed based on feedback data, but downstream assembly nodes become bottlenecks due to their inability to handle the surge in tasks, while upstream raw material supply nodes frequently stop due to mismatched schedules. Therefore, how to dynamically coordinate the execution order and resource allocation ratio of each node while it self-adjusts based on real-time feedback, to avoid local optimization causing blockages or imbalances in the global task flow, becomes a key issue in dynamic task flow optimization. Summary of the Invention

[0003] This invention provides a method for dynamic optimization of task flows based on data feedback, the method comprising:

[0004] The process feedback data, the quantity of workpieces to be processed, and the frequency fluctuation amplitude of output workpieces are obtained. The process feedback data includes equipment processing time, workpiece completion status, and equipment usage times. The quantity of workpieces to be processed represents the amount of workpieces accumulated at downstream assembly nodes. The frequency fluctuation amplitude of output workpieces represents the change in the output rhythm of upstream processes.

[0005] The process load change trend is determined based on the equipment utilization rate and capacity occupancy ratio. The workpiece flow rate is adjusted based on the comparison result between the number of workpieces to be processed and the preset assembly threshold. The potential process blockage location list and queued workpiece quantity are identified by combining the output workpiece frequency fluctuation amplitude.

[0006] Extract key process identifiers and blockage types from the potential process blockage location list, and adjust the equipment allocation scheme by combining real-time equipment load data, real-time capacity load data, and the number of queued workpieces. The equipment allocation scheme includes equipment number allocation and capacity allocation.

[0007] Based on the process equipment ratio and total number of completed workpieces in the equipment allocation scheme, the process processing execution order is rearranged in combination with workpiece priority and process dependency;

[0008] Based on the process execution sequence, obtain the number of queued workpieces and the real-time load, and determine the process processing capacity matching index and the global workpiece flow stability assessment result.

[0009] Based on the key process identifiers, blockage types, and global workpiece flow stability assessment results, the equipment allocation scheme and process execution sequence are adjusted to determine the dynamic optimization implementation scheme.

[0010] Based on the dynamic optimization implementation scheme, the change data of the total number of completed workpieces is obtained to determine the process load balance status.

[0011] Furthermore, the acquisition of process feedback data, the quantity of workpieces to be processed, and the frequency fluctuation amplitude of output workpieces includes:

[0012] Extract the operation logs of each process equipment from the production monitoring system, calculate the equipment processing time and workpiece dwell time based on the timestamps in the operation logs, and determine the process processing capacity coefficient in the process feedback data.

[0013] Based on the process processing capacity coefficient, low-processing-capacity process nodes are selected, the number of workpieces to be processed is counted, the changes in the number of workpieces output by the upstream process are monitored, and the frequency fluctuation amplitude of the output workpieces is determined based on the difference in the number of outputs in adjacent time periods.

[0014] Furthermore, the step of determining the process load change trend based on equipment utilization rate and capacity occupancy ratio, adjusting the workpiece flow rate based on the comparison result of the number of workpieces to be processed and the preset assembly threshold, and identifying the potential process blockage location list and queued workpiece quantity by combining the output workpiece frequency fluctuation amplitude includes:

[0015] The equipment utilization rate and capacity occupancy ratio of each process are collected to form a time series. The rate of change is calculated based on the time series to determine the load change trend of the process.

[0016] When the number of workpieces to be processed exceeds the preset assembly threshold, a flow restriction command is sent to the upstream process to adjust the workpiece release frequency.

[0017] Based on the output time interval of the upstream process after the flow restriction instruction, the coefficient of variation is calculated to determine the process with disordered output rhythm. The potential process blockage location list is generated by combining the process load change trend and the coefficient of variation. The potential process blockage location list includes the process number and the number of queued workpieces.

[0018] Furthermore, the step of calculating the coefficient of variation based on the output time interval of the upstream process after the flow restriction instruction to determine the process with disordered output rhythm includes:

[0019] Collect the output time of the upstream process after the flow restriction command is executed, and calculate the coefficient of variation of the adjacent output time interval sequence;

[0020] When the coefficient of variation exceeds a preset threshold, the process with disordered rhythm is marked, the number of queued workpieces before the process with disordered rhythm is counted, and a list containing process number and degree of congestion is generated.

[0021] Furthermore, the step of extracting key process identifiers and blockage types from the potential process blockage location list, and combining this with real-time equipment load data, real-time production capacity load data, and the queued workpiece quantity adjustment equipment allocation scheme, includes:

[0022] Extract the key process identifiers and blockage types from the potential process blockage location list, obtain the real-time load data of the equipment and the real-time capacity load data, and calculate the ratio of the queued workpiece quantity to the buffer capacity as the queue saturation.

[0023] Based on the weighted sum of the real-time load data of the equipment, the real-time load data of the production capacity, and the queuing saturation, a resource shortage score is determined. Based on the resource shortage score and the congestion type, the number of equipment and the upper limit of the production capacity are adjusted to form the equipment allocation scheme.

[0024] Furthermore, the step of rearranging the process execution order based on the process equipment ratio and total number of completed workpieces in the equipment allocation scheme, combined with workpiece priority and process dependencies, includes:

[0025] Extract the process equipment ratio from the equipment allocation scheme, count the total number of completed workpieces corresponding to the process equipment ratio, and determine the process with a downward trend based on the time series slope.

[0026] Based on the downward trend process, extract the workpiece priority value and process dependency relationship, construct a directed graph, generate a feasible process sequence based on topological sorting, and select the sequence with the smallest comprehensive evaluation value as the process processing execution order.

[0027] Furthermore, the step of obtaining the queued workpiece quantity and real-time load based on the process execution order, and determining the process processing capacity matching index and the global workpiece flow stability assessment result, includes:

[0028] The number of queued workpieces and the real-time load under the process execution sequence are obtained, and the processing capacity value of each process is determined based on the comparison between the actual processing rate and the standard processing rate.

[0029] The capacity difference between adjacent processes is calculated based on the processing capacity value, the proportion of mismatched processes is statistically analyzed, and the global workpiece flow stability assessment result is determined by weighted calculation combined with the buffer occupancy rate.

[0030] Furthermore, the adjustment of the equipment allocation scheme and process execution sequence based on the key process identifier, blockage type, and global workpiece flow stability assessment results to determine the dynamic optimization implementation scheme includes:

[0031] Obtain the current equipment allocation ratio and the process execution order, extract the process information corresponding to the key process identifier, and identify the capacity bottleneck process and resource surplus process by combining the blockage type and the global workpiece flow stability assessment result.

[0032] Based on the bottleneck processes and resource-surplus processes, a linear programming method is used to adjust the equipment allocation ratio and process execution sequence to form the dynamic optimization implementation scheme. The dynamic optimization implementation scheme includes the number of equipment adjustments and the position of the execution sequence adjustments, wherein:

[0033] The dynamic optimization implementation scheme, based on the bottleneck processes and resource-surplus processes, uses a linear programming method to adjust the equipment allocation ratio and process execution sequence, forming the dynamic optimization implementation scheme, including:

[0034] Extract the blockage type and workpiece dwell time corresponding to the key process identifier, count the number of transmission delays, mark processes with severe delays, and determine the number of equipment gaps;

[0035] Based on the number of equipment shortages and process dependencies, the execution order of processes is adjusted, equipment transfer paths and relocation schedules are formulated, and the dynamic optimization implementation plan, which includes equipment allocation and process priority rearrangement, is formed.

[0036] Furthermore, the step of obtaining the total number of completed workpieces change data based on the dynamic optimization implementation scheme and determining the process load balance status includes:

[0037] Extract the data on the change in the total number of completed workpieces from the dynamic optimization implementation scheme, and calculate the rate of change and the system processing throughput;

[0038] Calculate the load coefficient of each process based on the rate of change, statistically analyze the standard deviation of the load coefficient, mark overloaded and idle processes based on the load coefficient distribution, and determine the load balance status of the process.

[0039] On the other hand, this solution also discloses a data feedback task flow dynamic optimization system, which includes: a feedback data acquisition and processing capability identification module, used to acquire process feedback data, the quantity of workpieces to be processed, and the frequency fluctuation amplitude of output workpieces, wherein the process feedback data includes equipment processing time, workpiece completion status, and equipment usage times, the quantity of workpieces to be processed represents the workpiece accumulation at downstream assembly nodes, and the frequency fluctuation amplitude of output workpieces represents the change in the output rhythm of upstream processes; a load assessment and congestion identification module, used to determine the process load change trend based on equipment utilization rate and capacity occupancy ratio, adjust workpiece flow based on the comparison result of the quantity of workpieces to be processed and a preset assembly threshold, and identify a potential process congestion location list and queued workpiece quantity in combination with the frequency fluctuation amplitude of output workpieces; a key process analysis and equipment allocation adjustment module, used to extract key process identifiers and congestion types from the potential process congestion location list, and combine real-time equipment load data, The system includes real-time capacity load data and an equipment allocation scheme for adjusting the queued workpiece quantity. The equipment allocation scheme comprises equipment number allocation and capacity allocation. An equipment allocation scheme evaluation and process sequence rearrangement module is used to rearrange the process execution order based on the process equipment ratio and total number of completed workpieces in the equipment allocation scheme, combined with workpiece priority and process dependencies. A processing capacity matching degree analysis module is used to obtain the queued workpiece quantity and real-time load based on the process execution order, determine the process processing capacity matching degree index and the global workpiece flow stability assessment result. A dynamic optimization scheme generation module is used to adjust the equipment allocation scheme and process execution order based on the key process identifier, blockage type, and global workpiece flow stability assessment result, and determine a dynamic optimization implementation scheme. A throughput evaluation and load balancing judgment module is used to obtain the total number of completed workpieces change data based on the dynamic optimization implementation scheme and determine the process load balancing status. The technical solution provided by this embodiment of the invention may include the following beneficial effects:

[0040] This invention discloses a dynamic task flow optimization method and system based on data feedback. Addressing issues such as process blockage, uneven equipment allocation, and unstable workpiece flow during production, it analyzes process execution time, processing results, and upstream and downstream workpiece flow data by collecting real-time production monitoring system logs. This identifies the location and type of blockages in key processes and assesses the matching degree of process processing capacity and overall flow stability. By combining real-time equipment load and workpiece priority, this invention dynamically adjusts equipment allocation schemes and process execution sequences, optimizes workpiece flow limiting strategies, effectively alleviates pressure on downstream assembly nodes, and stabilizes upstream output rhythm, ultimately achieving increased process throughput and load balancing. Furthermore, this invention integrates blockage types, equipment occupancy ratios, and flow stability assessment results through a production scheduling center to form a dynamic optimization implementation plan. This significantly improves system throughput, ensures efficient and stable workpiece flow, and is suitable for complex production environments with high dynamics and multiple constraints. Attached Figure Description

[0041] Figure 1 is a flowchart of a data feedback task flow dynamic optimization method according to the present invention.

[0042] Figure 2 is a schematic diagram of the structure of a data feedback task flow dynamic optimization system according to the present invention. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and thoroughly described below with reference to the accompanying drawings. The described embodiments are merely some embodiments of the present invention.

[0044] As shown in Figures 1-2, the data feedback task flow dynamic optimization method and system of this embodiment may specifically include:

[0045] Step S101: Obtain process feedback data, determine the process execution time and processing results through the production monitoring system logs, identify the current processing capacity of the process, and at the same time obtain the quantity of workpieces to be processed at the downstream assembly node and the frequency fluctuation range of the output workpieces of the upstream process.

[0046] Real-time operation logs of each process's equipment in the production monitoring system are acquired. The power-on and power-off timestamps of each device are extracted from the logs. The actual processing time of the equipment is calculated based on the timestamp difference. Simultaneously, the entry and completion times of workpieces in the process are read to determine the dwell time of a single workpiece in the current process. This dwell time is compared with a preset standard process time to obtain a process processing capacity coefficient. Process nodes with processing capacities below a preset threshold are selected based on this coefficient. The amount of workpieces awaiting processing at downstream assembly nodes is statistically analyzed using the workshop data acquisition terminal. The change in the number of workpieces output by upstream processes per unit time is monitored. The absolute value of the difference in output quantities between adjacent time periods is calculated. When the absolute value of the difference exceeds a preset fluctuation threshold, it is determined to be a high-fluctuation state, and an evaluation value for the frequency fluctuation amplitude of workpiece output from upstream processes is obtained.

[0047] In one embodiment, the production monitoring system acquires operational data in real time through data acquisition devices deployed on equipment at each process stage, records the equipment's start-up and shutdown timestamps, and calculates the actual processing time. When workpieces move between processes, automatic identification technology records their entry and completion times, calculating the dwell time of each individual workpiece.

[0048] Specifically, the preset standard process time is determined based on the standard processing time specified in the product process card. For example, the standard time for drilling a part is 180 seconds. When the actual dwell time is 200 seconds, the process capacity coefficient is calculated as 180 ÷ 200 = 0.9, indicating that the actual processing capacity of the process is 90% of the standard capacity. By setting a preset threshold of 0.85, process nodes with a processing capacity coefficient lower than 0.85 are selected, and these nodes are the bottleneck processes that need to be monitored closely. The workshop data acquisition terminal uses photoelectric sensors to count the number of workpieces waiting to be processed on the conveyor belts of downstream assembly nodes. When the accumulation exceeds 70% of the buffer capacity, an alarm is triggered.

[0049] It should be noted that the fluctuation range of the output workpiece frequency of the upstream process is evaluated by counting the output quantity every 5 minutes. The absolute value of the difference between the output quantities of two adjacent time periods is the fluctuation value. The quantity of workpieces to be processed at the downstream assembly node and the fluctuation range of the output workpiece frequency of the upstream process are obtained. The fluctuation range includes two states: low-frequency stable fluctuation and high-frequency abnormal fluctuation. When the fluctuation value exceeds 20% of the average output quantity, it is judged as a high fluctuation state. The obtained fluctuation range evaluation value is used for subsequent adjustment of the coordination between processes.

[0050] Step S102: Based on the process load change trend recorded by equipment utilization rate and capacity occupancy ratio, if the number of workpieces to be processed at the downstream assembly node exceeds the preset assembly threshold, the workpiece flow rate is restricted, and the degree of disorder in the output rhythm of the upstream process is assessed based on the fluctuation amplitude of the output workpiece frequency, and a list of potential process blockage locations and the number of workpieces in queue for each process are identified.

[0051] The system acquires usage rate data and capacity utilization ratio data of equipment in each process over a past period. Data is collected at fixed time intervals to form a time series. The rate of change is calculated as the ratio of the difference between adjacent data points to the time interval. The sign of the rate of change indicates whether the process load is increasing or decreasing. Simultaneously, the real-time pending workpiece quantity at downstream assembly nodes is read. If the pending workpiece quantity exceeds a preset assembly threshold, a flow restriction command is sent to the upstream process to reduce the workpiece release frequency to a preset percentage of the original frequency. After the flow restriction command is executed, the changes in the workpiece output time interval of each upstream process are monitored. A stability index for the output rhythm is calculated using statistical analysis methods. When the stability index exceeds a preset threshold, the output rhythm of that process is determined to be disordered, and the disordered process number and the degree of disorder are recorded. Combining the process load change trend and disorder level values, the ratio of the average arrival rate to the average service rate of each process is calculated as a utilization rate index. When the utilization rate index is greater than a preset threshold, it is marked as a potentially blocked process. The number of queued workpieces in front of the potentially blocked process is counted. Based on the number of queued workpieces, the utilization rate index, and the disorder level value, a process blockage location list is generated. The list includes the process number, the blockage severity score, and the current number of queued workpieces.

[0052] In one embodiment, the production monitoring system collects equipment utilization and capacity occupancy data every 5 minutes through data acquisition terminals deployed in each process, forming a continuous time series.

[0053] Specifically, equipment utilization rate refers to the ratio of actual operating time to planned operating time, while capacity utilization rate refers to the ratio of actual output to rated capacity. The rate of change is obtained by dividing the difference between data collected at two adjacent time points by the time interval. A positive rate of change indicates an upward trend in process load; a negative rate of change indicates a downward trend in process load.

[0054] It should be noted that the preset assembly threshold for downstream assembly nodes is typically set to 80% of the assembly buffer capacity. When the number of workpieces to be processed exceeds this threshold, the system automatically sends a flow restriction command to each upstream process, reducing the workpiece release frequency to 70% of the original frequency. This dynamic control mechanism can prevent production line blockage caused by excessive workpiece accumulation at downstream nodes.

[0055] Preferably, after implementing flow control, the system continuously collects the workpiece output times of each upstream process at 10 time points, forming a time interval sequence. The coefficient of variation is obtained by calculating the ratio of the standard deviation to the mean of the time interval sequence. This indicator eliminates the influence of dimensions and objectively reflects the relative fluctuation of the output rhythm. When the coefficient of variation of a process exceeds 0.3, the output rhythm of that process is determined to be disordered, and the system records its process number and the specific coefficient of variation value.

[0056] In one possible implementation, the utilization rate index is calculated by comprehensively considering the dynamic load status of the process. The average arrival rate is obtained by counting the number of workpieces arriving at the process per unit time, and the average service rate is obtained by counting the number of workpieces completed by the process per unit time. When the utilization rate index exceeds 0.95, it indicates that the process is close to saturation and is highly prone to congestion. The congestion severity score uses a weighted calculation method, where the number of queued workpieces accounts for 50% of the weight, the utilization rate index accounts for 30%, and the disorder level value accounts for 20%. After normalization, a score value of 0-100 is obtained.

[0057] For example, if the utilization rate of a certain stamping process reaches 0.98, the number of workpieces in the queue is 45, the coefficient of variation is 0.35, and after weighted calculation, the severity score of the blockage is 85 points, indicating a severe blockage. The system records this process information in the blockage location list, providing data support for subsequent dynamic optimization and adjustment.

[0058] Monitor the change in the number of workpieces output per unit time in upstream processes. When the change exceeds a preset threshold, mark it as a process with disordered rhythm. Check the number of workpieces currently being processed and the backlog of workpieces to be processed in each process. Count the total number of workpieces waiting in line in front of each process. Identify the process locations where the number of workpieces in line exceeds the normal processing capacity. Mark processes where the equipment utilization rate exceeds saturation and the number of workpieces in line continues to increase as potential blockage points. Establish a list of locations containing process names and blockage locations.

[0059] The system monitors the number of workpieces output at preset time intervals in each upstream process. It calculates the ratio of the difference in output quantity between adjacent time intervals to the output quantity of the previous time interval as the variation amplitude. If the variation amplitude exceeds a preset variation threshold, the process is marked as a rhythm disorder process. Based on the rhythm disorder process labeling, the system reads the number of workpiece sensor signals currently being processed in each process's equipment, counts the backlog of workpieces waiting to be processed in the process buffer, and adds the currently being processed number to the backlog to obtain the total number of workpieces queuing ahead of the process. When the total number of workpieces exceeds the rated processing capacity of the process, it is determined to be an overload state. The system acquires real-time equipment utilization data and historical queued workpiece quantity change sequences for the overloaded process. If the equipment utilization rate exceeds a preset saturation threshold and the total number of queued workpieces continues to increase within a continuous monitoring period, the process is marked as a potential blockage point. Based on the rhythm disorder labeling, overload state, and equipment saturation state, the blockage type is determined, and a location list containing process names and blockage types is established.

[0060] In one embodiment, the production monitoring system uses photoelectric counters installed at the exit of each process to count the number of workpieces passing through every 5 minutes.

[0061] Specifically, the system records the output quantity in the Nth time interval as Qn, the output quantity in the (N-1)th time interval as Qn-1, and the variation range is calculated as |Qn-Qn-1| / Qn-1. When this ratio exceeds 0.25, it indicates that the output rhythm of the process has fluctuated significantly, and the system automatically marks the process as a rhythm disorder process.

[0062] It should be noted that the workpiece being processed inside the equipment is detected by pressure sensors or position sensors installed inside the equipment.

[0063] For example, pressure sensors inside a stamping press can detect the presence of workpieces, with each sensor signal corresponding to a workpiece being processed. The amount of workpieces piling up in the process buffer is statistically analyzed using an array of infrared through-beam sensors. The sensor array is arranged at certain intervals within the buffer, and the number of sensors that are blocked reflects the number of piling up workpieces.

[0064] Preferably, the rated processing capacity is determined jointly based on the designed capacity of the process and the physical capacity of the buffer zone. For example, the rated processing capacity of a drilling process is 30 pieces. When the total number of workpieces waiting in the queue reaches 35, the system determines that the process is overloaded. This overload determination mechanism can identify potential risks before the process becomes completely blocked.

[0065] In one possible implementation, the preset saturation threshold for equipment utilization is set to 0.9, meaning that equipment utilization exceeding 90% is considered saturated. The system analyzes changes in the number of queued items over the last 10 monitoring periods; if more than 8 periods show an increasing trend, it is considered a continuous increase. Congestion types are mainly divided into three categories: rhythm imbalance congestion, caused by disordered upstream output rhythm; insufficient capacity congestion, caused by equipment remaining at saturation for a long period; and cumulative congestion, caused by the continuous accumulation of queued items that cannot be processed.

[0066] For example, the location list is stored in a structured data format, including fields such as process number, process name, congestion type, current queue length, equipment utilization, and rhythm disorder flag. By establishing such a list, the production scheduling system can quickly locate bottleneck processes on the production line, achieving precise capacity allocation and process optimization.

[0067] Step S103: Extract key process identifiers and blockage types from the potential process blockage location list, obtain real-time load data of equipment for each process and real-time capacity load data through the production scheduling center, assess the current equipment occupancy ratio of each process in conjunction with the number of queued workpieces, and adjust equipment allocation.

[0068] Extract key process identifiers and corresponding blockage type codes from the potential process blockage location list. Obtain real-time equipment load data and real-time capacity load data for the key processes through the data interface of the production scheduling center. Read the current queued workpiece count for each process and calculate the ratio of the queued workpiece count to the process buffer capacity as the queue saturation. Based on the real-time equipment load data, real-time capacity load data, and queue saturation, multiply the three data items by their respective preset weight coefficients and sum them to obtain the resource stress score for each process, where the sum of the weight coefficients for equipment load, capacity load, and queue saturation is 1. Based on the resource stress score, identify resource-stressed and resource-rich processes. Determine the allocation priority based on the blockage type code and calculate the optimal solution for transferring equipment from resource-rich processes to resource-stressed processes. The optimal solution for transferring equipment includes allocating the number of equipment units and capacity, resulting in an equipment unit transfer plan. Simultaneously, adjust the upper limit of capacity capacity for each process based on the blockage type code and resource stress score. The equipment transfer plan and the capacity limit adjustment value are integrated to form an equipment allocation scheme that includes an equipment reallocation list and a capacity adjustment parameter table. The scheme specifies the new equipment configuration quantity and the maximum allowed capacity output value for each process.

[0069] In one embodiment, the production scheduling center maintains a real-time updated list of potential process congestion locations, recording information on all processes at risk of congestion. The system extracts key process identifiers from the list, each corresponding to a unique process number, and also extracts congestion type codes, including three main types: equipment failure, insufficient capacity, and material shortage. Through the scheduling center's data bus, the system collects real-time equipment load data for each process, reflecting the current operating intensity of the equipment; it also acquires capacity load data, representing the ratio of actual output to rated capacity. Queue saturation is calculated based on the physical capacity limit of the process buffer. When the number of queued workpieces approaches the buffer capacity, the saturation value approaches 1, indicating that the process is approaching a critical congestion state. By multiplying each indicator by its corresponding weight and summing the results, a resource stress score between 0 and 100 is obtained, with a higher score indicating greater resource stress for the process.

[0070] It should be noted that the system determines the optimal allocation plan by evaluating the equipment transfer benefits between each process. The transfer benefits comprehensively consider the resource shortage of the target process, the resource surplus of the source process, and the priority of the congestion type.

[0071] For example, a production line transferred two machines from the surplus deburring process to the busy drilling process, effectively alleviating the production bottleneck.

[0072] In one possible implementation, the upper limit of production capacity is adjusted using a dynamic threshold mechanism. The system determines the adjustment range based on both the congestion type code and the resource scarcity score. For processes with equipment failure-related congestion, the upper limit of production capacity is reduced to 70% of its original value to prevent equipment overload. For processes with insufficient production capacity congestion, the upper limit of production capacity can be increased to 120% of its original value by adding equipment. For processes with material shortage-related congestion, the production capacity remains unchanged, with the focus on resolving material supply issues.

[0073] For example, the final equipment allocation plan consists of two core components. The equipment reallocation list records in detail the equipment changes for each process, including the number of equipment transferred in, the number transferred out, and the final configuration quantity; the capacity adjustment parameter table records the adjusted upper limit of capacity for each process, the recommended operating rate, and the buffer capacity configuration.

[0074] Understandably, this solution enables dynamic balancing of production line resources. The system can reconfigure resources across the entire production line within 10 minutes, increasing overall equipment utilization from 85% to 92% while reducing the number of blocked processes by more than 60%. Furthermore, the system establishes a feedback mechanism for solution execution, monitoring the operational status of each process in real time after adjustments. When new resource imbalances are detected, the system automatically triggers the next round of optimization and adjustment.

[0075] Step S104: Identify the process equipment ratio and the corresponding total number of completed workpieces in the equipment allocation scheme. If the total number of completed workpieces corresponding to the process equipment ratio shows a downward trend, rearrange the process processing execution order according to workpiece priority and process dependency.

[0076] The system identifies the equipment ratio for each process in the equipment allocation scheme, calculates the total number of completed workpieces over multiple monitoring periods corresponding to these ratios, and uses linear regression to calculate the time series slope of the total number of completed workpieces. If the slope is negative, a downward trend is identified, and the process numbers exhibiting this downward trend are recorded. Based on these downward trend process numbers, the system extracts the workpiece priority values ​​related to these processes from the production management database. These priority values ​​are determined by a comprehensive evaluation of delivery urgency, customer importance, and product profit margin. Simultaneously, the system obtains the dependencies between processes and constructs a directed graph representing the constraints between processes. Based on the workpiece priority values ​​and the directed graph of process dependencies, a set of feasible process sequences satisfying the dependency constraints is generated. For each feasible sequence, the sum of the products of the expected completion time of all workpieces and their corresponding priority values ​​is calculated as the comprehensive evaluation value of the sequence. The sequence with the smallest comprehensive evaluation value is selected from the set of feasible process sequences as the optimized process processing execution order. The positions of processes exhibiting a downward trend in the new order are adjusted, forming a rearranged process processing execution order table, which specifies the execution priority of each process.

[0077] In one embodiment, the equipment allocation scheme records the ratio of the number of equipment configured for each process to the total number of equipment on the production line, i.e., the equipment ratio value. The system calculates the total number of completed workpieces for each process every hour, continuously recording data for 24 monitoring cycles to form a time-series dataset. Linear regression fitting is performed using the least squares method to calculate the slope of the total number of completed workpieces over time. When the slope is negative and its absolute value exceeds a preset threshold, it indicates that the production efficiency of that process is declining, and the system automatically marks it as a target process requiring optimization.

[0078] Specifically, the linear regression model uses time as the independent variable and the total number of completed workpieces as the dependent variable. The optimal fitted line is determined by minimizing the squared error between the predicted and actual values. The slope reflects the rate of change in the total number of workpieces completed per unit time; a negative slope indicates a gradual decrease in production capacity. For example, if the completion rate of a certain stamping process decreases from 120 pieces per hour to 95 pieces per hour within 24 hours, the linear regression yields a slope of -1.04, clearly showing a downward trend, necessitating an adjustment to the process sequence.

[0079] It should be noted that the workpiece priority assessment adopts a multi-factor comprehensive evaluation.

[0080] For example, a batch of automotive parts, based on a comprehensive assessment of delivery urgency, customer importance, and product profit margin, is ultimately assigned a priority value of 73.5. During process rescheduling, this priority is given to processing the preferred parts. The construction of the directed graph of process dependencies is based on the constraints of the production process flow. Each node in the graph represents a process, and directed edges represent the sequential constraints between processes. If process A must be completed before process B, a directed edge is drawn from node A to node B. The system automatically extracts the pre- and post-process relationships between processes by parsing the process route file, constructing a complete dependency graph. The processing flow of a certain product includes five processes: blanking, turning, milling, drilling, and assembly. Blanking must be performed first; turning and milling can be performed concurrently but must both be performed after blanking; drilling must be performed after turning; and assembly must wait for all processing processes to be completed. These constraints are accurately represented in the directed graph.

[0081] In one possible implementation, a depth-first search is used to traverse the directed graph, generating a sequence of execution steps that satisfies all dependency constraints. First, the in-degree of each node is calculated, i.e., the number of edges pointing to that node. Nodes with an in-degree of 0 indicate no pre-existing constraints and can be executed first. These nodes are added to a queue. Each time, a node is removed from the queue and added to the sorted sequence, while the in-degree of all its successor nodes is decremented by 1. When the in-degree of a successor node becomes 0, it is added to the queue. This process is repeated until all nodes have been processed, resulting in one or more feasible execution sequences. For cases with multiple nodes having an in-degree of 0, permutations and combinations are generated to form a set of feasible sequences.

[0082] For example, the calculation of the comprehensive evaluation value fully considers the impact of workpiece priority on production scheduling. For each feasible sequence, the system simulates the expected completion time of each workpiece when executing the sequence, and calculates the cumulative time for each workpiece through all processes based on the standard processing time of the process and equipment configuration. The completion time of each workpiece is multiplied by its priority value; the higher the priority of the workpiece, the greater the impact of its completion time on the evaluation value. The sum of the products of all workpieces yields the comprehensive evaluation value of the sequence; the smaller the value, the earlier the high-priority workpieces can be completed, and the better the scheduling scheme.

[0083] Understandably, the system selects the sequence with the lowest overall evaluation value from all feasible sequences as the final execution plan. For processes showing a downward trend, the system specifically adjusts their position in the new sequence, balancing the load by changing the execution order and preventing the process from becoming a production bottleneck. Furthermore, the process processing execution sequence table not only specifies the execution priority of each process but also includes dynamic scheduling parameters, such as the maximum allowed waiting time and rules for handling emergency orders, achieving refined management of production scheduling. Through this data-driven dynamic optimization mechanism, the overall efficiency of the production line has increased by more than 15%, and the on-time delivery rate has increased from 88% to 96%.

[0084] Step S105: Obtain the number of queued workpieces and real-time load of each process corresponding to the rearranged process execution order, analyze and obtain the process processing capacity matching index, and determine the global workpiece flow stability assessment result.

[0085] The process execution order is rearranged, and the number of queued workpieces and real-time equipment load for each process are read. The actual processing rate is calculated based on the number of workpieces completed per unit time and compared with the standard processing rate of the process design to obtain the processing capacity value of each process. The capacity difference between adjacent processes is calculated based on the processing capacity value. The processing capacity value of the upstream process is compared with that of the downstream process. If the absolute value of the difference exceeds a preset matching threshold, it is marked as a mismatched process pair. The proportion of mismatched process pairs to the total number of process pairs is calculated to obtain the process processing capacity matching degree index. Based on the process processing capacity matching degree index and the occupancy rate of each process buffer, the matching degree index is multiplied by a preset weighting coefficient and added to the weighted average of the buffer occupancy rates to obtain a global flow stability score. When the score is higher than a preset stability threshold, it is determined to be in a stable state; otherwise, it is determined to be in an unstable state. The global workpiece flow stability assessment result is then determined.

[0086] In one embodiment, the system obtains the rearranged process execution order, which has been optimized. The current queued workpiece volume for each process is read through a real-time workshop monitoring terminal, while real-time load data for each piece of equipment is obtained from the equipment management module. The actual processing rate is calculated by statistically analyzing the number of workpieces completed in the most recent hour, while the standard processing rate is derived from the theoretical capacity value specified in the process design document. Dividing the actual processing rate by the standard processing rate yields the processing capacity value, which reflects the ratio between the actual production capacity and the designed capacity of the process.

[0087] Specifically, the calculation of processing capacity values ​​provides a quantitative basis for evaluating the operational status of each process. When the processing capacity value of a process is 0.8, it indicates that the process has only reached 80% of its designed capacity, which may indicate problems such as aging equipment, unfamiliar operators, or untimely material supply. The system identifies capacity bottlenecks by comparing the processing capacity values ​​of adjacent processes. When the upstream process's processing capacity value is 0.95 while the downstream process's is only 0.75, the difference reaches 0.2, exceeding the preset matching threshold of 0.15. The system then marks this pair of processes as a mismatched process pair.

[0088] It should be noted that the process capacity matching index assesses the coordination level of the entire production line by statistically analyzing the proportion of mismatched process pairs. A certain production line has 10 pairs of adjacent processes, of which 3 pairs are identified as mismatched process pairs, with a matching index calculated as 70%, indicating a significant imbalance in production capacity.

[0089] Preferably, the global workflow stability score is calculated using a two-factor weighted method. The weighting coefficient for the matching degree index is set to 0.6, reflecting the importance of inter-process coordination; the weighting coefficient for the weighted average of buffer occupancy rate is set to 0.4, reflecting the impact of buffer status on system stability. When the matching degree index is 70% and the average buffer occupancy rate is 65%, the stability score is calculated as 0.6 × 70 + 0.4 × 65 = 68. The system sets a stability threshold of 75; the current score of 68 is below the threshold, indicating that the system is in an unstable state.

[0090] In one possible implementation, the evaluation results trigger a corresponding adjustment mechanism. In an unstable state, the system automatically initiates a capacity rebalancing procedure, adjusting equipment allocation or modifying process sequences to improve the matching degree until the stability score reaches the preset requirements, thereby achieving dynamic optimization of the production process.

[0091] Step S106: The production scheduling center re-identifies the equipment allocation ratio and process execution sequence, and determines the dynamic optimization implementation plan by combining the key process identifier, blockage type information and global workpiece flow stability assessment results to maintain process throughput.

[0092] The current equipment allocation ratio and process execution order are re-acquired through the data interface of the production scheduling center. The equipment configuration quantity of each process in the equipment allocation ratio is read, and the execution priority ranking of each process in the execution order table is extracted. The two data are merged to form a basic dataset for process resource configuration. From the basic dataset for process resource configuration, process information corresponding to key process identifiers is selected, and the adjustment priority is determined by combining the blocking type code of each process. Based on the unstable factors in the global workpiece flow stability assessment results, bottleneck processes that require additional equipment and resource-rich processes that can reduce equipment are identified. Based on the bottleneck processes and resource-rich processes, a resource configuration optimization model is established using mathematical optimization methods. Under the premise of meeting the total equipment constraints and basic process configuration requirements, the new equipment allocation ratio and the adjusted process execution order are obtained. The new equipment allocation ratio and the adjusted process execution order are integrated into a dynamic optimization implementation plan. The plan specifies the equipment adjustment quantity and execution order adjustment position for each process. After implementation, the actual throughput changes are monitored. When the throughput remains within the preset range, it is confirmed that the goal of maintaining the process processing throughput has been achieved.

[0093] In one embodiment, the production scheduling center, as the core control unit of the entire production system, acquires various production data in real time through standardized data interfaces. The system first reads the current equipment allocation ratio data, which records information such as the number of equipment units configured for each process, equipment model, and rated capacity. Simultaneously, it extracts the process execution sequence table from the execution management module, which details the sequence and priority of each process in the production flow. Through a data integration program, the equipment configuration information and execution sequence information are matched and associated according to the process number, forming a unified basic dataset for process resource allocation. This dataset serves as the basis for subsequent optimization decisions.

[0094] It should be noted that the selection of critical processes follows a multi-criteria judgment principle. The system extracts records with critical process identifiers from the basic dataset; these identifiers were already marked during the initial congestion analysis. Each critical process is associated with a specific congestion type code, including various types such as equipment failure, insufficient capacity, material shortage, and rhythm mismatch. Based on the severity and scope of the congestion type, the system automatically calculates and adjusts priority scores: equipment failure is assigned the highest priority of 1.5, insufficient capacity 1.2, material shortage 1.0, and rhythm mismatch 0.8.

[0095] Specifically, the identification of bottleneck and surplus processes is based on multiple indicators in the global workpiece flow stability assessment. When the equipment utilization rate of a process consistently exceeds 95%, the number of queued workpieces exceeds 80% of the buffer capacity, and it is marked as an unstable factor in the stability assessment, the system identifies it as a bottleneck process, indicating that the process urgently needs additional equipment resources. Conversely, processes with equipment utilization rates below 60%, essentially empty buffers, and normal operation of upstream and downstream processes are identified as surplus processes, and their equipment configuration can be reduced. For example, the drilling process on an assembly line had an equipment utilization rate of 98% and 120 workpieces waiting to be processed, which was identified as a typical bottleneck; while the adjacent deburring process had an equipment utilization rate of only 45%, and was identified as a surplus process.

[0096] Preferably, the application of linear programming involves a complex mathematical modeling process. The objective function is set to maximize the total throughput of the entire production line, expressed as the weighted sum of the actual output of each process, with the weights determined based on the contribution of each process to the product value chain. Constraints include three levels: first, a total equipment limit, meaning the sum of the number of devices in all processes cannot exceed the total number of available devices in the workshop; second, a minimum configuration requirement for each process, requiring at least one device to maintain basic operation; and third, a capacity balance constraint, meaning the capacity difference between adjacent processes cannot exceed a preset balance threshold to avoid creating new bottlenecks. The linear programming problem is solved using the simplex algorithm or interior-point method to obtain the optimal equipment configuration for each process. Simultaneously, based on the new capacity distribution, the system recalculates the execution priority of each process, giving higher execution priority to processes with lower capacity to fully utilize their limited processing capabilities.

[0097] In one possible implementation approach, the development of a dynamic optimization plan needs to consider the feasibility of practical operation. The plan clearly specifies the details of equipment adjustments: two machines are moved from the resource-rich deburring process to the drilling process, which faces capacity bottlenecks; the milling process, originally ranked 5th, is moved to 3rd to alleviate waiting time in downstream assembly processes. Each adjustment is accompanied by a detailed implementation time window, and equipment relocation is usually scheduled during production breaks to avoid disrupting normal production.

[0098] For example, the system tracks the implementation effect of the solution through a real-time monitoring mechanism. Within 24 hours after the equipment adjustment is completed, the system collects the actual throughput data of each process every hour and calculates the average throughput of the entire line. The preset throughput maintenance range is between 95% and 105% of the original throughput. When the throughput remains within this range for three consecutive monitoring cycles and no new blockages occur, the system determines that the target of maintaining the process processing throughput has been achieved.

[0099] Understandably, this dynamic optimization mechanism can adaptively adjust resource allocation based on real-time changes in production status, avoiding resource waste and capacity bottlenecks inherent in traditional fixed-configuration models. Through continuous monitoring and optimization, it can effectively improve the overall equipment efficiency of the production line and the on-time delivery rate of products. Furthermore, the system establishes an optimization effect evaluation archive, recording the specific parameters and implementation effects of each adjustment, providing historical experience for subsequent optimization decisions.

[0100] Extract the names of key processes and their corresponding blockage types in the production line, obtain the current workpiece's dwell time and transmission delay count between processes, formulate the adjustment content for equipment reallocation and the reordering scheme for process execution sequence, determine the process locations where the number of additional equipment units needs to be increased and the specific quantity of capacity allocation that needs to be adjusted, and form a complete implementation plan that includes equipment allocation time arrangements and process priority reordering.

[0101] Extract the names of key processes and their corresponding congestion types from the production line. Read the dwell time records of each workpiece between processes from the production monitoring database. Count the number of transmission delays for each process. Processes with dwell times exceeding twice the average dwell time are marked as severely delayed processes. Combine congestion and delay information to form a process problem diagnosis table. Based on the congestion severity in the process problem diagnosis table and the current capacity requirement, calculate the theoretical number of machines required for each process by dividing the capacity requirement by the processing capacity of a single machine. Determine the number of machine gaps by comparing with the existing equipment configuration. Simultaneously, reorder the processes based on inter-process dependencies and the number of delays to obtain the adjusted process execution order. Based on the number of machine gaps and the adjusted process execution order, determine the specific number and path of machines to be transferred from surplus processes to gapped processes. Query the standard relocation time for equipment based on the equipment model, and determine the allocation time window by combining the downtime maintenance period in the production plan. Compile a relocation start and end timetable for each machine. Integrate the equipment transfer path, relocation timetable, and new process execution order. Recalculate the target capacity value for each process based on the adjusted equipment configuration, and set the allowable fluctuation range as a preset percentage above and below the target value to form a complete implementation plan that includes equipment allocation arrangements and process priority reordering.

[0102] In one embodiment, the system extracts basic information about all critical processes from the production line monitoring database, including process name, workshop, and current equipment configuration. Each critical process is associated with a specific type of blockage, such as reduced processing speed due to equipment performance degradation, increased rework caused by process parameter deviations, and extended waiting time due to untimely material supply. The system analyzes the workpiece flow log to calculate the dwell time of each workpiece between adjacent processes, i.e., the time interval between the completion of the previous process and the start of the next process. When the dwell time of a workpiece reaches more than twice the average dwell time, the system records a transmission delay event.

[0103] It should be noted that the identification of severely delayed processes employs a dynamic threshold determination mechanism. The system first calculates the average downtime of all processes. For example, if the average downtime of a stamping production line is 15 minutes, then 30 minutes is set as the delay determination threshold. When the downtime of five consecutive workpieces in a process exceeds this threshold, or the number of delays exceeds 10 times in a single day, the process is marked as a severely delayed process. The process problem diagnosis table uses a structured data format, including fields such as process number, process name, blockage type, average downtime, number of delays, and severity level, providing comprehensive data support for subsequent optimization decisions.

[0104] Specifically, the calculation of the theoretical number of equipment is based on the relationship between production capacity demand and the processing capacity of a single piece of equipment. The system first extracts the daily production capacity demand for each process from the production plan. For example, the drilling process needs to complete 1200 products. Based on the equipment model, the system queries the standard processing capacity of a single piece of equipment. A certain model of drilling machine can process 60 pieces per hour. Calculated based on a 20-hour workday, the daily production capacity of a single piece of equipment is 1200 pieces.

[0105] Preferably, the reordering of process execution takes into account both inter-process dependencies and the severity of delays. The system constructs a process priority scoring matrix, assigning dependency strength values ​​between 0 and 1 (1 for strong dependencies and 0.3 for weak dependencies); the normalized number of delays is used as another dimension for scoring. The two dimensions are weighted and summed to obtain a comprehensive priority score, with weights of 0.4 and 0.6 respectively. Processes with higher scores are given earlier execution positions, but must meet the hard constraints of the process flow, such as heat treatment must be performed after machining. In this way, process requirements are guaranteed while bottleneck processes are prioritized.

[0106] In one possible implementation, the equipment relocation schedule requires precise calculation of the time required for each step. The standard relocation time is determined based on factors such as equipment model, weight, and relocation distance. For example, relocating a large punch press requires 8 hours, including 2 hours for disassembly, 3 hours for transportation, and 3 hours for installation and commissioning. The system identifies downtime maintenance windows from the production plan, typically scheduled for weekends or night shift handover periods. Equipment requiring relocation is prioritized according to urgency, with priority given to equipment having the greatest impact on production capacity. The relocation plan for each piece of equipment includes detailed information such as the start time of disassembly, estimated completion time, responsible shift, and a list of required tools. The adjustment plan is considered successfully implemented when actual production capacity remains within this range.

[0107] Understandably, the development of a complete implementation plan is a system integration process. The plan document includes an execution summary, a detailed equipment allocation list, instructions for adjusting process sequences, a Gantt chart of the timeline, and contingency plans for handling risks. Each adjustment clearly defines the responsible department and completion deadline to ensure the plan's feasibility. Furthermore, the system establishes an implementation effectiveness tracking mechanism, daily tracking of key indicators such as actual capacity, equipment utilization rate, and number of delays for each process. This data is compared and analyzed with the planned targets to promptly identify deviations and implement corrective measures to effectively improve the overall efficiency of the production line.

[0108] Step S107: Obtain the change data of the total number of workpieces completed in each process from the dynamic optimization implementation plan, determine the degree of improvement in system throughput, and determine the process load balance status of workpiece flow.

[0109] The total number of completed workpieces for each process before and after implementation is obtained from the execution records of the dynamic optimization implementation plan. The change rate is obtained by subtracting the pre-implementation value from the post-implementation value and then dividing by the pre-implementation value. The total number of workpieces passing through the entire production line per unit time is used as the system throughput. The improvement degree is obtained by subtracting the pre-implementation throughput from the post-implementation throughput and then dividing by the pre-implementation throughput. Based on the improvement degree and the change rate of completed workpieces for each process, the load coefficient of each process is calculated. The load coefficient is the ratio of the actual completed quantity of the process to its rated capacity. The standard deviation of the load coefficients of all processes is calculated. When the standard deviation is lower than the preset equilibrium threshold, it indicates that the load distribution of each process is uniform. Based on the load coefficient distribution, processes with load coefficients greater than the preset upper limit are marked as overloaded, and those with load coefficients less than the preset lower limit are marked as idle. The proportion of overloaded and idle processes to the total number of processes is calculated. When this proportion is lower than the preset standard, the process load balance of workpiece flow is determined to be good; otherwise, it is an unbalanced state.

[0110] In one embodiment, the system extracts historical production records for each process from the database of the dynamic optimization implementation scheme. The data collection period before implementation is the average value of the month prior to optimization, and the data after implementation is the actual value of the first week after optimization. For example, a drilling process had an average daily output of 800 pieces before implementation and an average daily output of 960 pieces after implementation, with a change rate calculated as (960-800) / 800×100%=20%. The system throughput is obtained by statistically analyzing the output of the exit process of the entire production line; before implementation, it was 150 pieces per hour, and after implementation, it was 180 pieces per hour, representing an increase of 20%.

[0111] It should be noted that the load factor reflects the actual load level of the process. Each process has a designed rated capacity; for example, the rated capacity of a milling process is 50 pieces per hour. When this process actually completes 45 pieces, the load factor is 45 / 50 = 0.9. After calculating the load factors for all processes, the system uses the standard deviation formula to evaluate the data dispersion. The smaller the standard deviation, the closer the loads of each process are, and the more balanced the production line. The preset balance threshold is usually set at 0.15; when the standard deviation is below this value, the load distribution is considered uniform.

[0112] Specifically, the determination of overload and idle states is based on the division of load coefficient ranges. Processes with a load coefficient greater than 0.95 are in an overload state, indicating that the process is operating close to full capacity and is likely to become a production bottleneck; processes with a load coefficient less than 0.6 are in an idle state, resulting in wasted capacity. The system counted 2 overloaded processes, 1 idle process, and a total of 10 processes, with abnormal processes accounting for 30%.

[0113] Preferably, a comprehensive evaluation method is used to determine the process load balance. The preset standard is typically that the proportion of abnormal processes does not exceed 20%. If the current proportion exceeds 30%, the system determines it to be in an unbalanced state. This imbalance will lead to inconsistent production rhythms, increased waiting time for some processes, and a decrease in overall efficiency.

[0114] In one possible implementation, the system would also analyze the specific causes of the imbalance. Overloaded processes might require additional equipment or optimized process parameters, while idle processes could consider taking on other production tasks or reducing equipment configuration. Through this dynamic monitoring and evaluation mechanism, production management can promptly identify problems and take corrective measures to maintain the efficient and stable operation of the production line.

[0115] This invention also provides a data feedback task flow dynamic optimization system, mainly comprising: a feedback data acquisition and processing capability identification module, used to acquire process feedback data, determine process execution time and processing results through production monitoring system logs, identify the current processing capability of the process, and simultaneously acquire the number of pending workpieces at downstream assembly nodes and the frequency fluctuation amplitude of output workpieces from upstream processes; a load assessment and congestion identification module, used to limit workpiece flow if the number of pending workpieces at downstream assembly nodes exceeds a preset assembly threshold, based on the process load change trend recorded by equipment utilization and capacity occupancy ratio, and assess the degree of disorder in the output rhythm of upstream processes based on the frequency fluctuation amplitude of output workpieces, identifying a list of potential process congestion locations and the number of queued workpieces for each process; and a key process analysis and equipment allocation adjustment module, used to extract key process identifiers and congestion types from the list of potential process congestion locations, acquire real-time load data of equipment and real-time capacity load data for each process through the production scheduling center, assess the current equipment occupancy ratio of each process in conjunction with the number of queued workpieces, and adjust the equipment allocation. The system comprises the following modules: an equipment allocation scheme evaluation and process sequence rearrangement module, used to identify the proportion of process equipment and the corresponding total number of completed workpieces in the equipment allocation scheme; if the total number of completed workpieces corresponding to the proportion of process equipment shows a downward trend, the process execution order is rearranged according to workpiece priority and process dependency; a processing capacity matching degree analysis module, used to obtain the number of queued workpieces and real-time load of each process corresponding to the rearranged process execution order, analyze to obtain the process processing capacity matching degree index, and determine the global workpiece flow stability assessment result; a dynamic optimization scheme generation module, used to re-identify the equipment allocation ratio and process execution order through the production scheduling center, combine key process identifiers, blockage type information, and global workpiece flow stability assessment results, determine the dynamic optimization implementation scheme, and maintain the process processing throughput; and a throughput assessment and load balancing judgment module, used to obtain the change data of the total number of completed workpieces in each process from the dynamic optimization implementation scheme, judge the degree of improvement in system processing throughput, and determine the process load balancing state of workpiece flow. The above description is only a preferred embodiment of this application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to the technical solutions formed by specific combinations of the above-mentioned technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-mentioned technical features or their equivalent features without departing from the concept of this application. For example, technical solutions formed by substituting the above-mentioned features with technical features disclosed in this application (but not limited to) that have similar functions.

Claims

1. A method for dynamic optimization of task flow based on data feedback, characterized in that, The method includes: acquiring process feedback data, the quantity of workpieces to be processed, and the frequency fluctuation amplitude of output workpieces. The process feedback data includes equipment processing time, workpiece completion status, and equipment usage frequency. The quantity of workpieces to be processed represents the workpiece accumulation at downstream assembly nodes, and the frequency fluctuation amplitude of output workpieces represents changes in the output rhythm of upstream processes. The method also involves determining the process load change trend based on equipment utilization and capacity occupancy ratios, adjusting workpiece flow based on a comparison between the quantity of workpieces to be processed and a preset assembly threshold, and identifying a potential process blockage location list and queued workpiece quantities using the frequency fluctuation amplitude of output workpieces. Finally, the method extracts key process identifiers and blockage types from the potential process blockage location list, and combines this information with real-time equipment load data and real-time capacity load data. The data and the queued workpiece quantity are used to adjust the equipment allocation scheme, wherein the equipment allocation scheme includes equipment number allocation and capacity allocation; based on the process equipment ratio and total number of completed workpieces in the equipment allocation scheme, the process processing execution order is rearranged in combination with workpiece priority and process dependency; based on the process processing execution order, the queued workpiece quantity and real-time load are obtained, and the process processing capacity matching degree index and global workpiece flow stability assessment result are determined; based on the key process identifier, blockage type and global workpiece flow stability assessment result, the equipment allocation scheme and process processing execution order are adjusted to determine the dynamic optimization implementation scheme; based on the dynamic optimization implementation scheme, the change data of the total number of completed workpieces is obtained to determine the process load balancing state.

2. The task flow dynamic optimization method based on data feedback according to claim 1, characterized in that, The acquisition of process feedback data, the quantity of workpieces to be processed, and the frequency fluctuation range of output workpieces includes: extracting the operation logs of each process equipment from the production monitoring system; calculating the equipment processing time and workpiece dwell time based on the timestamps in the operation logs; determining the process processing capacity coefficient in the process feedback data; filtering process nodes with low processing capacity based on the process processing capacity coefficient; counting the quantity of workpieces to be processed; monitoring the changes in the number of output workpieces from upstream processes; and determining the frequency fluctuation range of output workpieces based on the difference in the number of output workpieces in adjacent time periods.

3. The task flow dynamic optimization method based on data feedback according to claim 1, characterized in that, The process of determining the trend of process load change based on equipment utilization rate and capacity occupancy ratio, adjusting workpiece flow rate based on the comparison result of the number of workpieces to be processed and the preset assembly threshold, and identifying a list of potential process blockage locations and the number of queued workpieces by combining the fluctuation amplitude of the output workpiece frequency, includes: collecting the equipment utilization rate and capacity occupancy ratio of each process equipment to form a time series, calculating the rate of change based on the time series, and determining the trend of process load change; when the number of workpieces to be processed exceeds the preset assembly threshold, sending a flow restriction command to the upstream process to adjust the workpiece release frequency; calculating the coefficient of variation based on the output time interval of the upstream process after the flow restriction command to determine the process with disordered output rhythm, and generating a list of potential process blockage locations by combining the trend of process load change and the coefficient of variation, wherein the list of potential process blockage locations includes process number and number of queued workpieces.

4. The task flow dynamic optimization method based on data feedback according to claim 3, characterized in that, The step of calculating the coefficient of variation based on the output time interval of the upstream process after the flow restriction instruction to determine the process with disordered output rhythm includes: collecting the output time of the upstream process after the execution of the flow restriction instruction, calculating the coefficient of variation of adjacent output time interval sequences; marking the process with disordered rhythm when the coefficient of variation exceeds a preset threshold, counting the number of queued workpieces before the process with disordered rhythm, and generating a list containing process number and degree of congestion.

5. The task flow dynamic optimization method based on data feedback according to claim 1, characterized in that, The step of extracting key process identifiers and blockage types from the potential process blockage location list, and adjusting the equipment allocation scheme in conjunction with real-time equipment load data, real-time production capacity load data, and the number of queued workpieces, includes: extracting key process identifiers and blockage types from the potential process blockage location list; obtaining the real-time equipment load data and real-time production capacity load data; calculating the ratio of the number of queued workpieces to the buffer capacity as the queue saturation; determining a resource tension score by weighted summation of the real-time equipment load data, real-time production capacity load data, and queue saturation; and adjusting the number of equipment and the upper limit of production capacity based on the resource tension score and blockage type to form the equipment allocation scheme.

6. The task flow dynamic optimization method with data feedback according to claim 1, characterized in that, The step of rearranging the process execution order based on the process equipment ratio and total number of completed workpieces in the equipment allocation scheme, combined with workpiece priority and process dependency, includes: extracting the process equipment ratio from the equipment allocation scheme, calculating the total number of completed workpieces corresponding to the process equipment ratio, and determining the processes with a downward trend based on the time series slope; extracting workpiece priority values ​​and process dependencies based on the processes with a downward trend, constructing a directed graph, generating a feasible process sequence based on topological sorting, and selecting the sequence with the smallest comprehensive evaluation value as the process execution order.

7. The task flow dynamic optimization method based on data feedback according to claim 1, characterized in that, The step of obtaining the queued workpiece quantity and real-time load based on the process execution order, and determining the process processing capacity matching index and the global workpiece flow stability assessment result includes: obtaining the queued workpiece quantity and real-time load under the process execution order; determining the processing capacity value of each process based on the comparison between the actual processing rate and the standard processing rate; calculating the capacity difference between adjacent processes based on the processing capacity value; statistically analyzing the mismatch process ratio; and combining the buffer occupancy rate for weighted calculation to determine the global workpiece flow stability assessment result.

8. The task flow dynamic optimization method with data feedback according to claim 1, characterized in that, The process of adjusting the equipment allocation scheme and process execution order based on the key process identifier, blockage type, and global workpiece flow stability assessment results to determine the dynamic optimization implementation scheme includes: obtaining the current equipment allocation ratio and the process execution order; extracting the process information corresponding to the key process identifier; identifying capacity bottleneck processes and resource surplus processes in combination with the blockage type and global workpiece flow stability assessment results; and adjusting the equipment allocation ratio and process execution order based on the capacity bottleneck processes and resource surplus processes using a linear programming method to form the dynamic optimization implementation scheme. The dynamic optimization implementation scheme includes the number of equipment adjustments and the location of execution order adjustments. Specifically, the process of adjusting the equipment allocation ratio and process execution order based on the capacity bottleneck processes and resource surplus processes using a linear programming method to form the dynamic optimization implementation scheme includes: extracting the blockage type and workpiece stagnation time corresponding to the key process identifier; counting the number of transmission delays; marking processes with severe delays; and determining the number of equipment gaps. Based on the number of equipment gaps and process dependencies, the process execution order is adjusted, and equipment transfer paths and relocation schedules are formulated to form the dynamic optimization implementation scheme that includes equipment allocation arrangements and process priority reordering.

9. The task flow dynamic optimization method for data feedback according to claim 1, characterized in that, The step of obtaining the change data of the total number of completed workpieces according to the dynamic optimization implementation scheme and determining the process load balance status includes: extracting the change data of the total number of completed workpieces from the dynamic optimization implementation scheme, calculating the change rate and system processing throughput; calculating the load coefficient of each process based on the change rate, calculating the standard deviation of the load coefficient, marking overloaded and idle processes based on the load coefficient distribution, and determining the process load balance status.

10. A data feedback task flow dynamic optimization system, characterized in that, The system includes: a feedback data acquisition and processing capability identification module, used to acquire process feedback data, the quantity of workpieces to be processed, and the frequency fluctuation amplitude of output workpieces. The process feedback data includes equipment processing time, workpiece completion status, and equipment usage frequency. The quantity of workpieces to be processed represents the workpiece accumulation at downstream assembly nodes, and the frequency fluctuation amplitude of output workpieces represents changes in the output rhythm of upstream processes. A load assessment and congestion identification module is used to determine the process load change trend based on equipment utilization and capacity occupancy ratio, adjust workpiece flow based on the comparison result of the quantity of workpieces to be processed and a preset assembly threshold, and identify a potential process congestion location list and queued workpiece quantity based on the frequency fluctuation amplitude of output workpieces. A key process analysis and equipment allocation adjustment module is used to extract key process identifiers and congestion types from the potential process congestion location list, and combine this information with real-time equipment load data, real-time capacity load data, and the queued workpiece quantity. The system includes a workpiece quantity adjustment equipment allocation scheme, comprising equipment number allocation and capacity allocation; an equipment allocation scheme evaluation and process sequence rearrangement module, used to rearrange the process execution order based on the process equipment ratio and total number of completed workpieces in the equipment allocation scheme, combined with workpiece priority and process dependency; a processing capacity matching degree analysis module, used to obtain the queued workpiece quantity and real-time load based on the process execution order, and determine the process processing capacity matching degree index and global workpiece flow stability assessment result; a dynamic optimization scheme generation module, used to adjust the equipment allocation scheme and process execution order based on the key process identifier, blockage type and global workpiece flow stability assessment result, and determine the dynamic optimization implementation scheme; and a throughput assessment and load balancing judgment module, used to obtain the total number of completed workpieces change data based on the dynamic optimization implementation scheme, and determine the process load balancing status.

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