Production scheduling optimization system based on data analysis

Abstract

The invention relates to the technical field of production management, in particular to a production scheduling optimization system based on data analysis, which comprises a process data trend analysis module, a station resource real-time acquisition module, a scheduling sequence optimization judgment module, a rhythm coordination check module and a scheduling dynamic optimization module. According to the method, the process duration trend is analyzed in real time, continuous growth is accurately recognized, rhythm parameters are dynamically corrected, the stable process rhythm is ensured, efficient resource connection is achieved based on the station task completion time and the matching task and station idle starting time, the task sequence is dynamically adjusted according to the station idle state, and task connection is optimized; the beat proportion of adjacent tasks is verified, the task interval is adjusted, the beat difference is controlled within a reasonable range, beat sudden change is prevented from interfering with production, the rapid response ability to production changes is integrally enhanced, task and resource collaborative optimization is achieved, the overall scheduling efficiency and beat consistency are improved, and the production continuity and the resource utilization rate are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of production management, and particularly to a production scheduling optimization system based on data analysis. Background Art

[0002] The technical field of production management includes aspects such as production resource allocation, manufacturing process control, production plan formulation, and scheduling optimization. The core lies in effectively coordinating and managing each link in the production activities to improve production efficiency, reduce costs, and ensure product quality. It comprehensively covers the integrated management of elements such as materials, personnel, equipment, and time, dynamically monitors and adjusts the entire production process through scientific management methods, enables optimal allocation of various production resources, and thus meets market demands and improves the overall operation level.

[0003] Among them, the production scheduling optimization system based on data analysis refers to establishing an optimization strategy system for the production operation sequence through the collection, collation, and analysis of relevant data in the production process. For technical matters such as the reasonable arrangement of the operation sequence, the balanced allocation of resource usage, and the dynamic adjustment of production capacity in the production link, specifically, based on the real-time collected production data, it models the time required for operation processes and the resource occupancy situation through statistical analysis methods, and then uses operation means such as linear programming or integer programming to optimize the scheduling sequence and form an adjustment plan. Usually, it relies on the cooperation of three links: production data recording, data analysis, and optimization operation to complete the scheduling optimization task.

[0004] In the existing production scheduling management process, there is a lack of dynamic response ability to the real-time state of the production process, the changing trend of operation duration cannot be effectively tracked, the adjustment of beat parameters lags behind, resulting in the failure to timely correct the beat fluctuations in the production process, and frequent backlogs or delays. In terms of resource allocation, the station utilization rate depends on fixed configuration and is not optimized in combination with the real-time task completion situation. The station idle state is not timely identified and utilized, and the resource scheduling efficiency is relatively low. The task sorting is based on the initial plan, lacking a dynamic adjustment mechanism for the task execution time point and the actual idle state of the station, and frequent task waiting and conflicts occur. In terms of beat coordination, there is a lack of proportional verification mechanism, unable to timely correct the process interruption caused by the imbalance of beat ratio, resulting in the disorder of the production line rhythm. For example, the task interval setting that has not been adjusted for a long time causes the inability to immediately enter the next process after some processes are completed, resulting in the accumulation of waiting time and dragging down the overall production rhythm. This makes the production system difficult to efficiently respond to changing conditions, affecting the resource utilization efficiency and the coherence of task execution. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies existing in the prior art and propose a production scheduling optimization system based on data analysis.

[0006] To achieve the above object, the present invention adopts the following technical solutions: The production scheduling optimization system based on data analysis includes:

[0007] The process data trend analysis module obtains continuous process duration record data, constructs a duration increase sequence to perform symbol consistency judgment. If the symbols are all positive, calculate the deviation between the latest process duration and the current scheduling beat number. If it is greater than the beat time deviation threshold, record and generate a process beat adjustment parameter set;

[0008] The station resource real-time acquisition module collects the task completion time of each current station, records the starting time of station idle, retrieves the target station data of the current scheduling task, performs task-station-idle time pairing, and generates a station idle real-time data set;

[0009] The scheduling order optimization judgment module compares the starting time of station idle of the current task and the next task target station according to the station idle real-time data set, judges whether there is a need to swap the task order, performs an adjustment operation to update the scheduling task list, and generates a scheduling order optimization result;

[0010] The beat coordination inspection module judges the proportional relationship of the beat numbers of adjacent tasks in the scheduling task list based on the scheduling order optimization result and the process beat adjustment parameter set. If the adjacent task beat ratio is greater than the adjacent beat ratio tolerance, perform a shortening operation on the task interval time, and record and generate beat coordination correction data.

[0011] As a further solution of the present invention, the process beat adjustment parameter set includes a duration increase determination value, a beat time deviation amount, and a beat adjustment suggestion value. The station idle real-time data set includes a station number, an idle starting time, and a task matching degree. The scheduling order optimization result includes a task swap record, an updated task order list, and an adjusted scheduling priority. The beat coordination correction data includes a beat ratio correction value, a task interval adjustment amount, and a beat coordination flag.

[0012] As a further solution of the present invention, the process data trend analysis module includes:

[0013] The duration difference extraction sub-module, based on the obtained continuous process duration record data, arranges them in chronological order, performs difference calculation on any adjacent process durations, obtains a first-order difference sequence of adjacent process times, calculates the absolute value of all differences in the duration difference sequence, and obtains a duration difference sequence absolute value set;

[0014] The symbol consistency judgment sub-module, according to the duration difference sequence absolute value set, performs symbol consistency judgment on any continuous differences in the sequence, screens out the difference combination intervals where the symbols are all positive, extracts the latest process duration and the current scheduling beat number based on the screened intervals, and calculates and obtains the process duration beat deviation value;

[0015] The beat adjustment parameter generation sub-module compares the process duration beat deviation value with the beat time deviation threshold. If the deviation is greater than the beat time deviation threshold, it extracts the current scheduling beat number, the latest process duration, and the beat time deviation threshold, calculates the beat adjustment reference value, and establishes a set of beat adjustment parameters.

[0016] As a further aspect of the present invention, the station resource real-time acquisition module includes:

[0017] The task completion acquisition sub-module acquires the task completion time of each station currently, records the system clock time when the task corresponding to each station ends, and generates a set of station task completion times;

[0018] The idle time recording sub-module, based on the set of station task completion times, records the system clock time after the task completion of each station as the idle start time, and corresponds it to the station identifier to establish a station idle start time value;

[0019] The station data pairing sub-module, according to the station idle start time value, performs task and station pairing according to the target station data of the current scheduling task, combines the target station number of each task with the station idle start time, integrates the task number, station number, and idle start time, and obtains a set of real-time station idle data.

[0020] As a further aspect of the present invention, the scheduling order optimization judgment module includes:

[0021] The idle time comparison sub-module, according to the set of real-time station idle data, obtains the target station idle start times of the current task and the next task, performs a time sequence comparison according to the target station idle start time of the current task and the target station idle start time of the next task, and obtains the difference in station idle time;

[0022] The sequence interchange determination sub-module, based on the difference in station idle time, determines whether the target station idle start time of the next task is earlier than the target station idle start time of the current task. If the judgment result is yes, it records the requirement for sequence interchange between the current task and the next task and obtains a task sequence interchange flag;

[0023] The scheduling list update sub-module performs a sequence adjustment operation on the scheduling task list according to the task sequence interchange flag. After adjusting the task sequence, it updates the scheduling task list to generate an optimized scheduling order result.

[0024] As a further aspect of the present invention, the beat coordination inspection module includes:

[0025] The beat ratio judgment sub-module extracts the beat numbers of adjacent tasks in the scheduling task list based on the scheduling order optimization result and the process beat adjustment parameter set, judges the proportional relationship of the adjacent task beat numbers, calls the adjacent beat ratio tolerance, calculates the adjacent task beat ratio, compares it with the beat ratio tolerance, and obtains the beat ratio overrun mark;

[0026] The task interval adjustment sub-module shortens the task interval time between adjacent tasks marked as overrun according to the beat ratio overrun mark. After shortening the task interval time, the obtained task interval adjustment value is recorded;

[0027] The configuration record generation sub-module integrates the adjusted task interval time and the corresponding task numbers according to the task interval adjustment value, establishes a complete task interval configuration record, and generates the beat coordination correction data.

[0028] As a further solution of the present invention, the system further includes:

[0029] The scheduling dynamic optimization module integrally integrates the scheduling task data according to the beat coordination correction data, updates the execution period and the corresponding station configuration of each production task, constructs a globally optimized scheduling time table and resource allocation table, and generates the production scheduling dynamic optimization result;

[0030] The production scheduling dynamic optimization result includes an optimized scheduling time table, a resource allocation table, and a task execution period configuration.

[0031] As a further solution of the present invention, the scheduling dynamic optimization module includes:

[0032] The task data integration sub-module obtains the beat coordination correction data, performs an overall integration operation on the scheduling task data, extracts the beat information, station configuration, and adjusted task interval corresponding to each task, integrates them into a unified scheduling task set, and generates the scheduling task integration data;

[0033] The execution period update sub-module updates the execution period of each production task based on the scheduling task integration data, adjusts the start and end times of each task according to the task order and the beat coordination correction data, and obtains the task execution period configuration data;

[0034] The resource table generation sub-module establishes a globally optimized scheduling time table and resource allocation table according to the task execution period configuration data, combined with the station number and resource allocation requirements of each task, and generates the production scheduling dynamic optimization result.

[0035] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0036] In the present invention, by analyzing the trend of process duration in real time, continuously growing processes are accurately identified, the beat parameters are dynamically corrected to ensure stable process rhythm, local blockages are avoided, the starting time of the idle state of the station is matched based on the completion time of the station tasks, efficient resource connection is achieved, idling is reduced, the task order is dynamically adjusted according to the idle state of the station, task connection is optimized, and the scheduling flexibility is improved. The beat ratio of adjacent tasks is verified, the task interval is adjusted, the beat difference is controlled within a reasonable range, and sudden beat changes are prevented from interfering with production. The overall rapid response ability to production changes is enhanced, the coordination optimization of tasks and resources is achieved, the overall scheduling efficiency and beat consistency are improved, and the production continuity and resource utilization rate are enhanced. Description of the Drawings

[0037] Figure 1 is the system flow chart of the present invention;

[0038] Figure 2 is the flow chart of the process data trend analysis module of the present invention;

[0039] Figure 3 is the flow chart of the real-time acquisition module of station resources of the present invention;

[0040] Figure 4 is the flow chart of the scheduling order optimization judgment module of the present invention;

[0041] Figure 5 is the flow chart of the beat coordination verification module of the present invention;

[0042] Figure 6 is the flow chart of the scheduling dynamic optimization module of the present invention. Detailed Embodiment

[0043] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0044] Please refer to Figure 1 , the production scheduling optimization system based on data analysis includes:

[0045] The process data trend analysis module obtains the continuous process duration record data, arranges it in chronological order, calculates the difference between any adjacent durations to construct a duration increase sequence (i.e., the first-order difference sequence of adjacent process times), performs a sign consistency judgment based on any continuous differences in the sequence. If the signs are all positive, the deviation between the latest process duration and the current scheduling beat number is calculated and compared with the beat time deviation threshold (the maximum time deviation allowed for the production beat). If the deviation is greater than the threshold, a process beat adjustment parameter set is recorded and generated;

[0046] The station resource real-time acquisition module collects the completion time of tasks at each station currently, records the system clock time as the starting time of station idle, retrieves the data of the target station of the current scheduling task, performs task-station-idle time pairing, and generates a real-time dataset of station idle time;

[0047] The scheduling order optimization judgment module compares the starting time of station idle of the current task and the next task according to the real-time dataset of station idle. If the starting time of station idle of the next task is earlier than that of the current task, it records the requirement for swapping the order of the two tasks, performs the order adjustment operation, updates the scheduling task list, and generates the scheduling order optimization result;

[0048] The beat coordination inspection module judges the proportional relationship of the beat numbers of adjacent tasks in the scheduling task list based on the scheduling order optimization result and the process beat adjustment parameter set. If the beat ratio of adjacent tasks is greater than the adjacent beat ratio tolerance (obtained by calculating the production line balance rate or statistically analyzing the historical beat synchronization data, representing the maximum allowable ratio of the beat difference between adjacent processes, such as the common 20%-30% tolerance range in industry standards), it shortens the task interval time, records the adjusted task interval configuration, and generates the beat coordination correction data;

[0049] The scheduling dynamic optimization module integrally integrates the scheduling task data according to the beat coordination correction data, updates the execution period of each production task and the corresponding station configuration, constructs a globally optimized scheduling time table and resource allocation table, and generates the production scheduling dynamic optimization result.

[0050] The process beat adjustment parameter set includes the duration increase determination value, the beat time deviation amount, and the beat adjustment suggestion value. The real-time dataset of station idle includes the station number, the starting time of idle, and the task matching degree. The scheduling order optimization result includes the task swap record, the updated task order list, and the adjusted scheduling priority. The beat coordination correction data includes the beat ratio correction value, the task interval adjustment amount, and the beat coordination flag. The production scheduling dynamic optimization result includes the optimized scheduling time table, the resource allocation table, and the task execution period configuration.

[0051] Please refer to Figure 2 , the process data trend analysis module includes:

[0052] The duration difference extraction sub-module, based on the obtained continuous process duration record data, arranges them in chronological order, performs difference calculation on the duration of any adjacent processes, obtains the first-order difference sequence of adjacent process times, calculates the absolute value of all differences in the duration difference sequence, and obtains the set of absolute values of the duration difference sequence;

[0053] Based on obtaining the recorded data of the continuous process duration, it is necessary to first collect the original records of the process execution time. This data can be obtained through the timestamp records of the industrial control system. For example, the start and end times of each process on the production line are 10:00 - 10:05, 10:06 - 10:12, 10:13 - 10:20, etc., and the corresponding process durations are 5 minutes, 6 minutes, 7 minutes respectively. This data is arranged in chronological order to ensure the consistency of the time series of adjacent processes. Through the above time difference calculation, the duration differences between adjacent processes are 1 minute and 1 minute in sequence. Further, take the absolute value of all adjacent process duration differences, and the set of absolute values of the difference sequence is {1, 1}. Specifically in implementation, first extract the durations of the 1st and 2nd processes, execute |6 - 5| = 1, extract the durations of the 2nd and 3rd processes, execute |7 - 6| = 1, form a complete duration difference sequence, and then generate the set of absolute values of the duration difference sequence.

[0054] The symbol consistency judgment sub-module, according to the set of absolute values of the duration difference sequence, performs symbol consistency judgment on any continuous differences in the sequence, screens the intervals of difference combinations where the symbols are all positive, and based on the screened intervals, extracts the latest process duration and the current scheduling beat number, and calculates to obtain the process duration beat deviation value;

[0055] According to the set of absolute values of the duration difference sequence, perform symbol judgment on the continuous differences in the difference sequence. Specifically, detect the positive and negative signs of each difference in turn. If all differences in the sequence are positive, then the symbol consistency condition is satisfied. For example, the difference sequence is {1, 1}, and its symbols are all positive, meeting the consistency requirement. Based on this result, extract the latest process duration of 7 minutes and the current scheduling beat number of 6 minutes, perform difference operation, 7 - 6 = 1 minute, and obtain the process duration beat deviation value of 1 minute.

[0056] The beat adjustment parameter generation sub-module compares the process duration beat deviation value with the beat time deviation threshold. If the deviation is greater than the beat time deviation threshold, then extract the current scheduling beat number, the latest process duration, and the beat time deviation threshold, and use the formula:

[0057]

[0058] Calculate to obtain the beat adjustment reference value P a and establish a set of beat adjustment parameters. Among them, T n represents the latest process duration, S p represents the current scheduling beat number, D i represents the i-th difference in the set of absolute values of the duration difference sequence, and N represents the number of differences in the symbol consistency interval of the difference sequence;

[0059] According to the deviation value of the process duration rhythm, the rhythm time deviation threshold is set to 0.8 minutes. This threshold is set based on the standard tolerance interval of the production rhythm, usually determined according to the equipment operation stability and the maximum allowable time floating range between processes. Specifically, referring to the principle that the rated rhythm cycle of this production line fluctuates no more than 10%, and the current scheduled rhythm number is 6 minutes, then the maximum allowable deviation is 6×10% = 0.6 minutes. Considering the influence of equipment switching time and manual operation differences between processes, the tolerance is adjusted and relaxed to 13%, that is, 6×13% = 0.78 minutes, and rounded up to 0.8 minutes. This threshold changes with the adjustment of the scheduled rhythm number. The higher the scheduled rhythm number, the greater the allowable time deviation, and vice versa, ensuring the flexibility and precision matching of rhythm control. Comparing 1 minute with 0.8 minutes, 1>0.8, meeting the condition that the deviation is greater than the threshold, and extracting the current scheduled rhythm number S p = 6 minutes, the latest process duration T n = 7 minutes, the difference sets D1 = 1, D2 = 1, substituting into the formula:

[0060]

[0061] Performing operations to obtain the rhythm adjustment reference value P a Approximately 0.832 minutes, combining the current scheduled rhythm number 6 minutes and P a , establishing a rhythm adjustment parameter set to dynamically reflect the change range of the process rhythm. As shown in Table 1:

[0062] Table 1: Table of parameters related to rhythm adjustment

[0063] Current scheduling beat count Latest process duration <![CDATA[Difference D1]]> <![CDATA[Difference D2]]> Beat adjustment reference value 6 minutes 7 minutes 1 minute 1 minute 0.832 minute

[0064] The operation logic of the formula is to comprehensively reflect the comprehensive influence of the process duration change on the rhythm adjustment through multi-level numerical operation relationships. First, the T n -S p part represents the direct deviation between the latest process duration and the current scheduled rhythm number. This deviation value is used to measure the initial difference between the actual execution of the process and the planned rhythm. Subsequently, it is added to the cumulative sum of consecutive differences , reflecting the cumulative effect of the duration increase between historical consecutive processes, so that the current deviation not only reflects the single-process difference but also considers the superimposed influence of trend changes. The overall deviation sum is then compared with Normalization is performed. Through square root operation, the order of magnitude of the latest process duration and the current scheduling beat count is scaled to avoid abnormal fluctuations caused by numerical amplification. At the same time, the stability of the beat adjustment reference value and the consistency of the relative scale are improved. Finally, through absolute value operation, it is ensured that the deviation amount is positive, which is conducive to subsequent judgment and application. Therefore, through the accumulation of deviation influence, square root normalization scaling, and absolute value standardization processing, the beat adjustment reference value has the comprehensive reflection ability of actual deviation, historical trend, and beat magnitude.

[0065] Please refer to Figure 3 , the station resource real-time acquisition module includes:

[0066] The task completion acquisition sub-module acquires the task completion time of each station currently, records the system clock time corresponding to the end of the task for each station, and generates a set of station task completion times;

[0067] Acquire the task completion time of each station currently, and record the system clock time as the starting time of station idle. First, for each station, based on the station number and task assignment information, monitor its task execution status in real time. When it is detected that the task status changes from "executing" to "completed", immediately read the current system clock time, which is the task completion time. For example, station A completes the task at 10:15:30, record this time point. Next, store this completion time in the station task record table, along with associated information such as station number and task number. At the same time, for other stations such as station B and station C in the same time period, record their corresponding task completion times respectively. For example, station B is 10:17:45 and station C is 10:20:10. Then, classify and summarize the collected completion time set, arrange it in ascending order according to the station number to form a complete time record sequence, and check whether there is missing data. If there is, re-acquisition is required. Assume the collected data is station A: 10:15:30, station B: 10:17:45, station C: 10:20:10, as shown in Table 2. Table 2 lists the acquisition results of station task completion times. Further, for station A, record 10:15:30 as its idle starting time, and so on for other stations. This process needs to consider the system clock accuracy at the millisecond level to avoid recording errors caused by insufficient time accuracy. Finally, obtain the set of station task completion times.

[0068] Table 2: Station Task Completion Time Table

[0069] Station number Task number Completion time A T001 10：15：30 B T002 10：17：45 C T003 10：20：10

[0070] The idle time record sub-module, based on the set of station task completion times, records the system clock time after the task completion for each station as the starting time of idle and corresponds it to the station identifier to establish the value of the starting time of station idle;

[0071] Based on the set of task completion times for each work station, record the system clock time after task completion for each work station as the idle start time, and associate it with the work station identifier. First, call the set of task completion times for the work stations, extract the completion time data for each work station, and sequentially read the completion times of work station a, work station b, and work station C according to the work station number, which are 10:15:30, 10:17:45, and 10:20:10 respectively. Assign them as the idle start times respectively. Then, perform format standardization on the idle start times to ensure that the time format is in 24-hour format HH:MM:SS. For example, if the recorded time for a certain work station is 10:5:3, it is converted to 10:05:03. After that, pair the idle start times with the work station numbers to generate a key-value pair structure, such as {a: 10:15:30}, {b: 10:17:45}, {C: 10:20:10}, and establish a list or dictionary structure to uniformly manage this data set. Then, perform logical verification on the time data to confirm that the time sequence is reasonable and avoid incorrect pairing caused by data anomalies. For example, if the idle time of work station b is earlier than that of work station a, it is necessary to re-verify or collect. After completing the above operations, obtain the idle start time values of the work stations.

[0072] Work station data pairing sub-module, according to the idle start time value of the work station, pair the tasks executed according to the target work station data of the current scheduling task with the work station, combine the target work station number of each task with the idle start time of the work station, integrate the task number, work station number, and idle start time, and obtain the real-time data set of work station idle times;

[0073] According to the idle start time value of the work station, pair the tasks executed according to the target work station data of the current scheduling task with the work station, combine the target work station number of each task with the idle start time of the work station. First, obtain the current scheduling task data, read the corresponding relationship between the task number and the target work station number. For example, the target work station of task T004 is a, the target work station of task T005 is b, and the target work station of task T006 is c. Then, call the established idle start time value of the work station, such as work station a: 10:15:30, work station b: 10:17:45, work station c: 10:20:10, pair task T004 to work station a and assign the idle start time 10:15:30, pair task T005 to work station b and assign the idle start time 10:17:45, and pair task T006 to work station c in the same way. After completing the pairing, summarize the pairing results to form a triple set of task number, work station number, and idle start time, such as {T004, a, 10:15:30}, {T005, b, 10:17:45}, {T006, c, 10:20:10}. This set is the data set that reflects the current idle state of each task at each work station in real time. Finally, obtain the real-time data set of work station idle times.

[0074] Please refer toFigure 4 , the scheduling order optimization judgment module includes:

[0075] An idle time comparison sub-module, according to the real-time data set of station idle time, obtains the starting time of station idle for the current task and the next task. According to the starting time of station idle for the current task's target station and the starting time of station idle for the next task's target station, it performs a comparison of time sequence to obtain the difference in station idle time;

[0076] Obtain the starting time of station idle for the current task and the next task. Obtain the target station numbers for tasks with task numbers T1 and T2 in the current scheduling list, which are W1 and W2 respectively. Further extract the corresponding starting times of idle for W1 and W2 from the generated real-time data set of station idle time, which are S1 and S2 respectively. Perform a comparison operation on S1 and S2 to judge their time sequence relationship, refined to obtain the specific time values of S1 and S2. For example, S1 = 10:35:20, S2 = 10:28:45. Convert the time values to seconds, that is, S1 = 38120 seconds, S2 = 37725 seconds. By comparing the sizes of 38120 and 37725, it is determined that S2 is earlier than S1. During the execution of this comparison operation, attention should be paid to the standardization of time. All times need to be uniformly converted to seconds to avoid conversion errors between hours, minutes, and seconds. Here, the obtained difference in idle time ΔT = 38120 - 37725 = 395 seconds. This difference is the difference in station idle time. As shown in Table 3, the W2 station is idle 395 seconds earlier than the W1 station, indicating the possibility of optimizing the sorting.

[0077] Table 3: Table of starting times of station idle

[0078] Station number Station idle start time Station idle start time (seconds) W1 10：35：20 38120 W2 10：28：45 37725

[0079] As shown in Table 3, the idle times of W1 and W2 are listed. After calculating the standardized seconds, the corresponding time difference ΔT = 395 seconds is obtained.

[0080] A sequence swap determination sub-module, based on the difference in station idle time, judges whether the starting time of station idle for the next task is earlier than the starting time of station idle for the current task. If the judgment result is yes, it records the requirement for swapping the order of the current task and the next task and obtains a task order swap flag;

[0081] Based on the difference in the idle time of workstations, determine whether the start time of the idle time of the next task workstation is earlier than that of the current task workstation. Judge the need for swapping by comparing the sign of the idle time difference ΔT. If ΔT>0, it means that the idle time of the next task workstation is earlier than that of the current task workstation. Record the need for swapping the task order, and mark the current task T1 and the next task T2 as needing to adjust the order. Here, taking ΔT = 395 seconds as an example, judge that ΔT is positive, and determine that the order of T1 and T2 needs to be adjusted. On this basis, generate a mark M = 1, where M is the order swapping mark. M = 1 means swapping is required, and M = 0 means swapping is not required. This mark is used as the basis for scheduling adjustment. In the further judgment process, if ΔT is in different numerical intervals, the reference intervals can be set as 0 - 60 seconds, 60 - 300 seconds, and more than 300 seconds, corresponding to no need for swapping, optional swapping, and mandatory swapping respectively. In this embodiment, ΔT = 395 seconds, which belongs to the mandatory swapping interval, and obtain the task order swapping mark.

[0082] The scheduling list update sub-module adjusts the order of the scheduling task list according to the task order swapping mark. After adjusting the task order, update the scheduling task list to generate an optimized scheduling order result;

[0083] According to the task order swapping mark, perform an order adjustment operation on the scheduling task list. Extract the original scheduling task list {T1, T2, T3,..., Tn}. According to the swapping mark M = 1, swap T1 and T2, and update it to {T2, T1, T3,..., Tn}. During the update process, the task attributes and the corresponding target workstations need to be kept unchanged, and only their order in the task list is adjusted. Re-number and sort the new task list to form an updated scheduling task set, record the updated list and store it, and obtain the optimized scheduling order result.

[0084] Please refer to Figure 5 , the beat coordination inspection module includes:

[0085] The beat ratio judgment sub-module extracts the beat numbers of adjacent tasks in the scheduling task list based on the optimized scheduling order result and the process beat adjustment parameter set, and judges the proportional relationship of the beat numbers of adjacent tasks. Call the adjacent beat ratio tolerance to compare whether the adjacent task beat ratio is greater than the tolerance. Use the formula:

[0086]

[0087] Calculate the adjacent task beat ratio R gh , take R gh and compare it with the beat ratio tolerance T r to judge whether R gh is greater than T r , and obtain the beat ratio over-limit mark. Among them, S gRepresents the beat number of the g-th task in the scheduling task list, S h Represents the beat number of the h-th task;

[0088] To obtain the optimized result of the scheduling order and the set of process beat adjustment parameters, first extract the beat numbers of adjacent tasks from the scheduling task list, and set them as the beat number S of the g-th task g And the beat number S of the h-th task h , for example, the beat number of scheduling task 1 on a certain production line is 50 seconds, and the beat number of task 2 is 60 seconds. After extraction, they are S1 = 50 and S2 = 60 respectively. Subsequently, calculate the beat difference between the two. First, find the absolute value of the difference |S1 - S2| = |50 - 60| = 10, and then divide this difference by the latter beat number S2, that is Converted to a percentage to get 16.67%. Then, refer to the beat ratio tolerance T r For comparison. The setting of this beat ratio tolerance is based on the production line balance rate. This balance rate is obtained by statistically analyzing the average load rate of each workstation on the production line and the stability of the process beat time. Usually, during the evaluation of the operation stability of multiple production lines, the range of beat differences of each workstation under standard working conditions is used as the basis to select the maximum allowable beat difference ratio interval. This interval fluctuates non-linearly with the change of the number of processes. When the number of processes is between 10 and 20, its beat fluctuation tolerance is usually stable between 20% and 30%. Combining the synchronous efficiency of production equipment and the matching degree of transmission time, 25% is selected as the standard setting of the adjacent process beat ratio tolerance. Judge whether 16.67% is less than 25%. If it is less, no processing is required. If it is greater, it is marked as the beat ratio over-limit state. Then, taking task 3 and task 4 as examples, the beat numbers are 70 seconds and 50 seconds respectively. Calculate |70 - 50| = 20, and the ratio R gh Is That is 40%, exceeding the tolerance of 25%. Therefore, it is marked as over-limit. Apply this processing process to all adjacent tasks in turn. Through the calculation of the beat ratio of each pair of tasks and the tolerance comparison, obtain the beat ratio over-limit mark.

[0089] Formula The operation logic of is to evaluate the relative difference size by normalizing the difference between the beat numbers of adjacent tasks. First, S g And S h Respectively represent the beat numbers of two adjacent tasks, where |S g -S h | represents the absolute difference value between the two. The absolute value operation is used to eliminate the influence of the task order on the difference size, so that whether the former is greater than the latter or the latter is greater than the former, the difference value is positive, ensuring the generality of the calculation result. Secondly, the difference value and S hThe division operation is performed to convert the absolute difference into a proportional value relative to the number of beats of the latter term, achieving the standardization of the difference, enabling the comparison of differences between different beat number pairs on a unified scale. Furthermore, the standardized difference value is converted into a percentage form by multiplying by 100%, which is convenient for comparison with the preset beat ratio tolerance T r Direct comparison is carried out. Therefore, this formula comprehensively utilizes the logic of using the absolute value to eliminate the influence of the sign, performing proportional operations to normalize the differences, and the percentage form being convenient for threshold judgment to fully reflect the measurement method of the coordination of adjacent task beats.

[0090] Table 4: Beat Ratio and Tolerance Judgment Table

[0091]

[0092] As shown in Table 4, the beat ratio between adjacent tasks is judged by the tolerance. Task 3 - 4 is marked as overlimit because the beat ratio exceeds 25%.

[0093] The task interval adjustment sub-module shortens the task interval time between adjacent tasks marked as overlimit according to the beat ratio overlimit mark. After shortening the task interval time, the obtained task interval adjustment value is recorded;

[0094] According to the beat ratio overlimit mark, interval adjustment is performed on the marked task pairs. First, the original interval time of Task 3 and Task 4 is read. Assuming the original interval is 30 seconds, the shortening operation is performed according to the preset shortening ratio. Let the shortening ratio be 20%, then the adjusted interval time is 30×(1 - 0.2) = 24 seconds. This result is recorded. The new interval of Task 3 - 4 is 24 seconds. The interval times of Task 1 - 2 and Task 5 - 6 remain unchanged because they are not overlimit. This processing logic is repeated for all marked task pairs to obtain the task interval adjustment value.

[0095] Table 5: Task Interval Adjustment Table

[0096]

[0097] Referring to Table 5, Task 3 - 4 has been adjusted by shortening, and the adjusted interval is 24 seconds.

[0098] The configuration record generation sub-module integrates the adjusted task interval time and the corresponding task numbers according to the task interval adjustment value, establishes a complete task interval configuration record, and generates the beat coordination correction data;

[0099] Adjust the value according to the task interval, integrate all task pairs and their corresponding adjusted intervals into a configuration record. Tasks 1-2 and 5-6 maintain the original intervals, and task 3-4 uses the adjusted interval of 24 seconds. Based on this, establish a data table corresponding to the task number pairs and interval times to form a task interval configuration record, and finally generate the beat coordination correction data.

[0100] Please refer to Figure 6 , the scheduling dynamic optimization module includes:

[0101] The task data integration sub-module obtains the beat coordination correction data, performs an overall integration operation on the scheduling task data, extracts the beat information, station configuration, and adjusted task interval corresponding to each task, integrates them into a unified scheduling task set, and generates scheduling task integration data;

[0102] Obtain the beat coordination correction data and perform an overall integration operation on the scheduling task data. First, extract the task interval adjustment record and beat adjustment value in the beat coordination correction data, and item by item correspond to the task number and original task interval in the scheduling task data. By comparing the interval adjustment value in the beat coordination correction data with the time period of the original scheduling task data, determine the correction amplitude of each task, refine it to the task level, further perform cumulative adjustment on the task time period after beat adjustment to form a continuous scheduling time period, and according to the station configuration corresponding to the task, associate and integrate the updated task time period with the station resource information of each task. For example, if the original execution time of task a is 8:00 - 8:30 and it is shortened to 8:00 - 8:25 after beat adjustment, then the start time of the subsequent task b needs to be adjusted synchronously to 8:25. If the station resource where task b is located is station W1, then the resource allocation time period of station W1 needs to be re-corresponded to 8:25 - 8:55. Further, during the data integration process of all tasks, for tasks with resource conflicts, it is necessary to compare the beat correction amplitudes of adjacent tasks and combine the minimum task interval threshold allowed by the production beat to adjust the order or correct the time period again. For example, if tasks c and d overlap in time due to beat adjustment, and the beat adjustment amplitudes are task c minus 5 minutes and task d minus 3 minutes, then it is necessary to determine whether the time meets the minimum task interval threshold of 5 minutes. If not, re-allocate the beat adjustment amplitude of task c to minus 3 minutes and task d remains unchanged to avoid conflicts. In an actual example, if the beat coordination correction data adjusts the task intervals as follows: task 1 interval is adjusted by 2 minutes, task 2 interval is adjusted by 3 minutes, and task 3 interval is adjusted by 1 minute, then the integrated scheduling task data is as follows: task 1 execution time is 8:00 - 8:28, task 2 execution time is 8:28 - 8:57, and task 3 execution time is 8:57 - 9:26, generating scheduling task integration data.

[0103] The execution period update submodule updates the execution period of each production task based on the scheduling task integration data, adjusts the start and end time of each task according to the task sequence and beat coordination correction data, and obtains the task execution period configuration data;

[0104] Based on the scheduling task integration data, the execution time period of each production task is updated. First, the integrated time period information and workstation number corresponding to each task are obtained from the scheduling task integration data. The task time period is checked for continuity to ensure that the end time of each task corresponds to the start time of the next task. The adjustment range in the beat coordination correction data is called again to calculate the final execution start and end time of each task, and form the updated time period configuration value. In actual application, if the scheduling task integration data is task 4 time period 9:00-9:30, task 5 time period 9:30-10:00, and the beat adjustment range of task 4 is minus 2 minutes, then After the update, the time period of Task 4 is 9:00-9:28, and the start time of Task 5 is updated to 9:28. The updated execution time period configuration values are further aggregated to generate a four-tuple set including task number, workstation number, start time, and end time. This set can be used for subsequent resource scheduling and task scheduling. In a specific example, if the original time of a production task is 10:00-10:30, the workstation is W2, and it is corrected to 9:55-10:25 after the beat adjustment, then the task execution time period configuration value is task number: z1, workstation number: W2, start time: 9:55, end time: 10:25, and the task execution time period configuration data is obtained.

[0105] The resource table generation submodule configures the data of the task execution period, combines the station number and resource allocation requirements of each task, establishes a globally optimized scheduling timetable and resource allocation table, and generates dynamic optimization results for production scheduling;

[0106] According to the task execution period configuration data, combined with the workstation numbers and resource allocation requirements of each task, first sort each task entry in the task execution period configuration value according to the task order, extract the workstation number, start time, and end time of each task in turn, and compare with the workstation resource list to determine the available time period and resource occupancy of each workstation. Fill the task execution period configuration value into the scheduling time table and synchronously fill the resource allocation table. For resource conflicts, by judging whether there is an intersection between the task interval and the available time of the workstation resources, if there is an intersection, mark the conflict in the resource allocation table. In the actual scenario, if task z2 has been allocated to workstation W3 from 10:00 to 11:00, and the new task z3 is allocated to W3 with an execution time from 10:30 to 11:00, then the resource conflict needs to be marked. The solution is to adjust the execution time of task z3 or change the workstation. After completing the resource scheduling, a scheduling time table and a resource allocation table including task numbers, workstation numbers, execution time periods, and resource statuses are formed, and a dynamic optimization result of production scheduling is generated.

[0107] Table 6: Production Task Scheduling Time Configuration Table

[0108] Task number Station number Start time End time Beat adjustment (minutes) z1 W1 8：00 8：25 -5 z2 W2 8：25 8：50 -5 z3 W3 8：50 9：20 -10

[0109] Table 6 lists the start time, end time of the production tasks and the corresponding beat adjustment ranges, and all tasks are arranged in turn according to the scheduling optimization order.

[0110] The above is only a preferred embodiment of the present invention, and does not limit the present invention in other forms. Any person skilled in the art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A production scheduling optimization system based on data analysis, characterized in that The system includes: A process data trend analysis module, which obtains continuous process duration record data, constructs a duration increase sequence to perform symbol consistency judgment. If all symbols are positive, it calculates the deviation between the latest process duration and the current scheduling beat number. If it is greater than the beat time deviation threshold, it records and generates a process beat adjustment parameter set; A station resource real-time acquisition module, which acquires the task completion time of each current station, records the starting time of station idle time, retrieves the target station data of the current scheduling task, and performs task-station-idle time pairing to generate a real-time station idle data set; A scheduling order optimization judgment module, which compares the starting time of station idle of the current task and the next task target station according to the real-time station idle data set, judges whether there is a need to swap the task order, performs adjustment operations to update the scheduling task list, and generates a scheduling order optimization result; A beat coordination inspection module, based on the scheduling order optimization result and the process beat adjustment parameter set, judges the proportional relationship of the beat numbers of adjacent tasks in the scheduling task list. If the adjacent task beat ratio is greater than the adjacent beat ratio tolerance, it shortens the task interval time and records and generates beat coordination correction data.

2. The production scheduling optimization system based on data analysis according to claim 1, wherein The process beat adjustment parameter set includes a duration increase determination value, a beat time deviation amount, and a beat adjustment suggestion value. The real-time station idle data set includes a station number, an idle starting time, and a task matching degree. The scheduling order optimization result includes a task swap record, an updated task order list, and an adjusted scheduling priority. The beat coordination correction data includes a beat ratio correction value, a task interval adjustment amount, and a beat coordination flag.

3. The production scheduling optimization system based on data analysis according to claim 1, characterized in that, The process data trend analysis module includes: A duration difference extraction sub-module, which, based on the obtained continuous process duration record data, arranges them in chronological order, performs difference calculation on any adjacent process durations, obtains a first-order difference sequence of adjacent process times, calculates the absolute value of all differences in the duration difference sequence, and obtains a set of absolute values of the duration difference sequence; A symbol consistency judgment sub-module, according to the set of absolute values of the duration difference sequence, performs symbol consistency judgment on any continuous differences in the sequence, screens out the difference combination intervals where all symbols are positive, extracts the latest process duration and the current scheduling beat number based on the screened intervals, and calculates and obtains the process duration beat deviation value; A beat adjustment parameter generation sub-module, which compares the process duration beat deviation value with the beat time deviation threshold. If the deviation is greater than the beat time deviation threshold, it extracts the current scheduling beat number, the latest process duration, and the beat time deviation threshold, calculates the beat adjustment reference value, and establishes a beat adjustment parameter set.

4. The production scheduling optimization system based on data analysis according to claim 1, characterized in that The station resource real-time acquisition module includes: A task completion acquisition sub-module, which acquires the task completion time of each current station and records the system clock time corresponding to the end of the task for each station, generating a set of station task completion times; An idle time recording sub-module, based on the set of station task completion times, records the system clock time after task completion for each station as the idle starting time, and corresponds it to the station identifier to establish a value of the station idle starting time. The station data pairing sub-module pairs tasks with stations according to the starting time value of station idle time, and combines the target station number of each task with the starting time of station idle time based on the target station data of the current scheduling task, integrates the task number, station number, and starting time of idle time, and obtains the real-time dataset of station idle time.

5. The production scheduling optimization system based on data analysis according to claim 1, characterized in that The scheduling order optimization judgment module includes: The idle time comparison sub-module obtains the starting time of target station idle time of the current task and the next task according to the real-time dataset of station idle time, performs a comparison of time sequence according to the starting time of target station idle time of the current task and the starting time of target station idle time of the next task, and obtains the difference in station idle time. The order swap determination sub-module determines whether the starting time of the next task station idle time is earlier than the starting time of the current task station idle time based on the difference in station idle time. If the judgment result is yes, it records the order swap requirement of the current task and the next task, and obtains the task order swap flag. The scheduling list update sub-module performs an order adjustment operation on the scheduling task list according to the task order swap flag. After adjusting the task order, it updates the scheduling task list to generate the scheduling order optimization result.

6. The production scheduling optimization system based on data analysis according to claim 1, characterized in that The beat coordination inspection module includes: The beat ratio judgment sub-module extracts the beat numbers of adjacent tasks in the scheduling task list based on the scheduling order optimization result and the process beat adjustment parameter set, performs a ratio relationship judgment on the beat numbers of adjacent tasks, calls the adjacent beat ratio tolerance, calculates the adjacent task beat ratio, and compares it with the beat ratio tolerance to obtain the beat ratio overrun flag. The task interval adjustment sub-module shortens the task interval time between adjacent tasks marked as overrun according to the beat ratio overrun flag. After shortening the task interval time, it records the obtained task interval adjustment value. The configuration record generation sub-module integrates the adjusted task interval time and the corresponding task number according to the task interval adjustment value, establishes a complete task interval configuration record, and generates the beat coordination correction data.

7. The production scheduling optimization system based on data analysis according to claim 1, characterized in that, The system further includes: The scheduling dynamic optimization module integrally integrates the scheduling task data according to the beat coordination correction data, updates the execution period of each production task and the corresponding station configuration, constructs a globally optimized scheduling time table and resource allocation table, and generates the production scheduling dynamic optimization result. The production scheduling dynamic optimization result includes an optimized scheduling time table, a resource allocation table, and a task execution period configuration.

8. The production scheduling optimization system based on data analysis according to claim 7, wherein The scheduling dynamic optimization module includes: The task data integration sub-module obtains the beat coordination correction data, performs an overall integration operation on the scheduling task data, extracts the beat information, station configuration, and adjusted task interval corresponding to each task, integrates them into a unified scheduling task set, and generates the scheduling task integration data. The execution period update sub-module updates the execution period of each production task based on the scheduling task integration data, adjusts the start and end times of each task according to the task order and the beat coordination correction data, and obtains the task execution period configuration data. The resource table generation sub-module, according to the task execution period configuration data, combines the work station numbers and resource allocation requirements of each task to establish a globally optimized scheduling time table and resource allocation table, and generates a dynamically optimized result of production scheduling.

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