Enterprise management process optimization method and system

Abstract

The invention relates to the technical field of workflow optimization, in particular to an enterprise management process optimization method and system, and the method comprises the following steps: obtaining the length of an approval task queue, the average approval time and the number of pending tasks, calculating a task backlog rate, calling task flow fluctuation to set a node load state coefficient, and generating a load distribution state of approval nodes. According to the invention, the task circulation priority is calculated based on the time constraint and the service weight, so that the emergency task is circulated preferentially, the approval efficiency is improved, the approval path is sorted through the node processing capability and is adjusted and optimized, the task circulation adapts to the load change, the flow blocking risk is reduced, and the task state change record is screened based on the circulation path. The data screening accuracy is improved, the effectiveness of task execution data is ensured, the abnormal detection is combined with the time sequence trend and abnormal node analysis, abnormal task tracking is more efficient, problem nodes are traced accurately, and abnormal task processing is accelerated.

Description

Technical Field

[0001] The present invention relates to the technical field of workflow optimization, and in particular to an enterprise management process optimization method and system. Background Art

[0002] The field of workflow optimization technology includes technical methods for analyzing, modeling, executing, monitoring and optimizing business processes. The core content of this technical field includes the use of computer systems or software tools to standardize the processes within the enterprise and across organizations, describe the sequence of business activities, data flow and decision nodes through process modeling, and rely on rule engines or scheduling mechanisms to execute tasks in each link. Workflow optimization usually involves task allocation, process automation, resource scheduling, anomaly detection and other aspects, and is mainly used in enterprise resource planning, supply chain management, customer relationship management and other systems to improve process execution efficiency and task collaboration capabilities.

[0003] Among them, the enterprise management process optimization method and system refers to adjusting the existing management process of the enterprise through process modeling, task scheduling, data analysis and other methods to improve business processing efficiency and resource utilization. This patent subject is aimed at task allocation, approval flow, data interaction and other matters in process execution. By building a task allocation mechanism based on rule matching, tasks are assigned to corresponding execution entities according to preset conditions, and node status tracking methods are used to record process progress to ensure orderly progress in each stage. At the same time, by setting approval authority control strategies, classified approval process management for different business matters is realized, and key data in the process execution process is recorded and retrieved using data storage and query methods to support management decisions and process optimization.

[0004] Existing technologies rely on fixed rules or historical statistical data in the evaluation of task load status, which cannot reflect the actual task backlog of approval nodes in real time, resulting in the accumulation of approval tasks at high-load nodes, affecting the overall flow efficiency. Task priority is usually set with predefined weights, which cannot be adjusted in real time according to the dynamic changes in business needs, resulting in high-priority tasks being occupied by low-priority tasks in key scenarios. Approval resources may be occupied by approval resources, affecting the processing time of urgent tasks. The adjustment of approval paths mainly relies on static rules or manual intervention, lacking adaptive optimization capabilities, making it difficult to quickly adjust approval tasks when system load changes, resulting in delays in the approval of some tasks or process blockages. The screening of task execution data is usually based on fixed data screening rules, lacking the ability to dynamically match the actual task flow path, resulting in the screened data may not accurately reflect the execution status of the current task, reducing the effectiveness of the data screening results. Abnormal detection of task execution status mainly relies on single-point abnormality analysis, lacking the ability to track time series trends, making it difficult to trace the root cause of task abnormalities, resulting in abnormal status difficult to correct quickly. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an enterprise management process optimization method and system.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a method for optimizing enterprise management process, comprising the following steps:

[0007] S1: Obtain the approval task queue length, average approval time, and number of pending tasks, calculate the task backlog rate, call the task flow fluctuation to set the node load state coefficient, and generate the load distribution state of the approval node;

[0008] S2: Obtain the task deadline, business type, and dependent task status, call the load distribution status of the approval node to calculate the task time constraint level, set the task flow priority adjustment value, and generate the task flow priority parameter;

[0009] S3: Based on the load distribution state and the task flow priority parameter, obtain the task processing capacity ranking of the approval node, adjust the task flow path weight of the approval node, calculate the task path adjustment interval, call the task flow path weight of the approval node and the task path adjustment interval to reset the approval path of the task, and generate the task flow path;

[0010] S4: Obtain the task flow path and task execution record, call the optimized task flow path to filter task status change records, adjust the task data filtering range, and generate task data filtering results;

[0011] S5: Obtain the task data screening result, call the optimized task flow path, calculate the task status change trend, call the abnormal task node, and establish a task execution status backtracking chain.

[0012] As a further solution of the present invention, the load distribution status of the approval node includes the approval task backlog ratio, the task flow change rate, and the node load level; the task flow priority parameters include the task time constraint coefficient, the business priority weight, and the task approval order adjustment value; the task flow path includes the node processing capability sequence, the task flow path weight value, and the path adjustment interval range; the task data screening results include the screened task status records, the matching execution data range, and the task path optimization results; the task execution status backtracking chain includes the status change trend indicator, the abnormal task identification node, and the traceable task status trajectory.

[0013] As a further solution of the present invention, the specific steps of obtaining the approval task queue length, average approval time, and number of pending tasks, calculating the task backlog rate, calling the task flow fluctuation to set the node load state coefficient, and generating the load distribution state of the approval node are:

[0014] S101: Obtain the number of tasks in the approval task queue, obtain the approval time of each task and accumulate it, calculate the total approval time value of the task queue, obtain the number of pending tasks and accumulate it, analyze the proportion of pending tasks in the task queue, calculate the ratio of the total number of tasks to the number of pending tasks, obtain the approval time of the unit task, and obtain the proportion of pending tasks and the average approval time;

[0015] S102: calling the pending task ratio and the average approval time, obtaining the task processing capacity per unit time, obtaining the number of tasks entering the task queue in the same time period, analyzing the task entry rate, calculating the task backlog growth rate, calling the task backlog growth rate and the task processing capacity, calculating the task backlog rate, and obtaining the task backlog result;

[0016] S103: Call the task backlog result, obtain the task flow fluctuation parameter, calculate the load state coefficient of the approval node, obtain the task processing capacity of the approval node, calculate the task processing ratio under the differentiated load state, match the task processing ratio with the task processing capacity of the approval node, calculate the load distribution state of the node under the differentiated flow fluctuation condition, and obtain the load distribution state of the approval node.

[0017] As a further solution of the present invention, the specific steps of obtaining the deadline, business type, and dependent task status of the task, calling the load distribution status of the approval node to calculate the task time constraint level, setting the task flow priority adjustment value, and generating the task flow priority parameter are as follows:

[0018] S201: Obtain the deadline of the task, obtain the business type of the task and the status of the dependent tasks, determine the completion status of the dependent tasks, filter out the unfinished dependent tasks, and calculate the remaining available time of the current task, match the available time with the task business type, identify the time demand intensity of the task, and combine the number and status of the dependent tasks to obtain the task time constraint parameters;

[0019] S202: calling the task time constraint parameter, calling the approval node load distribution status, obtaining the load status of the approval node where the current task is located, calculating the matching degree between the task time constraint parameter and the node load, determining the time pressure of the task at the current node, setting the time constraint level of the task according to the time pressure, and combining the time constraint level with the key parameters of the task business type to obtain the task time constraint level;

[0020] S203: Call the task time constraint level, obtain the flow rules of the approval process where the task is located, calculate the flow time difference of the task under differentiated priorities, calculate the priority adjustment value of the task flow according to the time constraint level, and superimpose the adjustment value with the priority calculation benchmark of the process where the task is located to obtain the task flow priority parameter.

[0021] As a further solution of the present invention, based on the load distribution state and the task flow priority parameter, the task processing capacity ranking of the approval node is obtained, the task flow path weight of the approval node is adjusted, the task path adjustment interval is calculated, and the task flow path weight of the approval node and the task path adjustment interval are called to reset the approval path of the task. The specific steps of generating the task flow path are:

[0022] S301: calling the load distribution state of the approval node and the task flow priority parameter, obtaining the current task processing capacity of each approval node and the flow status of the approval node, calculating the task processing rate of each node, sorting the approval nodes based on the task processing rate, determining the processing order of tasks in the differentiated nodes in combination with the task flow priority parameter, and obtaining the task processing capacity sorting of the approval nodes;

[0023] S302: calling the task processing capacity ranking of the approval node, obtaining each task flow path in the current approval path, calculating the flow duration of each node task flow path, determining the flow burden of the task on the differentiated path, calculating the task flow path weight adjustment value according to the flow burden, and obtaining the approval node task flow path weight;

[0024] S303: Call the task flow path weight of the approval node, calculate the adjustment interval of the task on the differentiated path, obtain the adjusted flow efficiency of each path, calculate the optimal flow path of the task based on the task flow priority parameter and the task flow path weight of the approval node, apply the result to the task approval path setting, and obtain the task flow path.

[0025] As a further solution of the present invention, the comprehensive circulation time calculation formula is specifically:

[0026]

[0027] Among them, T 2 Represents the comprehensive flow time of the node, t i represents the task processing time on node i, w i represents the task processing priority weight of node i, t 总 represents the cumulative processing time of all tasks on the flow path, and n represents the total number of nodes in the task path.

[0028] As a further solution of the present invention, the specific steps of obtaining the task flow path and task execution record, calling the optimized task flow path to filter task status change records, adjusting the task data filtering range, and generating task data filtering results are as follows:

[0029] S401: Acquire the task flow path and task execution record, filter the task status change data at the approval node, calculate the task stay time at the node, determine the key node where the task status change occurs, obtain the task status change, filter the status change data, and obtain the task status change record;

[0030] S402: calling the task status change record, calling the optimized task flow path, filtering the execution data of the task at the approval node, calculating the matching degree value between the task status change record and the flow path, filtering the task data whose matching degree exceeds the adjustment threshold, adjusting the data filtering range, removing the redundant data outside the filtering range, and obtaining the task data filtering range;

[0031] S403: calling the task data screening range, obtaining the task flow data within the screening range, screening the data that meets the status change, calculating the distribution ratio value of the data category, screening the data set that meets the screening conditions according to the status change, and obtaining the task data screening result.

[0032] As a further solution of the present invention, the matching degree value calculation formula is specifically:

[0033]

[0034] Calculate the matching degree value, filter out the task data whose matching degree exceeds the adjustment threshold, adjust the data filtering range, remove the redundant data outside the filtering range, and obtain the task data filtering range;

[0035] Among them, M match Indicates the matching degree value, R j，act represents the actual state change record value of the jth node, P j，opt represents the preset state change value of the jth node in the optimization path, W j，node represents the weight of the jth node, reflecting the importance of the node in the entire flow path, N nodes Indicates the total number of nodes in the task flow path.

[0036] As a further solution of the present invention, the specific steps of obtaining the task data screening result, calling the optimized task flow path, calculating the task status change trend, calling the abnormal task node, and establishing the task execution status backtracking chain are as follows:

[0037] S501: calling the task data screening result, calling the optimized task flow path, obtaining the task status change record at the approval node, calculating the task status conversion time at the differentiation node, obtaining the status change rate, judging whether there is abnormal fluctuation in the task status change trend, screening the task nodes whose status change rate exceeds the set range, and obtaining the task status change trend;

[0038] S502: calling the task status change trend, obtaining the approval path of the task in the abnormal fluctuation range, screening the stagnation state of the task in the abnormal path, calculating the stay time of the task in the abnormal path, determining the task nodes whose stay time exceeds the normal range, screening the task nodes whose status changes do not comply with the flow rules, marking the abnormal nodes that exceed the stay time threshold, and obtaining the abnormal task nodes;

[0039] S503: Call the abnormal task node, obtain the task status of the abnormal task node, screen the state change trajectory of the task before and after the abnormal node, calculate the matching degree between the state change time and the task flow path, establish the task state backtracking path according to the matching degree, connect the abnormal task node and the state change chain, and obtain the task execution state backtracking chain.

[0040] An enterprise management process optimization system, comprising:

[0041] The task queue module obtains the approval task queue length, average approval time, and number of pending tasks, calls task flow fluctuation to set the node load state coefficient, and generates the load distribution state of the approval node;

[0042] The task constraint module obtains the deadline and business type of the task, calls the load distribution status of the approval node to calculate the task time constraint level, sets the task flow priority adjustment value, and generates the task flow priority parameter;

[0043] The path adjustment module obtains the task processing capacity ranking of the approval node based on the load distribution state and the task flow priority parameter, calculates the task path adjustment interval, calls the task flow path weight of the approval node and the task path adjustment interval to reset the approval path of the task, and generates the task flow path;

[0044] The status screening module obtains the task flow path and task execution record, calls the optimized task flow path to screen the task status change record, and generates a task data screening result;

[0045] The status backtracking module obtains the task data screening result, calls the optimized task flow path, calculates the task status change trend, and establishes a task execution status backtracking chain.

[0046] Compared with the prior art, the advantages and positive effects of the present invention are:

[0047] In the present invention, the priority of task flow is calculated based on time constraints and business weights, so that urgent tasks are given priority to improve approval efficiency. The approval path is optimized by node processing capacity sorting and path adjustment, so that task flow can adapt to load changes and reduce the risk of process blocking. Task status change records are filtered based on flow paths to improve data screening accuracy and ensure the validity of task execution data. Anomaly detection is combined with time series trend and abnormal node analysis to make abnormal task tracking more efficient, accurately trace problem nodes, and speed up abnormal task processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0049] Figure 1 It is a schematic diagram of the steps of the present invention;

[0050] Figure 2 is a flow chart of the steps of S1 of the present invention;

[0051] Figure 3 is a flow chart of the steps of S2 of the present invention;

[0052] Figure 4 is a flow chart of the steps of S3 of the present invention;

[0053] Figure 5 is a flow chart of the steps of S4 of the present invention;

[0054] Figure 6 is a flow chart of the steps of S5 of the present invention;

[0055] Figure 7 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0056] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0057] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0058] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0059] In the embodiments of the present invention, sometimes the subscripts such as W 1 It may be written in non-subscript form such as W1. When the difference is not emphasized, the meaning is the same.

[0060] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0061] See also Figure 1 , a method for optimizing enterprise management processes, comprising the following steps:

[0062] S1: Obtain the approval task queue length, average approval time, and number of pending tasks, calculate the task backlog rate, call the task flow fluctuation to set the node load state coefficient, and generate the load distribution state of the approval node;

[0063] S2: Obtain the task deadline, business type, and dependent task status, call the load distribution status of the approval node to calculate the task time constraint level, set the task flow priority adjustment value, and generate the task flow priority parameter;

[0064] S3: Based on the load distribution status and task flow priority parameters, obtain the task processing capacity ranking of the approval node, adjust the task flow path weight of the approval node, calculate the task path adjustment interval, call the task flow path weight of the approval node and the task path adjustment interval to reset the task approval path, and generate the task flow path;

[0065] S4: Obtain the task flow path and task execution record, call the optimized task flow path to filter the task status change record, adjust the task data filtering range, and generate the task data filtering result;

[0066] S5: Obtain the task data screening results, call the optimized task flow path, calculate the task status change trend, call the abnormal task node, and establish the task execution status backtracking chain.

[0067] The load distribution status of the approval node includes the approval task backlog ratio, task flow change rate, and node load level. The task flow priority parameters include the task time constraint coefficient, business priority weight, and task approval order adjustment value. The task flow path includes the node processing capacity sequence, the task flow path weight value, and the path adjustment interval range. The task data screening results include the screened task status records, the matching execution data range, and the task path optimization results. The task execution status backtracking chain includes the status change trend indicator, the abnormal task identification node, and the traceable task status trajectory.

[0068] See also Figure 2 , the specific steps of S1 are:

[0069] S101: Obtain the number of tasks in the approval task queue, obtain the approval time of each task and accumulate it, calculate the total approval time value of the task queue, obtain the number of pending tasks and accumulate it, analyze the proportion of pending tasks in the task queue, calculate the ratio of the total number of tasks to the number of pending tasks, obtain the approval time of the unit task, and obtain the proportion of pending tasks and the average approval time;

[0070] First, get the total number of tasks from the approval task queue. This data is achieved through database query operations. Then, accumulate the approval time of each task, which usually involves traversing the task queue and calling the time record. Such accumulation operations require correct processing of data types to ensure the accuracy of time values. For example, if the approval time of task 1 is 2 hours and that of task 2 is 3 hours, the accumulated result should be 5 hours. Then, according to the definition of pending tasks, traverse the task queue and count the number of tasks in the pending state. Pending tasks refer to those tasks that have entered the queue but have not yet completed approval. For example, if there are 10 tasks in the queue and 3 of them are pending, the number of pending tasks is 3. After accumulating these data, , analyze the proportion of pending tasks in the task queue, which is the ratio obtained by dividing the number of pending tasks by the total number of tasks. This calculation can be obtained by simple division, such as 3 / 10=0.3, which means that pending tasks account for 30% of the total tasks. Then calculate the ratio of the total number of tasks to the number of pending tasks. This ratio reflects the relationship between pending tasks and total tasks. By dividing the total number of tasks by the number of pending tasks, for example, 10 / 3≈3.33, it means that each pending task corresponds to approximately 3.33 tasks. Finally, obtain the approval time of the unit task, which is obtained by dividing the total approval time by the total number of tasks, such as 5 hours / 10 tasks=0.5 hours / task, and get the proportion of pending tasks and the average approval time.

[0071] S102: Call the pending task ratio and the average approval time to obtain the task processing capacity per unit time, obtain the number of tasks entering the task queue in the same time period, analyze the task entry rate, calculate the task backlog growth rate, call the task backlog growth rate and the task processing capacity, calculate the task backlog rate, and obtain the task backlog result;

[0072] First, the task processing capacity per unit time is analyzed by the obtained pending task ratio and the average approval time. This can be achieved by calculating the number of tasks that can be processed in a given time. For example, if the average approval time is 0.5 hours / task, then theoretically 2 tasks can be processed within 1 hour. Next, the number of tasks entering the task queue in the same time period is obtained, which involves real-time monitoring and recording of data. For example, if 3 tasks have been added to the queue in the past hour, the task entry rate is 3 tasks / hour. Based on these data, the task backlog growth rate is calculated. This is achieved by taking the difference between the task entry rate and the processing rate, such as the growth rate of 3 tasks / hour-2 tasks / hour=1 task / hour. The task backlog growth rate and task processing capacity are called, and the task backlog rate is calculated by combining these two indicators. The task backlog rate can be obtained by dividing the total number of unprocessed tasks by the total number of tasks. For example, if the backlog tasks are 5 and the total tasks are 15, the backlog rate is 5 / 15=0.33, i.e. 33%. Finally, the task backlog result is obtained. This is a process of analyzing data and proposing improvement measures, for example, reducing the backlog rate by increasing approval resources or optimizing processes.

[0073] S103: calling the task backlog result, obtaining the task flow fluctuation parameter, calculating the load state coefficient of the approval node, obtaining the task processing capacity of the approval node, calculating the task processing ratio under the differentiated load state, matching the task processing ratio with the task processing capacity of the approval node, calculating the load distribution state of the node under the differentiated flow fluctuation state, and obtaining the load distribution state of the approval node;

[0074] First, the task flow fluctuation parameters are obtained through the existing task backlog results. This usually involves historical data analysis. For example, the peak and valley time periods are identified through the data in the past week. Then, the load state coefficient of the approval node is calculated by the ratio of the actual processing capacity to the theoretical maximum processing capacity. For example, if the number of tasks that the node can process at peak times is 8, and theoretically it can process 10 tasks, then the load state coefficient is 8 / 10=0.8. Next, the task processing capacity of the approval node is obtained. This requires testing the performance of the node under different conditions, such as the number of tasks processed under normal and high load conditions. Then, the load state coefficient under differentiated load conditions is calculated. Task processing ratio, which is achieved by comparing the processing capacity under different load conditions. For example, if the processing capacity is 10 tasks / hour under low load conditions and 8 tasks / hour under high load conditions, the processing ratio is 8 / 10=0.8. The task processing ratio is matched with the task processing capacity of the approval node. This is an optimization process that aims to adjust resource allocation to match the needs under different load conditions. Finally, the load distribution state of the computing node under differentiated traffic fluctuations involves a comprehensive evaluation of the entire approval system to ensure efficient operation under any circumstances. For example, by adjusting the task allocation strategy, the node load can be maintained in the optimal state at any time.

[0075] See also Figure 3 , the specific steps of S2 are:

[0076] S201: Obtain the deadline of the task, obtain the business type of the task and the status of the dependent tasks, determine the completion status of the dependent tasks, filter out the unfinished dependent tasks, and calculate the remaining available time of the current task, match the available time with the task business type, identify the time demand intensity of the task, and combine the number and status of the dependent tasks to obtain the task time constraint parameters;

[0077] First, call the unique identifier of the task from the task management system and match it with the corresponding database field to extract its deadline. For example, the deadline of Task A is February 15, 2025. Then, obtain the business type of the task and the status of the dependent tasks. By querying the business type table and the dependent task status table, the business type of Task A is obtained as "Contract Approval", and the dependent tasks are Task B and Task C, with the statuses of "Completed" and "Uncompleted" respectively. Determine the completion status of the dependent tasks by traversing the dependent task status table and matching the status fields with the "Completed" status one by one. For example, Task B is a completed task, and Task C is an uncompleted task. When filtering unfinished dependent tasks, the tasks marked as "Uncompleted" are separately included in the list of unfinished dependent tasks. For example, after filtering, the unfinished task is Task C. Calculate the remaining available time of the current task through the formula:

[0078] T 剩余 =T 截止 -T 当前 ;

[0079] Among them, T 截止 is the task deadline, T 当前 is the current system time. For example, if the deadline is February 15, 2025 and the current time is February 11, 2025, then T 剩余 = February 15, 2025 - February 11, 2025 = 4 days, match the available time with the task business type, obtain the time requirement range (2-5 days) corresponding to the task type "contract approval" by querying the business rule table, identify the time requirement intensity of the task, and quantify it by comparing the deviation between the remaining time and the lower limit of the requirement range. For example, if the remaining time is 4 days, which is close to the upper limit, it is marked as "low intensity". Combined with the number and status of dependent tasks, the time requirement intensity weight is corrected. If there are more than 1 unfinished tasks, the time requirement intensity weight is increased. For example, there is only 1 unfinished task at present, and no correction is required. Finally, the corrected time requirement intensity weight is applied to the time calculation to obtain the task time constraint parameter.

[0080] S202: calling the task time constraint parameters, calling the approval node load distribution status, obtaining the load of the approval node where the current task is located, calculating the matching degree between the task time constraint parameters and the node load, judging the time pressure of the task at the current node, setting the time constraint level of the task according to the time pressure, and combining the time constraint level with the key parameters of the task business type to obtain the task time constraint level;

[0081] First, parse the time pressure weight in the task time constraint parameter. For example, if the weight value is 0.7, call the load distribution status of the approval node, and obtain the real-time load rate of the node through the current load record of the approval node. For example, if the maximum capacity of the node is 20 tasks and the current task is 16 tasks, the load rate calculation formula is:

[0082]

[0083] Among them, N 当前 is the current number of tasks, N 最大 is the maximum task capacity of the node, and L is calculated 负载 =16 / 20=0.8, calculate the matching degree between the task time constraint parameters and the node load, and calculate the matching value by multiplying the time pressure weight and the load rate:

[0084] M 匹配 =W 压力 ×L 负数 ;

[0085] Among them, W 压力=0.7, then M 匹配 =0.7×0.8=0.56, judge the time pressure of the task at the current node, by comparing the matching value with the benchmark value (such as 0.5), if the matching value is greater than the benchmark value, it is marked as "high pressure", set the time constraint level of the task, by looking up the pressure level table, for example, a matching value of 0.56 corresponds to "medium pressure", combined with the time constraint level and the key parameters of the task business type, adjust the level through the correction coefficient, for example, the task type "contract approval" corresponds to the correction coefficient +0.2, then the final time constraint level is adjusted to "medium high", and the task time constraint level is obtained.

[0086] S203: calling the task time constraint level, obtaining the flow rules of the approval process where the task is located, calculating the flow time difference of the task under the differentiated priority, calculating the priority adjustment value of the task flow according to the time constraint level, and superimposing the adjustment value with the priority calculation benchmark of the process where the task is located to obtain the task flow priority parameter;

[0087] Call the task time constraint level, for example, the time constraint level of the task is 2.5, obtain the flow rules of the approval process where the task is located, query the flow path of the task through the process rule table, for example, the "contract approval" task needs to pass through nodes A, B, and C, calculate the flow time difference of the task under differentiated priorities, and use the priority-related processing time difference formula:

[0088] ΔT 流转 =T 低优先级 -T 高优先级 ;

[0089] Among them, T 低优先级 = 8 hours, T 高优先级 = 6 hours, then ΔT 流转 =8-6=2 hours. Calculate the priority adjustment value of task flow according to the time constraint level. Match it through the priority adjustment table. For example, if the time constraint level is 2.5, the corresponding adjustment value is +0.4. Add the adjustment value to the priority calculation benchmark value. The formula is:

[0090] P 优先级 =P 基准 +ΔP 调整 ;

[0091] Among them, P 基准 =1.6, ΔP 调整 =0.4, then P 优先级 =1.6+0.4=2.0, and finally the task flow priority parameter is obtained.

[0092] See also Figure 4 , the specific steps of S3 are:

[0093] S301: Call the load distribution status of the approval node and the task flow priority parameter to obtain the current task processing capacity of each approval node and the flow status of the approval node, calculate the task processing rate of each node, sort the approval nodes based on the task processing rate, and determine the processing order of tasks in the differentiated nodes in combination with the task flow priority parameter to obtain the task processing capacity ranking of the approval nodes;

[0094] The current task processing capacity of each approval node is obtained by querying the node status table. This is calculated by counting the total number of tasks being processed by the node and its average processing time. The flow status of each node is extracted in combination with the node task flow number in the flow record table, and the task processing rate of each node is calculated. The formula task processing rate = number of processed tasks / total processing time is used to obtain the capacity. For example, it takes a total of 20 hours for node 1 to process 10 tasks, and its task processing rate is 0.5 tasks / hour. Subsequently, the task processing rates of each node are sorted according to the numerical value. For example, the rate of node 1 is 0.5, node 2 is 0.8, and node 3 is 0.6, then the order is node 2>node 3>node 1. Combined with the task flow priority parameter, the processing order of tasks at each node is determined by the priority parameter value. For example, when the priority parameter is 1.2, the node with the highest processing rate is selected first to obtain the task processing capacity ranking of the approval nodes.

[0095] S302: calling the approval node task processing capacity ranking, obtaining each task flow path in the current approval path, calculating the flow duration of each node task flow path, determining the flow burden of the task on the differentiated path, calculating the task flow path weight adjustment value according to the flow burden, and obtaining the approval node task flow path weight;

[0096] The calculation formula for comprehensive circulation time is as follows:

[0097]

[0098] Among them, T 2 Represents the comprehensive flow time of the node, t i represents the task processing time on node i, w i represents the task processing priority weight of node i, t 总 represents the cumulative processing time of all tasks on the flow path, and n represents the total number of nodes in the task path.

[0099] The formula consists of two parts. The first part calculates the comprehensive duration value by weighted average, and the second part calculates the imbalance of duration distribution by absolute difference.

[0100] Parameter details:

[0101] T 2Represents the comprehensive duration of the node flow path, which is used to measure the overall efficiency of the path. i is the task processing time on node i, which is calculated by the difference between the task processing start time and end time recorded in the system log. For example, if the task processing start time of node 1 is 8:00 and the end time is 8:45, the task processing time is 45 minutes. i is the priority weight of task processing at node i, which is set by analyzing the importance of tasks on node i based on historical data. For example, if the critical tasks account for 30% of the historical processing of node 1, its weight value is 0.3. 总 is the cumulative processing time of all nodes on the flow path, and i Sum calculation, n is the total number of nodes in the flow path, which is determined by the number of nodes in the approval process path.

[0102] Calculation process:

[0103] Assume that an approval path contains 3 nodes, and the node processing time and weight are:

[0104] Node 1: t 1 =45 minutes, w 1 =0.3

[0105] Node 2: t 2 = 60 minutes, w 2 =0.4

[0106] Node 3: t 3 = 30 minutes, w 3 =0.3

[0107] Calculate the weighted average processing time portion:

[0108]

[0109]

[0110] Calculate the balance part of the duration distribution:

[0111] t 总 =t 1 +t 2 +t 3 =45+60+30=135;

[0112]

[0113] Comprehensive calculation:

[0114] T 2 =6.82+0=6.82;

[0115] The result shows that the comprehensive duration of the node flow path is 6.82 minutes, which is used to judge the overall efficiency of this path. Further correlation with the path weight result can optimize the task allocation plan.

[0116] S303: Call the task flow path weight of the approval node, calculate the adjustment interval of the task on the differentiated path, obtain the adjusted flow efficiency of each path, calculate the optimal flow path of the task according to the task flow priority parameter and the task flow path weight of the approval node, apply the result to the task approval path setting, and obtain the task flow path;

[0117] The adjustment interval of the task is calculated by the path weight value to obtain the adjusted flow efficiency of each path. According to the path weight adjustment value and the task flow priority parameter, the weight and priority parameters need to be combined through the weighted sum formula calculation. The efficiency of the task on all paths is sorted by the adjusted flow efficiency value to identify the optimal path. For example, the basic efficiency of node path 1 is 0.7, the weight value is 0.2, and the priority parameter is 0.5. The adjusted efficiency is 0.7×(1-0.2+0.5)=0.77. By comparing the adjusted efficiency values ​​of all paths, the path with the highest efficiency is selected as the task flow path, which is finally applied to the task approval path setting to obtain the task flow path.

[0118] See also Figure 5 , the specific steps of S4 are:

[0119] S401: Obtain the task flow path and task execution record, filter the task status change data at the approval node, calculate the task stay time at the node, determine the key node where the task status change occurs, obtain the task status change, filter the status change data, and obtain the task status change record;

[0120] First, the system extracts the flow paths and execution records of all tasks from the database. For example, for each task, the system records each node and timestamp from its entry into the queue to its completion. Next, the status change data of the task at the approval node is screened. This step involves identifying specific data points where the status has changed from a large number of task records. For example, the status of task A at node 1 changes from "pending" to "processing". Next, the time the task stays at the node is calculated by differentiating the timestamps of each task status. For example, the time task A stays at node 1 is 2 hours, which is the length of time from the start of the status change to the next status change. This is used to determine the key nodes where the task status change occurs. This is achieved by analyzing the residence time of all tasks and identifying those nodes with abnormally long residence time. If the task stays at a node for more than twice the average residence time, the node is marked as a key node. The task status change situation is obtained, which involves summarizing the change records of the task status at each key node, analyzing its frequency and pattern, and filtering the status change data. The purpose is to extract the task data with important status changes at the key nodes from all records, such as selecting those status changes from "processing" to "completed" or "delayed". Finally, the task status change records are obtained, which are used to further analyze the efficiency of task flow and the performance of nodes.

[0121] S402: calling the task status change record, calling the optimized task flow path, filtering the execution data of the task at the approval node, calculating the matching degree value between the task status change record and the flow path, filtering the task data whose matching degree exceeds the adjustment threshold, adjusting the data filtering range, removing the redundant data outside the filtering range, and obtaining the task data filtering range;

[0122] The specific calculation formula for the matching degree value is:

[0123]

[0124] Calculate the matching degree value, filter out the task data whose matching degree exceeds the adjustment threshold, adjust the data filtering range, remove the redundant data outside the filtering range, and obtain the task data filtering range;

[0125] Among them, M match Indicates the matching degree value, R j，act represents the actual state change record value of the jth node, P j，opt represents the preset state change value of the jth node in the optimization path, W j，node represents the weight of the jth node, reflecting the importance of the node in the entire flow path, N nodes Indicates the total number of nodes in the task flow path;

[0126] The formula calculates the difference between the actual state change record of the task and the preset state change value of the optimized path, and combines the node weights for weighted average to obtain the overall matching degree value M. match . Weight W j，node The setting basis is the contribution of the node to the approval efficiency in the historical task data. For example, if the volatility of the node in the task completion time is high, the weight will also be higher.

[0127] Assume there are four nodes with the following parameters:

[0128] Node 1: R 1，act =3,P 1，opt =2,W 1，node =0.4

[0129] Node 2: R 2，act =1,P 2，opt =1,W 2，node =0.3

[0130] Node 3: R 3，act =4,P 3，opt =3,W 3，node =0.2

[0131] Node 4: R 4，act =2,P 4，opt =3,W 4，node =0.1

[0132] N nodes =4;

[0133] Calculate the difference weighted value for each node:

[0134] |R 1，act -P 1，opt |·W 1，node =|3-2|·0.4=0.4;

[0135] |R 2，act -P 2，opt |·W 2，node =|1-1|·0.3=0.0;

[0136] |R 3，act -P 3，opt |·W 3，node =|4-3|·0.2=0.2;

[0137] |R 4，act -P 4，opt |·W 4，node =|2-3|·0.1=0.1;

[0138] Calculate the denominator (weight sum):

[0139]

[0140] Calculate the matching degree value M match :

[0141]

[0142] The result shows that the overall matching degree is 0.7, indicating that the actual status of the task is highly consistent with the preset flow path. This value can be used to further screen task data with low matching degree and optimize the flow path setting.

[0143] S403: calling the task data screening range, obtaining the task flow data within the screening range, screening the data that meets the status change, calculating the distribution ratio value of the data category, screening the data set that meets the screening condition according to the status change, and obtaining the task data screening result;

[0144] The task flow data within the range determined in the previous step is obtained. For example, all task data with a matching degree of more than 0.8 will be considered. The data that meets the status change is filtered, including filtering out the data whose status changes from "in process" to "completed" or "needs review". The distribution ratio value of the data category is calculated by counting the frequency of various status changes and calculating its proportion in the total data. For example, the "completed" status change accounts for 30%. The data set that meets the filtering conditions is filtered based on the status change. This process involves using complex query statements and data processing algorithms to filter out data that meets specific status change patterns from large data sets. Finally, the task data filtering results are obtained. This result reflects the efficiency and characteristics of task status changes in the filtered data set, and provides a basis for further process optimization.

[0145] See also Figure 6 , the specific steps of S5 are:

[0146] S501: Call the task data screening result, call the optimized task flow path, obtain the task status change record at the approval node, calculate the task status conversion time at the differentiation node, obtain the status change rate, determine whether there is abnormal fluctuation in the task status change trend, filter the task nodes whose status change rate exceeds the set range, and obtain the task status change trend;

[0147] First, extract the task flow path and task status change records from the data screening results. For example, for task A, its status change records from node 1 to node 3 are "pending", "processing", and "completed". Calculate the state transition time of the task at the differentiated nodes, and obtain the state transition time through the difference in state change timestamps. For example, the state transition time of task A from node 1 to node 2 is 2 hours, and the state transition time from node 2 to node 3 is 3 hours. Obtain the state change rate through the formula:

[0148]

[0149] Among them, R 状态变更 Indicates the state change rate, T 转换 Indicates the state transition time. For example, if the state transition time of node 2 is 3 hours, the rate is 1 / 3 = 0.33 times / hour. Determine whether there is abnormal fluctuation in the task state change trend. By comparing each state change rate with the set range (such as the normal range is 0.2-0.5 times / hour), determine whether it is abnormal. For example, if the rate is 0.8 times / hour, it is marked as abnormal. Filter out task nodes whose state change rates exceed the set range. Extract these abnormal data through filtering operations, and finally obtain the task state change trend.

[0150] S502: Call the task status change trend, obtain the approval path of the task in the abnormal fluctuation range, filter the stagnation state of the task in the abnormal path, calculate the stay time of the task in the abnormal path, determine the task node whose stay time exceeds the normal range, filter the task node whose status change does not comply with the flow rules, mark the abnormal node that exceeds the stay time threshold, and obtain the abnormal task node;

[0151] First, based on the status change trend data obtained from the previous analysis, the approval path of the task in the abnormal fluctuation range is obtained. This is obtained by querying the nodes marked as abnormal in the system. For example, the status change rate of nodes D and E is abnormal. The stagnation state of the task in the abnormal path is screened, which includes all task records whose status has not changed as expected at these abnormal nodes. For example, the status of the task at node D should be changed from "under approval" to "completed", but it stays in "under approval" for a long time. The residence time of the task on the abnormal path is calculated. By comparing the actual residence time of the task at the node with the preset average residence time, the task node whose residence time exceeds the normal range is judged. This requires setting a normal residence time range. For example, the normal range is that each node is completed within 1 hour. If it exceeds this time, it is considered abnormal. The task nodes whose status changes do not comply with the flow rules are screened. This involves analyzing the deviation between the task flow path and the preset flow path, and marking the abnormal nodes that exceed the residence time threshold. For example, the task residence time of nodes D and E is 2 hours and 2.5 hours respectively, both exceeding the set threshold of 1.5 hours, and the abnormal task nodes are obtained. These nodes will be the focus of approval process optimization and monitoring.

[0152] S503: Call the abnormal task node, obtain the task status of the abnormal task node, screen the task status change track before and after the abnormal node, calculate the matching degree between the status change time and the task flow path, establish the task status backtracking path according to the matching degree, connect the abnormal task node with the status change chain, and obtain the task execution status backtracking chain;

[0153] The approval system obtains node information marked as abnormal and its task status. For example, nodes D and E are marked as abnormal, and the task status includes "under approval" and "pending confirmation". The status change trajectory of the task before and after the abnormal node is screened. This requires analyzing the task status records near the abnormal node, such as the status of node C to node D is from "completed" to "under approval". The matching degree between the status change time and the task flow path is calculated, which is achieved by comparing the status change time with the preset flow path time. For example, the preset flow time from node C to D is 30 minutes, and the actual time is 45 minutes. The task status backtracking path is established based on the matching degree, which involves reconstructing the historical trajectory of task status changes to determine the specific link where the problem occurred, and connecting the abnormal task node with the status change chain. This is completed by linking the status change records of each abnormal node to obtain the task execution status backtracking chain, which provides detailed context information for diagnosing and solving problems in the approval process.

[0154] See also Figure 7 , an enterprise management process optimization system, comprising:

[0155] The task queue module obtains the approval task queue length, average approval time, and number of pending tasks, calls task flow fluctuation to set the node load state coefficient, and generates the load distribution state of the approval node;

[0156] The task constraint module obtains the deadline and business type of the task, calls the load distribution status of the approval node to calculate the task time constraint level, sets the task flow priority adjustment value, and generates the task flow priority parameter;

[0157] The path adjustment module obtains the task processing capacity ranking of the approval node based on the load distribution status and task flow priority parameters, calculates the task path adjustment interval, calls the task flow path weight of the approval node and the task path adjustment interval to reset the task approval path and generate the task flow path;

[0158] The status screening module obtains the task flow path and task execution records, calls the optimized task flow path to screen the task status change records, and generates task data screening results;

[0159] The status backtracking module obtains the task data screening results, calls the optimized task flow path, calculates the task status change trend, and establishes the task execution status backtracking chain.

[0160] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for optimizing enterprise management processes, characterized in that: The following steps are involved: S1: Obtain the approval task queue length, average approval time, and number of pending tasks, calculate the task backlog rate, call the task flow fluctuation to set the node load state coefficient, and generate the load distribution state of the approval node; S2: Obtain the task deadline, business type, and dependent task status, call the load distribution status of the approval node to calculate the task time constraint level, set the task flow priority adjustment value, and generate the task flow priority parameter; S3: Based on the load distribution state and the task flow priority parameter, obtain the task processing capacity ranking of the approval node, adjust the task flow path weight of the approval node, calculate the task path adjustment interval, call the task flow path weight of the approval node and the task path adjustment interval to reset the approval path of the task, and generate the task flow path; S4: Obtain the task flow path and task execution record, call the optimized task flow path to filter task status change records, adjust the task data filtering range, and generate task data filtering results; S5: Obtain the task data screening result, call the optimized task flow path, calculate the task status change trend, call the abnormal task node, and establish a task execution status backtracking chain.

2. The enterprise management process optimization method according to claim 1, characterized in that: The load distribution status of the approval node includes the approval task backlog ratio, the task flow change rate, and the node load level. The task flow priority parameters include the task time constraint coefficient, the business priority weight, and the task approval order adjustment value. The task flow path includes the node processing capability sequence, the task flow path weight value, and the path adjustment interval range. The task data screening result includes the screened task status record, the matching execution data range, and the task path optimization result. The task execution status backtracking chain includes the status change trend indicator, the abnormal task identification node, and the traceable task status trajectory.

3. The enterprise management process optimization method according to claim 1, characterized in that: The specific steps to obtain the approval task queue length, average approval time, number of pending tasks, calculate the task backlog rate, call the task flow fluctuation to set the node load state coefficient, and generate the load distribution state of the approval node are as follows: S101: Obtain the number of tasks in the approval task queue, obtain the approval time of each task and accumulate it, calculate the total approval time value of the task queue, obtain the number of pending tasks and accumulate it, analyze the proportion of pending tasks in the task queue, calculate the ratio of the total number of tasks to the number of pending tasks, obtain the approval time of the unit task, and obtain the proportion of pending tasks and the average approval time; S102: calling the pending task ratio and the average approval time, obtaining the task processing capacity per unit time, obtaining the number of tasks entering the task queue in the same time period, analyzing the task entry rate, calculating the task backlog growth rate, calling the task backlog growth rate and the task processing capacity, calculating the task backlog rate, and obtaining the task backlog result; S103: Call the task backlog result, obtain the task flow fluctuation parameter, calculate the load state coefficient of the approval node, obtain the task processing capacity of the approval node, calculate the task processing ratio under the differentiated load state, match the task processing ratio with the task processing capacity of the approval node, calculate the load distribution state of the node under the differentiated flow fluctuation condition, and obtain the load distribution state of the approval node.

4. The enterprise management process optimization method according to claim 1, characterized in that: The specific steps to obtain the task deadline, business type, and dependent task status, call the load distribution status of the approval node to calculate the task time constraint level, set the task flow priority adjustment value, and generate the task flow priority parameters are as follows: S201: Obtain the deadline of the task, obtain the business type of the task and the status of the dependent tasks, determine the completion status of the dependent tasks, filter out the unfinished dependent tasks, and calculate the remaining available time of the current task, match the available time with the task business type, identify the time demand intensity of the task, and combine the number and status of the dependent tasks to obtain the task time constraint parameters; S202: calling the task time constraint parameter, calling the approval node load distribution status, obtaining the load status of the approval node where the current task is located, calculating the matching degree between the task time constraint parameter and the node load, determining the time pressure of the task at the current node, setting the time constraint level of the task according to the time pressure, and combining the time constraint level with the key parameters of the task business type to obtain the task time constraint level; S203: Call the task time constraint level, obtain the flow rules of the approval process where the task is located, calculate the flow time difference of the task under differentiated priorities, calculate the priority adjustment value of the task flow according to the time constraint level, and superimpose the adjustment value with the priority calculation benchmark of the process where the task is located to obtain the task flow priority parameter.

5. The enterprise management process optimization method according to claim 1, characterized in that: Based on the load distribution state and the task flow priority parameter, the task processing capacity ranking of the approval node is obtained, the task flow path weight of the approval node is adjusted, the task path adjustment interval is calculated, and the task flow path weight of the approval node and the task path adjustment interval are called to reset the approval path of the task. The specific steps of generating the task flow path are as follows: S301: calling the load distribution state of the approval node and the task flow priority parameter, obtaining the current task processing capacity of each approval node and the flow status of the approval node, calculating the task processing rate of each node, sorting the approval nodes based on the task processing rate, determining the processing order of tasks in the differentiated nodes in combination with the task flow priority parameter, and obtaining the task processing capacity sorting of the approval nodes; S302: calling the task processing capacity ranking of the approval node, obtaining each task flow path in the current approval path, calculating the flow duration of each node task flow path, determining the flow burden of the task on the differentiated path, calculating the task flow path weight adjustment value according to the flow burden, and obtaining the approval node task flow path weight; S303: Call the task flow path weight of the approval node, calculate the adjustment interval of the task on the differentiated path, obtain the adjusted flow efficiency of each path, calculate the optimal flow path of the task based on the task flow priority parameter and the task flow path weight of the approval node, apply the result to the task approval path setting, and obtain the task flow path.

6. The enterprise management process optimization method according to claim 5, characterized in that: The calculation formula for the comprehensive circulation time is specifically: Among them, T2 represents the comprehensive flow time of the node, t i represents the task processing time on node i, w i represents the task processing priority weight of node i, t 总 represents the cumulative processing time of all tasks on the flow path, and n represents the total number of nodes in the task path.

7. The enterprise management process optimization method according to claim 1, characterized in that: The specific steps of obtaining the task flow path and task execution record, calling the optimized task flow path to filter task status change records, adjusting the task data filtering range, and generating task data filtering results are as follows: S401: Acquire the task flow path and task execution record, filter the task status change data at the approval node, calculate the task stay time at the node, determine the key node where the task status change occurs, obtain the task status change, filter the status change data, and obtain the task status change record; S402: calling the task status change record, calling the optimized task flow path, filtering the execution data of the task at the approval node, calculating the matching degree value between the task status change record and the flow path, filtering the task data whose matching degree exceeds the adjustment threshold, adjusting the data filtering range, removing the redundant data outside the filtering range, and obtaining the task data filtering range; S403: calling the task data screening range, obtaining the task flow data within the screening range, screening the data that meets the status change, calculating the distribution ratio value of the data category, screening the data set that meets the screening conditions according to the status change, and obtaining the task data screening result.

8. The enterprise management process optimization method according to claim 7, characterized in that: The matching degree value calculation formula is specifically: Calculate the matching degree value, filter the task data whose matching degree exceeds the adjustment threshold, adjust the data filtering range, remove the redundant data outside the filtering range, and obtain the task data filtering range; Among them, M match Indicates the matching degree value, R j，act represents the actual state change record value of the jth node, P j，opt represents the preset state change value of the jth node in the optimization path, W j，node represents the weight of the jth node, reflecting the importance of the node in the entire flow path, N nodes Indicates the total number of nodes in the task flow path.

9. The enterprise management process optimization method according to claim 1, characterized in that: The specific steps of obtaining the task data screening result, calling the optimized task flow path, calculating the task status change trend, calling the abnormal task node, and establishing the task execution status backtracking chain are as follows: S501: calling the task data screening result, calling the optimized task flow path, obtaining the task status change record at the approval node, calculating the task status conversion time at the differentiation node, obtaining the status change rate, judging whether there is abnormal fluctuation in the task status change trend, screening the task nodes whose status change rate exceeds the set range, and obtaining the task status change trend; S502: calling the task status change trend, obtaining the approval path of the task in the abnormal fluctuation range, screening the stagnation state of the task in the abnormal path, calculating the stay time of the task in the abnormal path, determining the task nodes whose stay time exceeds the normal range, screening the task nodes whose status changes do not comply with the flow rules, marking the abnormal nodes that exceed the stay time threshold, and obtaining the abnormal task nodes; S503: Call the abnormal task node, obtain the task status of the abnormal task node, screen the state change trajectory of the task before and after the abnormal node, calculate the matching degree between the state change time and the task flow path, establish the task state backtracking path according to the matching degree, connect the abnormal task node and the state change chain, and obtain the task execution state backtracking chain.

10. An enterprise management process optimization system, characterized in that: According to the enterprise management process optimization method according to any one of claims 1 to 9, the system comprises: The task queue module obtains the approval task queue length, average approval time, and number of pending tasks, calls task flow fluctuation to set the node load state coefficient, and generates the load distribution state of the approval node; The task constraint module obtains the deadline and business type of the task, calls the load distribution status of the approval node to calculate the task time constraint level, sets the task flow priority adjustment value, and generates the task flow priority parameter; The path adjustment module obtains the task processing capacity ranking of the approval node based on the load distribution state and the task flow priority parameter, calculates the task path adjustment interval, calls the task flow path weight of the approval node and the task path adjustment interval to reset the approval path of the task, and generates the task flow path; The status screening module obtains the task flow path and task execution record, calls the optimized task flow path to screen the task status change record, and generates a task data screening result; The status backtracking module obtains the task data screening result, calls the optimized task flow path, calculates the task status change trend, and establishes a task execution status backtracking chain.

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