CN118761745A

CN118761745A - OA collaborative workflow optimization method applied to enterprise

Info

Publication number: CN118761745A
Application number: CN202411246463.6A
Authority: CN
Inventors: 侯雨晨; 章凤琳; 张陈; 张�杰; 刘露露
Original assignee: Hefei Hanjiu Technology Co ltd
Current assignee: Hefei Hanjiu Technology Co ltd
Priority date: 2024-09-06
Filing date: 2024-09-06
Publication date: 2024-10-11

Abstract

The invention discloses an optimization method applied to an OA collaborative workflow of an enterprise, which relates to the technical field of workflow collaboration and specifically comprises the following steps: acquiring condition information for switching the approval task from the conventional approval process to the advanced approval process under the condition that the condition information exceeds the threshold value is detected; analyzing the acquired condition information, evaluating whether the approval task needs to be switched from the conventional approval process to the advanced approval process, and determining whether the process switching operation of the approval task needs to be executed according to the evaluation result; switching the approval task from the conventional approval process to the advanced approval process according to the evaluation result, and dynamically adjusting the approval path in the switching process; and aiming at the flow switching of each approval task, determining an optimal approval path, and adjusting the task priority in the advanced approval flow based on the switching management measure of the path. The invention solves the problem of untimely switching of the approval path, ensures the efficient switching of the flow and improves the approval accuracy.

Description

OA collaborative workflow optimization method applied to enterprise

Technical Field

The invention relates to the technical field of workflow collaboration, in particular to an optimization method applied to an OA collaborative workflow of an enterprise.

Background

Collaborative workflow refers to the process of completing business activities by orderly dividing work, information sharing and task collaboration around a common goal by multiple departments or teams in an enterprise. The aim of the collaborative workflow is to improve cross-department collaboration efficiency and ensure smooth information flow, thereby realizing more efficient team collaboration. The collaborative workflow is optimized to eliminate redundant links, reduce human intervention, and improve automation level to cope with the complexity of the workflow, and reduce time and resource waste. The optimization is applied to the OA system of the enterprise, so that the flow operation can be further standardized and automated, and the overall operation efficiency, response speed and working experience of staff of the enterprise are improved.

The existing optimization technology applied to the OA collaborative work flow of the enterprise mainly improves the work efficiency through flow combing and reconstruction, intelligent approval, automatic task allocation, data-driven decision making, user experience optimization and multi-platform integration. Firstly, redundant steps are removed and standardized and templated flows are designed through business flow analysis, so that different teams can operate consistently and efficiently. Then, by means of a rule engine and AI technology, automatic approval and intelligent task allocation can reduce manual intervention and optimize resource allocation. Real-time data monitoring and analysis helps identify flow bottlenecks, and the flow is continuously improved by adjusting key links. Meanwhile, the optimized UI design simplifies user operation, and combines mobile office support and instant messaging tool integration, so that staff can efficiently cooperate anytime and anywhere. Finally, the technologies cooperate to form an integrated flow optimization system, so that the efficiency and flexibility of the OA system of the enterprise in management cooperation are comprehensively improved.

The prior art has the following defects:

In an enterprise OA system, when the approval amount exceeds a specific threshold, the approval process needs to be switched from the conventional approval process to the advanced approval process, and the situation that the approval task cannot be correctly switched to the advanced process may occur, because on a condition judgment node, the system fails to monitor and accurately update the state in real time, so that the approval state stays in the original conventional process path when the process is switched, but the prior art fails to transfer the approval task to the correct advanced process in time, so that the approval process is stopped and the approval task cannot be timely distributed to the advanced approval staff, finally, the approval progress of the high-amount item is delayed, the approval decision is wrong, the business risk is increased, and meanwhile, the process correction and operation cost are additionally increased, and the overall operation efficiency and business continuity of the enterprise are affected.

The above information disclosed in the background section is only for enhancement of understanding of the background of the disclosure and therefore it may include information that does not form the prior art that is already known to a person of ordinary skill in the art.

Disclosure of Invention

The invention aims to provide an optimization method applied to an OA collaborative workflow of an enterprise, so as to solve the problems in the background art.

In order to achieve the above object, the present invention provides the following technical solutions: the OA collaborative workflow optimization method applied to the enterprise specifically comprises the following steps:

in the approval process of the enterprise OA system, monitoring whether the approval amount in the approval task exceeds a set threshold in real time, and acquiring condition information for switching the approval task from the conventional approval process to the advanced approval process under the condition that the approval amount exceeds the threshold is detected;

analyzing the acquired condition information, evaluating whether the approval task needs to be switched from the conventional approval process to the advanced approval process, and determining whether the process switching operation of the approval task needs to be executed according to the evaluation result;

Switching the approval task from the conventional approval process to the advanced approval process according to the evaluation result, and dynamically adjusting the approval path in the switching process;

Aiming at the flow switching of each approval task, an optimal approval path is determined, and task priority adjustment in the advanced approval flow is performed based on switching management measures of the path;

And generating a flow optimization management report by monitoring and recording the execution data of the adjusted approval task flow, and providing the flow optimization management report for a system administrator and business decision-making personnel.

Preferably, the method includes analyzing the acquired condition information, evaluating whether the approval task needs to be switched from the conventional approval process to the advanced approval process, and determining whether the process switching operation of the approval task needs to be executed according to the evaluation result, wherein the method specifically comprises the following steps:

preprocessing the acquired condition information for switching the approval task from the conventional approval process to the advanced approval process;

extracting performance measurement information and path topology information in the preprocessed condition information;

Analyzing performance metric information and path topology information in the extracted preprocessed condition information to respectively generate a task priority adjustment coefficient and a path complexity index;

And constructing a switching evaluation model by using the generated task priority adjustment coefficient and the path complexity index, generating a switching evaluation coefficient, comparing the generated switching evaluation coefficient with a preset switching evaluation coefficient threshold value, evaluating whether the approval task needs to be switched from a conventional approval process to an advanced approval process according to the comparison result, and determining whether the process switching operation of the approval task needs to be executed according to the evaluation result.

Preferably, the task priority adjustment coefficient and the path complexity index acquiring logic are as follows:

Extracting performance measurement information in the preprocessed condition information, wherein the performance measurement information specifically comprises approval amount, approval time and task integrity rate when the approval task is approved for a plurality of times in a historical time period, and the approval amount, the approval time and the task integrity rate are respectively calibrated as follows AndRepresenting the first in the history periodThe approval amount when the approval task is approved for the second time,Representing the first in the history periodThe approval time when the approval task is approved again,Representing the first in the history periodThe task integrity rate when the examination and approval task is examined and approved again,，Is a positive integer;

The task priority adjustment coefficient is calculated, and a specific calculation formula is as follows:

In the method, in the process of the invention, Adjusting the coefficient for the task priority;

Extracting path topology information in the preprocessed condition information, specifically including the number of approval nodes, the number of approval people layer levels and the approval sequence dependence coefficient in the approval path when the approval task is approved for a plurality of times in a historical time period, and calibrating the path topology information as respectively AndRepresenting the first in the history periodThe number of approval nodes in the approval path when the approval task is approved for the second time,Representing the first in the history periodThe number of levels of approvers in the approval path when the approval task is approved for the second time,Representing the first in the history periodThe approval sequence in the approval path depends on the coefficient when the approval task is approved for the second time,，Is a positive integer;

The path complexity index is calculated by the following specific calculation formula:

In the method, in the process of the invention, Is a path complexity index.

Preferably, the task priority adjustment coefficient to be generatedAnd path complexity indexConstructing a switching evaluation model, and generating switching evaluation coefficients through weighted summationAnd evaluate the generated switching coefficientWith a preset handover evaluation coefficient thresholdComparing, namely evaluating whether the approval task needs to be switched from the conventional approval process to the advanced approval process according to the comparison result, and determining whether the process switching operation of the approval task needs to be executed according to the evaluation result, wherein the specific comparison analysis is as follows:

If it is The approval task is not required to be switched from the conventional approval process to the advanced approval process, and the process switching operation of the approval task is not required to be executed;

If it is If the approval task needs to be switched from the conventional approval process to the advanced approval process, a process switching operation of the approval task needs to be performed.

Preferably, the approval task is switched from the conventional approval process to the advanced approval process according to the evaluation result, and the approval path is dynamically adjusted in the switching process, which specifically comprises: when the evaluation result is that the approval task needs to be switched from the conventional approval process to the advanced approval process, executing the process switching operation of the approval task; in the switching process, dynamically adjusting the approval paths, including rearranging the order of approval nodes, determining the execution order among the nodes, distributing approval roles according to the authority corresponding to the nodes and adjusting the path dependency relationship.

Preferably, for each process switching of approval tasks, an optimal approval path is determined, and task priority adjustment in an advanced approval process is performed based on a switching management measure of the path, and the method specifically comprises the following steps:

extracting path complexity information and task execution efficiency information of all approval paths of each approval task in the process of performing flow switching, and preprocessing after extracting;

analyzing path complexity information and task execution efficiency information of all the preprocessed approval paths, and respectively generating path adaptability coefficients and task matching degree indexes of all the approval paths;

constructing a path selection evaluation model by using the generated path adaptability coefficients and task matching degree indexes of each examination and approval path, generating path selection evaluation coefficients of each examination and approval path, comparing the generated path selection evaluation coefficients of each examination and approval path with a preset path selection evaluation coefficient threshold value, and dividing each examination and approval path into a preferred examination and approval path and a non-preferred examination and approval path according to the comparison result;

constructing a set of path selection evaluation coefficients of all approval paths divided into preferred approval paths according to the size sequence, and determining the optimal approval path;

Based on the determined optimal approval path, adjusting task priority in the advanced approval process, including rearranging approval node sequences, determining execution orders among nodes, assigning approval roles of corresponding authorities, and adjusting path dependency relationships.

Preferably, the logic for acquiring the path adaptability coefficient and the task matching degree index of each approval path is as follows:

Extracting path complexity information of all approval paths of each approval task in the process of flow switching, specifically including the number of nodes, average approval time and approval passing rate of each approval path of a plurality of approval tasks in the process of flow switching in a historical time period, and calibrating the path complexity information as respectively AndRepresenting the first time period in the history periodThe second approval task is the first one in the process of flow switchingThe number of nodes in the individual approval paths,Representing the first time period in the history periodThe second approval task is the first one in the process of flow switchingAverage approval time in individual approval paths,Representing the first time period in the history periodThe second approval task is the first one in the process of flow switchingThe approval passing rate in the individual approval paths,，AndAre all positive integers;

The path adaptability coefficient of each approval path is calculated, and a specific calculation formula is as follows:

In the method, in the process of the invention, Represent the firstPath adaptability coefficients of the individual approval paths;

Extracting task execution efficiency information of all approval paths of each approval task in the process of flow switching, specifically including task processing time length, task delay rate and approval role processing efficiency of each approval path of a plurality of approval tasks in the process of flow switching in a historical time period, and calibrating the task processing time length, the task delay rate and the approval role processing efficiency as follows respectively AndRepresenting the first time period in the history periodThe second approval task is the first one in the process of flow switchingThe duration of task processing in the individual approval paths,Representing the first time period in the history periodThe second approval task is the first one in the process of flow switchingTask delay rate in individual approval paths,Representing the first time period in the history periodThe second approval task is the first one in the process of flow switchingThe efficiency of the approval character processing in the individual approval paths,，A positive integer;

the task matching degree index of each approval path is calculated, and a specific calculation formula is as follows:

In the method, in the process of the invention, Is the firstTask matching index of each approval path.

Preferably, the path adaptability coefficients of the respective approval paths to be generatedIndex of matching degree with taskConstructing a path selection evaluation model, and generating path selection evaluation coefficients of each approval path through weighted summationAnd the generated path selection evaluation coefficients of each examination and approval pathWith a preset path selection evaluation coefficient threshold valueComparing, and dividing each approval path into a preferred approval path and a non-preferred approval path according to a comparison result, wherein the specific dividing process is as follows:

If it is Dividing the approval path into preferred approval paths;

If it is The approval path is divided into non-preferred approval paths.

Preferably, the path selection evaluation coefficients of all the approval paths divided into the preferred approval paths are calculatedConstructing sets in order of sizeAggregation of，And taking the approval path corresponding to the maximum value of the path selection evaluation coefficient in all the approval paths divided into the preferred approval paths as the optimal approval path.

In the technical scheme, the invention has the technical effects and advantages that:

1. The invention can trigger the path switching in time when the condition changes by monitoring the approval amount in real time and judging whether the approval amount exceeds the preset threshold value. The invention describes the specific steps of acquiring the condition information and evaluating the path switching, in particular to the method for constructing a switching evaluation model by preprocessing and analyzing the condition information and generating a task priority adjustment coefficient and a path complexity index. The process ensures that the system can dynamically make decisions according to actual conditions, avoids the approval detention problem caused by untimely status updating, and fundamentally improves the accuracy and timeliness of flow switching. The technical effect not only optimizes the path selection, but also solves the problem that the approval task cannot be smoothly transited under the critical condition.

2. In the path switching process, the invention realizes the dynamic adjustment of the approval process by rearranging the order of the approval nodes, adjusting the dependency relationship among the nodes and distributing proper authority. Meanwhile, for each flow switching, the invention further introduces a path selection evaluation model, determines an optimal path through a path adaptability coefficient and a task matching degree index, and performs sequencing and selection according to the path selection evaluation coefficient. The path selection method based on quantitative analysis not only improves the scientificity and reliability of decision making, but also ensures that the system can select the path most suitable for the current task from a plurality of candidate paths, thereby reducing approval delay, reducing error distribution and improving the overall efficiency of approval tasks.

3. The invention also generates a flow optimization management report by monitoring and recording the flow data of the approval task after adjustment, and provides decision support based on data for system administrators and business decision-making staff. The report contains the actual execution effect analysis after approval process adjustment, such as key indexes of process execution efficiency, success rate of path switching, task completion time and the like. By means of the analysis results, the manager can better evaluate the actual effect of flow optimization, and further adjust and optimize the flow optimization to form a closed-loop management system. The continuous optimization based on the data not only improves the intelligent degree of the system, but also ensures that the approval process is always high-efficiency, stable and controllable in dynamic change, and effectively reduces the business risk and the operation cost.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to these drawings for those skilled in the art.

FIG. 1 is a flow chart of the invention applied to an optimization method of an OA collaborative workflow of an enterprise.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.

The invention provides an optimization method for an OA collaborative workflow of an enterprise as shown in FIG. 1, which specifically comprises the following steps:

The method for monitoring whether the approval amount exceeds the set threshold value in the approval task in real time is specifically implemented as follows: in an OA system of an enterprise, monitoring whether the approval amount in the approval task exceeds a set threshold in real time can be achieved by the following ways: first, the system continuously monitors data fields related to the amount of money in each approval task by setting a condition trigger based on a rule engine. When the approval task enters the approval process, the system automatically captures and reads the value of the monetary field and compares the value with a preset threshold. In particular, the system periodically polls the running approval task or uses an event-driven monitoring mechanism, and the system automatically checks the current monetary value whenever the approval task is entered or the monetary field is changed. Meanwhile, the system can utilize a data stream processing technology (such as a technology based on a message queue or a stream processing framework) to process and analyze the inflow amount data in real time so as to ensure that each approved amount can be instantly compared with a set threshold value. When the amount of money exceeds the threshold value, a flow switching condition judging module is triggered to provide a basis for subsequent flow adjustment. The process can enable the system to flexibly adjust the judging conditions to adapt to the requirements of different business scenes by configuring the parameterized threshold range.

When the condition information is detected to exceed the threshold value, the mode for switching the approval task from the conventional approval process to the advanced approval process, and the specific acquisition and condition information content are acquired: after detecting that the amount exceeds the set threshold, the system needs to acquire condition information for flow switching, where the condition information generally includes priority of approval task, service type related to the task, approval authority level, historical data of the task (such as processing result of similar amount item in the past), flow path dependency relationship, and the like. In particular, the system can call the condition information from the database through the data acquisition and rule judgment module. Firstly, the system extracts historical processing data related to a current approval task through a business rule engine, and calculates the priority and path dependence coefficient of the task according to the data; secondly, the system can inquire the service type related to the task and the approval stage where the current task is located so as to judge whether a higher-level approval path needs to be entered or not. Meanwhile, the system can also determine the necessity of switching according to the approval authority level and the criticality of related services. The condition information acquisition process is usually realized through a series of rule matching, data query and calculation operations, the operations can be automatically completed in a background database, and after the condition information extraction and analysis are completed, the system can generate an evaluation report or a path adjustment suggestion so as to provide basis for the subsequent flow switching decision. The method ensures that the system can carry out comprehensive judgment based on multidimensional conditions when executing flow switching, thereby improving the accuracy and rationality of switching decision.

In this embodiment, the obtained condition information is analyzed to evaluate whether the approval task needs to be switched from the conventional approval process to the advanced approval process, and whether the process switching operation of the approval task needs to be executed is determined according to the evaluation result, which specifically includes the following steps:

The preprocessing of the condition information is performed to ensure accuracy, consistency and computational efficiency of the data in the subsequent analysis and evaluation process. In complex enterprise OA systems, the condition information obtained for switching the approval task flows typically contains a variety of data types and sources, which may be subject to redundancy, noise, or format inconsistencies. Without preprocessing, the data analysis results may be inaccurate, thereby affecting the flow switching decision. The pretreatment mainly comprises the following aspects: firstly, data cleaning is carried out to remove repeated, wrong or invalid condition information, such as incorrect data, null values or entries which do not accord with logic; secondly, carrying out data normalization processing, converting data with different sources or different scales into uniform scales, and ensuring that various data have comparability in the calculation process, for example, normalizing numerical values in different ranges to be between 0 and 1; thirdly, data aggregation is carried out, scattered data items are combined into a more meaningful comprehensive index according to the context or business logic, for example, the same approval task data recorded for a plurality of times are aggregated into an average value or a sum; finally, abnormal data points, such as extreme values or abrupt change data, which may affect the analysis result are identified and processed through an abnormality detection algorithm. The preprocessing steps can be automatically completed through a rule engine and a data processing module which are configured in software, so that processed data is cleaner, standardized and consistent, evaluation parameters can be efficiently and accurately generated during subsequent analysis, and reliable flow switching decisions can be obtained.

The extraction of performance metric information and path topology information from the preprocessed condition information may be implemented by specific software modules and data mining techniques. First, the system classifies the preprocessed data using algorithms based on feature selection and classification, and identifies numerical data (e.g., approval amount, approval time, task completion rate, etc.) related to performance metrics and structured data (e.g., approval nodes, hierarchical relationships, path lengths, etc.) related to path topology. Next, the system matches and maps specific fields in the condition information with performance metric information and path topology information by building a data mapping model. For example, fields related to "amount", "time", "node", etc. are extracted from the preprocessed data using keyword matching or regular expression recognition methods. Then, the statistical characteristics of the fields are further extracted through an aggregation analysis function, and a comprehensive information set is generated. Eventually, the system will group and tag the extracted data for convenient recall and computation in subsequent steps.

Performance metric information refers to a series of quantitative data reflecting the performance of an approval task, which typically includes approval amounts, approval time, task completion rates, and the like. The approval amount can reveal the financial importance of the task, the approval time reflects the efficiency of the flow, and the task completion rate is used for measuring the success rate of the task in the historical data. The information is an important basis for evaluating the task priority, and can help the system determine whether the task needs to be switched from the conventional approval process to the advanced approval process. The path topology information describes the structure and complexity of the approval process, including the length of the approval path (i.e., the number of nodes that pass through), the level of the approvers involved in the path (e.g., the level span from the common approver to the advanced management layer), and the number of dependencies in the path (e.g., the approval result of a node depends on the result of another node). The path topology information is used to evaluate the complexity of the procedure, thereby helping the system identify whether a procedure switch is needed in the case of higher complexity. The information is extracted and analyzed by technical means such as data structuring, topology analysis, association mining and the like, and is an important data base for flow optimization and decision making.

The preset handover evaluation coefficient threshold value can be determined by combining historical data analysis, model training and business requirement analysis. First, the system will collect and sort data for a large number of historical approval tasks, including cases of how the task switches from a regular approval process to a high-level approval process under different conditions. Through machine learning algorithms such as cluster analysis and regression analysis, the system can find rules between switching conditions and successful decisions in the data, and a preliminary evaluation threshold range is generated. Then, the system simulates the switching effect under different thresholds through simulation and experiments, and the evaluation threshold can reach the optimal effect under the conditions of ensuring high-efficiency switching and reducing unnecessary switching through adjustment and optimization. Besides the data driving method, the method can also be used for optimizing in combination with actual service demands, such as human intervention or parameter fine adjustment according to factors such as enterprise risk preference, importance of approval tasks, priority of projects and the like, and finally an optimal threshold meeting service scenes is determined. In the whole process, the system can provide the operation of setting and optimizing the threshold value through the configuration interface and the rule engine, so that the threshold value can continuously reflect the real service requirement in the dynamic adjustment, and the intelligent level of flow optimization is improved.

In this embodiment, the task priority adjustment coefficient and the path complexity index acquiring logic are as follows:

the performance measurement information in the preprocessed condition information can be extracted by means of data mining and data screening, and the specific steps include: firstly, the system classifies and screens the approval task data stored in the historical time period, and identifies fields related to approval amount, approval time and task completion rate. And extracting a plurality of historical records meeting the conditions from the mass data by the system through database query and data filtering algorithm. Next, the system uses feature extraction algorithms to further analyze and integrate these historical data, categorizing numerical data related to approval amounts, approval times, and task completion rates into a set of performance metric information. For example, the system may extract from the database monetary data (e.g., number of ten thousand elements), time spent on approval (e.g., number of hours), and task completion (e.g., percentage of completion) for each approval task by setting screening conditions. After extraction is completed, the system performs standardized processing and data cleaning on the data so as to ensure consistency and accuracy of the data. Eventually, the processed performance metric information will be packaged and stored for later recall in evaluating task priority adjustment coefficients.

in the calculation of the task priority adjustment coefficients, Represent the firstCube root of approval amount in secondary approval task. The processing mode of the cube root can balance the influence of the amount data on the priority, and avoid the extreme influence of excessive or insufficient amount on the calculation result. The cube root operation not only reduces the excessive weight of the large-amount task in calculation, but also avoids the excessive compression of the small-amount task on the whole priority, so that the calculation result is more stable and reasonable.Represent the firstNatural logarithm of approval time of secondary approval task. Natural log processing is used to amplify the role of time factors in priority assessment, especially when time is longer, its impact is more pronounced. At the same time, the operation of adding 1 ensures that mathematical calculation errors do not occur in the case of approval time being zero. This approach allows the more time-consuming tasks to be more fully considered in priority evaluation.Represent the firstInverse of the completion rate of the secondary approval task. By calculating the reciprocal portion of the completion rate, tasks with low completion rates can be prioritized. Tasks with lower completion rates typically mean greater potential risks or problems, and therefore require higher priority in process optimization. This approach ensures that the historical performance of the task is fully accounted for in task priority evaluation.

The size of the task priority adjustment coefficient directly reflects the urgency and importance of the approval task. When the coefficient is larger, the method indicates that the approval amount in the historical approval task is higher, the approval time is longer or the task completion rate is lower, which means that the task has higher risk or business criticality and needs to be processed preferentially. In this case, the system is more inclined to switch the task from a regular approval process to a high-level approval process to ensure that important tasks can be supported by higher priority reviews and decisions. In contrast, when the task priority adjustment coefficient is smaller, the importance of the task is relatively lower, and the task is maintained in the conventional approval process without switching to a more complex process.

Extracting path topology information in the preprocessed condition information, specifically including the number of approval nodes, the number of approval people layer levels and the approval sequence dependence coefficient in the approval path when the approval task is approved for a plurality of times in a historical time period, and calibrating the path topology information as respectivelyAndRepresenting the first in the history periodThe number of approval nodes in the approval path when the approval task is approved for the second time,Representing the first in the history periodThe number of levels of approvers in the approval path when the approval task is approved for the second time,Representing the first in the history periodThe approval sequence in the approval path depends on the coefficient when the approval task is approved for the second time,，Is a positive integer;

The extraction of path topology information in the preprocessed condition information can be realized by means of path analysis and structured data extraction, and the specific steps comprise: firstly, the system analyzes the approval flow data in the historical time period through a path analysis algorithm, and identifies the approved nodes, the participating approval person levels and the sequence dependency relationship among the nodes passing through each approval task. The system firstly extracts log data related to the approval path from the database, records the node sequence of each approval task, and counts the number of approved nodes. Meanwhile, the system analyzes the role hierarchy of the approver and determines the number of levels of the approver layer corresponding to each node. Then, the system identifies the path dependency that needs to be approved by a certain node before entering the next node by analyzing the dependency relationship between the nodes. The system analyzes the sequence dependency relationships in a graph structure or a flow chain table mode, and counts the number of the dependency relationships. The parsed data is normalized and packaged into a path topology information set for use in the subsequent computation of the path complexity index. The process relies on database query, graph structure analysis and data analysis techniques to realize automatic extraction and structuring of path topology information.

In the method, in the process of the invention, Is a path complexity index.

In the calculation of the path complexity index,Represent the firstAnd the square sum of the number of approval nodes and the number of approval role grades in the secondary approval path. The square sum is calculated to amplify the influence of the number of nodes and the number of role levels, so that the number of nodes and the number of approval role layers involved in the path are fully embodied in complexity evaluation. By squaring, the contribution of path length and level complexity to the overall complexity of the flow can be better reflected.Represent the firstAnd adding 1 operation of the number of path dependency relations in the secondary approval path. The 1 is added to avoid causing calculation errors when the number of the dependency relationships is zero, and meanwhile, the operation ensures that the existence of the dependency relationships can be properly reflected in complexity calculation. The more path dependencies, the more complex the decision path that describes the flow, and therefore, greater weight needs to be given when evaluating path complexity.Represent the firstNatural logarithm of the product of the number of approval nodes and the number of approval role grades in the secondary approval path. This approach is used to amplify the nonlinear effects of the path structure, especially when the number of nodes and the number of levels are simultaneously high, which can significantly increase the complexity. The add 1 operation is also to prevent the product from zero causing a calculation error. By logarithmic operation, the hierarchy and structure of the complex path is more intuitively reflected in the complexity index.

The size of the path complexity index reflects the structural complexity of the approval process. When the index is large, it means that the approval path contains more nodes, higher number of levels of approvers or more complex dependency relationships, which means that the process may be difficult to effectively process in the conventional approval path, so that it is required to switch to an advanced approval process to adapt to the complex process management requirement. In this case, the switching procedure helps to avoid bottlenecks or delays in the complex path. In contrast, when the path complexity index is smaller, the approval process is shown to be simpler, and can be effectively completed in the conventional process without switching to the advanced process.

In this embodiment, the task priority adjustment coefficient to be generatedAnd path complexity indexConstructing a switching evaluation model, and generating switching evaluation coefficients through weighted summationAnd evaluate the generated switching coefficientWith a preset handover evaluation coefficient thresholdComparing, namely evaluating whether the approval task needs to be switched from the conventional approval process to the advanced approval process according to the comparison result, and determining whether the process switching operation of the approval task needs to be executed according to the evaluation result, wherein the specific comparison analysis is as follows:

This means that neither the urgency of the approval task nor the complexity of the procedure meets the criteria for switching to an advanced approval procedure, indicating that the task is of relatively low importance or that the procedure is simple. Under the condition, the task is continuously remained in the conventional approval process for processing, so that reasonable allocation of resources can be ensured, and unnecessary process upgrading is avoided. For enterprises, the method is helpful for maintaining the efficient operation of the approval process, avoiding the occupation of the advanced approval process due to unnecessary task switching, and improving the overall approval efficiency. The system can continue to process the task according to the conventional approval path, and does not trigger the flow switching operation, so that the time and labor cost of the advanced flow are saved.

If it isIf the approval task needs to be switched from the conventional approval process to the advanced approval process, a process switching operation of the approval task needs to be performed.

This means that the priority of the approval task or the complexity of the process has reached the standard that requires switching to a high-level approval process, which generally means that the task is of higher importance or the process is more complex. In this case, the task will be switched to an advanced approval process for processing to ensure that a higher level of approval resources and more adequate approval are available. Such decisions help businesses avoid delays or errors in mission-critical processing while ensuring that complex flows are performed in appropriate paths, reducing unnecessary business risks. The method comprises the specific operation that the system triggers task switching, transfers the approval task from a conventional flow to an advanced approval path, and updates the approval path configuration of the task to ensure that subsequent approval loops can be smoothly connected.

Task priority adjustment coefficient to be generatedAnd path complexity indexThe process of constructing the switching evaluation model can be realized in a weighted summation mode so as to comprehensively consider the urgency of the task and the complexity of the approval path and obtain the switching evaluation coefficient. The specific implementation mode is as follows: firstly, the system sets weight coefficients according to different service scenes and actual requirementsAndWhereinRepresenting task priority adjustment coefficientsIs used for the weight of the (c),Representing path complexity indexIs a weight of (2). In general, in the case of a conventional,AndThe setting of (1) is adjusted according to the operation strategy, the risk preference and the resource allocation strategy of the enterprise. For example, if the business is more focused on the importance and urgency of the task,Will be set relatively high and if more attention is paid to the complexity of the approval path and the flow bottlenecks that may occurWill be higher. Next, the system performs a weighted summation of the two parameters as follows: ; in this model, the purpose of the weighted summation is to uniformly quantify the two dimensions of task priority and path complexity to generate a handover evaluation coefficient that comprehensively reflects the task and path characteristics. By adjusting AndThe system can flexibly adapt to different service requirements and scenes, and the intellectualization and the dynamics of flow switching decision are realized. Specially producedAnd comparing the flow switching value with a preset switching evaluation coefficient threshold value to judge whether the flow switching is required to be executed or not, so that the balance efficiency and risk of an enterprise in the flow optimization are ensured.

In this embodiment, the approval task is switched from the conventional approval process to the advanced approval process according to the evaluation result, and the approval path is dynamically adjusted in the switching process, which specifically includes: when the evaluation result is that the approval task needs to be switched from the conventional approval process to the advanced approval process, executing the process switching operation of the approval task; in the switching process, dynamically adjusting the approval paths, including rearranging the order of approval nodes, determining the execution order among the nodes, distributing approval roles according to the authority corresponding to the nodes and adjusting the path dependency relationship.

The flow switching operation of performing approval tasks can be implemented by a dynamic flow reconstruction and path update mechanism. Firstly, after the evaluation result triggers the flow switching, the system moves the approval task out of the current conventional approval flow and relocates to the advanced approval flow according to the result of the switching evaluation model. The specific operation comprises the following steps: the system calls a pre-configured flow path library to retrieve and load the approval path corresponding to the high-level approval flow. Then, the system can migrate the state, the historical processing record and the related data of the current approval task into a new flow path so as to ensure that the information of the task is not lost in the switching process, and the flow is connected without errors. The process is automatically completed through a background task transfer module, the system can redistribute tasks according to the flow definition rules, and the approval path configuration of the tasks is dynamically updated, so that the tasks can be continuously executed in a new flow path. In addition, the system can ensure seamless connection of task node information and approval progress after switching through real-time monitoring, and smooth transition of the flow is realized.

In the process of flow switching, rearranging approval node sequences, determining node execution sequences, distributing approval roles and adjusting path dependency relations are mainly realized through a path optimization algorithm and a permission mapping mechanism. The system firstly uses a topology ordering algorithm or a priority-based node ordering algorithm to reorder the approval nodes in the current path according to the complexity of the advanced approval process and the urgency of the task, so as to ensure that the key nodes are processed preferentially in the process. Then, the system determines the execution sequence among the nodes through a logic judgment mechanism, and adjusts the sequence and the execution condition of the nodes according to the dependency relationship among the nodes and the business logic of the tasks so as to avoid the problems of circular dependency or deadlock in the path. In order to ensure the execution effect of the nodes, the system can automatically match the node authorities, and according to the node functions and task requirements, approval roles with corresponding authorities are selected from the authority database to be distributed. Finally, the system adjusts the dependency relationship in the paths according to the rearranged node sequence and execution conditions, so that the connection of the front node and the rear node is more logical and coherent, the flow execution efficiency is optimized, the smooth switching of approval tasks among different paths is ensured, and the problems of delay or repeated processing caused by unreasonable paths are avoided.

in this embodiment, for each process switching of approval tasks, an optimal approval path is determined, and task priority adjustment in an advanced approval process is performed based on a switching management measure of the path, and specifically includes the following steps:

In the process of flow switching, the system can extract path complexity information and task execution efficiency information of all approval paths through a path analysis module and a data mining tool. Firstly, the system automatically analyzes each path in the examination and approval process, and extracts the actual examination and approval node number, the dependency relationship among the nodes, the average examination and approval time of each node, the historical examination and approval passing rate and other specific quantitative data contained in the path. These data are typically stored in a log database of the enterprise OA system, from which the system extracts complexity and efficiency data associated with each path by querying information sources such as historical approval records, approval path profiles, and task execution logs. In order to ensure the real-time performance and accuracy of the data, the system uses a data synchronization and update mechanism to ensure that the extracted data reflects the current service condition. The extracted data are packaged into two major categories of path complexity information and task execution efficiency information for subsequent evaluation and analysis.

The extracted path complexity information and task execution efficiency information are preprocessed to ensure the integrity, accuracy and consistency of data, so that the reliability of subsequent evaluation and analysis is improved. In practice, the extracted data may have problems of redundancy, errors, or inconsistencies, such as repeated recordings, null values, format inconsistencies, and the like. These problems, if left untreated, can lead to inaccurate evaluation results, affecting the correctness of the path selection. The pretreatment step typically includes the following aspects: firstly, cleaning data, and removing repeated records, error data and null values; secondly, data standardization processing is carried out, and data (such as time, quantity, percentage and the like) with different scales are converted into a unified standard format, so that comparability in subsequent calculation is ensured; then, abnormal value detection and correction are carried out, and possible extreme values or unreasonable data are identified and processed; finally, data aggregation is performed, and the dispersed records are combined into a representative statistical value, such as an average value or a median value. The preprocessed data is clearer and structured, and a solid foundation is provided for accurate calculation of a subsequent path evaluation model.

The preset path selection evaluation coefficient threshold value can be determined by combining historical data analysis and model optimization, and the specific implementation method is as follows: firstly, the system can collect and analyze data of a large number of historical approval tasks, including key performance indexes such as success rate, processing time length and delay condition of the tasks on different approval paths. Through a machine learning algorithm, such as regression analysis or cluster analysis, the system can identify the association between the evaluation coefficients of different paths and the success rate of the task, so as to preliminarily determine the optimal evaluation coefficient range of various tasks under different paths. Then, the system evaluates the path selection results under different thresholds through simulation and emulation, optimizes and fine-tunes the thresholds, so as to ensure the accuracy and effectiveness of path selection under different service scenes. Finally, the system combines the actual business requirements and the risk management strategies, and manually inspects and regularly updates the threshold value after model optimization to ensure that the threshold value is consistent with the operation targets and strategies of enterprises all the time. The whole process is realized through a data analysis module, a model training module and a parameter configuration interface in software, so that the path selection evaluation coefficient threshold can be dynamically adapted to different scenes and service changes.

In this embodiment, the logic for obtaining the path adaptability coefficient and the task matching degree index of each approval path is as follows:

The path complexity information of all approval paths of the approval task in the process of flow switching is extracted, and the method is mainly realized through a data recording and analyzing module in an approval flow management system. The method comprises the specific operation that the system extracts key quantitative data of each approval path, such as the number of nodes, average approval time and approval passing rate, through automatic analysis and screening of flow data in a historical approval log. Firstly, the system analyzes the nodes of different approval paths, counts the number of actual nodes contained in each path, and records the average duration of node execution. Next, the system calculates the average approval passing rate, typically expressed in percent, for all nodes in each path by querying the historical data. In order to ensure the accuracy of the data, the system applies data cleaning and outlier detection algorithms to perform standardized processing and optimization on the extracted data, and finally structured path complexity information is formed.

The magnitude of the path adaptation coefficient reflects the balance between the complexity and execution efficiency of each approval path. The larger the coefficient, the more complex and more adaptive the path is in terms of node number, approval time, approval passing rate, etc., which means that the path is more superior in handling more complex tasks. When a specific approval path is selected, the path with higher path adaptability coefficient is more suitable for processing approval tasks with high task complexity and multiple dependency relations, and the structure of the path can better cope with complex approval scenes, so that smooth operation of the flow is ensured. Therefore, in the path selection process, the system prioritizes paths with larger path adaptability coefficients to ensure robustness in the mission-critical processing.

Extracting task execution efficiency information of all approval paths of each approval task in the process of flow switching, specifically including task processing time length, task delay rate and approval role processing efficiency of each approval path of a plurality of approval tasks in the process of flow switching in a historical time period, and calibrating the task processing time length, the task delay rate and the approval role processing efficiency as follows respectivelyAndRepresenting the first time period in the history periodThe second approval task is the first one in the process of flow switchingThe duration of task processing in the individual approval paths,Representing the first time period in the history periodThe second approval task is the first one in the process of flow switchingTask delay rate in individual approval paths,Representing the first time period in the history periodThe second approval task is the first one in the process of flow switchingThe efficiency of the approval character processing in the individual approval paths,，A positive integer;

The task execution efficiency information of all approval paths of the approval task in the process of flow switching is extracted, and the task execution efficiency information is mainly realized through a historical task data analysis and role performance tracking module. The system firstly extracts the processing time length of each approval task in each approval path from the task execution log, specifically comprises the processing start time and the processing end time of each node, and obtains the processing time length by calculating the time difference. Then, the system calculates the delay rate of the task by analyzing the delay condition in the task execution, for example, whether the task exceeds a preset processing time limit. For approval character processing efficiency, the system quantifies task processing speed and decision accuracy of approval characters in historical data under different paths, and is generally measured by average response time and completion quality of the approval characters in processing similar tasks. The extracted data is subjected to data cleaning and normalization processing to form standardized task execution efficiency information for subsequent path evaluation and optimization operation. The whole process is automatically realized in software through a data analysis and task performance module, and the real-time performance and accuracy of information are ensured.

The magnitude of the task matching degree index reflects the historical performance and adaptability of each approval path when processing a specific task. The larger the index, the lower delay rate and higher processing efficiency are provided when the path processes similar tasks historically, which indicates that the path is highly matched with the requirements of the current task. When a specific approval path is selected, a path with a higher task matching index means that the path has higher success rate and efficiency when processing similar tasks. Therefore, the system can preferentially select a path with higher task matching degree index so as to ensure that the current task can be completed quickly and accurately, and the approval efficiency and the consistency of task processing are improved.

In this embodiment, the path adaptability coefficients of the generated approval paths are calculatedIndex of matching degree with taskConstructing a path selection evaluation model, and generating path selection evaluation coefficients of each approval path through weighted summationAnd the generated path selection evaluation coefficients of each examination and approval pathWith a preset path selection evaluation coefficient threshold valueComparing, and dividing each approval path into a preferred approval path and a non-preferred approval path according to a comparison result, wherein the specific dividing process is as follows:

If it is Dividing the approval path into preferred approval paths;

Meaning that the approval path meets the expected standard in terms of path adaptability and task matching degree, and has excellent performance. In this case, the path is divided into preferred approval paths, and becomes one of the best path candidates. This means that the path has the best condition and efficient execution capability for processing the current approval task, and the efficiency and accuracy of the flow can be improved to the maximum extent. The method has the effect that the system can give priority to the preferred approval paths, so that the reliability of decision making and the success rate of tasks are improved in the subsequent path selection, and the approval tasks are ensured to be completed quickly on the suitable paths.

If it isThe approval path is divided into non-preferred approval paths.

Meaning that the approval path does not meet the expected standard in terms of path adaptability or task matching degree, there is a disadvantage. In this case, the path is divided into non-preferred approval paths, excluding the optimal path selection. This means that the path may be at risk of inefficiency, delay or error in processing the current task, and its structure and performance do not guarantee the smooth progress of the approval task. The influence of the method is that the paths are not considered as candidate paths any more, so that the possibility of flow stagnation or failure caused by selecting unsuitable paths in the approval process is reduced, and the robustness and reliability of the whole approval system are improved.

In this embodiment, all the approval paths divided into the preferred approval paths are evaluated for path selectionConstructing sets in order of sizeAggregation of，And taking the approval path corresponding to the maximum value of the path selection evaluation coefficient in all the approval paths divided into the preferred approval paths as the optimal approval path.

The aim of this is to precisely select the approval path most suitable for the current task by selecting the size of the evaluation coefficient from a plurality of preferred approval paths. Preferred approval paths are screened based on path adaptability coefficients and task matching degree indexes, and among the preferred paths, paths with the largest path selection evaluation coefficients tend to have higher efficiency, stability and adaptability, so that the method can be more excellent in terms of processing complex tasks, ensuring smooth approval flows and reducing delays and errors. By ordering the preferred paths according to the evaluation coefficients, the system can more intuitively determine which path is most suitable for executing the task in the current scene. By the aid of the method, accuracy and reliability of flow decision are improved, subjectivity and uncertainty in path selection by manpower are avoided, and accordingly the system is ensured to select an optimal path in a self-adaptive mode when facing diversified approval tasks, and approval efficiency maximization, business risk minimization and resource utilization optimization are achieved.

The system can collect various key data of the approval task in the execution process in real time through the integrated flow monitoring module. The data comprise processing time of the task, approval passing rate of each node, task delay condition, execution efficiency change after path selection and the like. The monitoring module automatically captures each operation detail in the approval process by using log tracking, data acquisition and sensor technology, and records and stores key data in real time. These data may be aggregated into a data warehouse, which is cleaned, filtered and standardized to form a structured dataset, ensuring the accuracy and reliability of subsequent analysis.

After the execution data is collected and consolidated, the system further processes the data through a data analysis module. The specific operation includes multi-dimensional analysis of bottlenecks, delay nodes, efficiency improvement after path adjustment, and the like in the flow execution. The system may use data visualization techniques to present the analysis results in the form of graphs, trend lines, and indicators summaries. The generated flow optimization management report generally comprises the contents of approval task completion rate, approval efficiency change, task processing time distribution, effect evaluation of flow adjustment and the like. The report can be automatically generated and provided in the form of PDF, excel or on-line dashboard, which is convenient for system administrators and business decision-making personnel to review. The system can also provide a self-defined report function, so that an administrator can select data dimension and analysis angle according to specific requirements, and customize a personalized report.

The purpose of generating the flow optimization management report is to provide data-driven decision support for system administrators and business decision-makers. By recording and analyzing the adjusted approval process execution data, an administrator can intuitively see the change before and after process adjustment, know the running efficiency of the current process, discover potential problems and optimize in time. The analysis based on the data can effectively help a decision maker evaluate the improvement effect of the flow, identify the low-efficiency links in the flow and formulate a more accurate optimization strategy, thereby improving the overall approval efficiency and the flow compliance. In addition, by periodically generating reports, the enterprise can be helped to form a visual performance evaluation system, the continuous optimization of the business process is promoted, and the enterprise is ensured to keep a competitive advantage in a dynamic environment.

The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.

The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired or wireless means (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.

It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

In the several embodiments provided in the present application, it should be understood that the disclosed system and method may be implemented in other manners. For example, the embodiments described above are merely illustrative, e.g., the division of the elements is merely a logical functional division, and there may be additional divisions in actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other forms.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.

The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. The OA collaborative workflow optimization method applied to the enterprise is characterized by comprising the following steps of:

aiming at the flow switching of each approval task, an optimal approval path is determined, and task priority adjustment in the advanced approval flow is performed based on switching management measures of the path, and the method specifically comprises the following steps:

Based on the determined optimal approval path, adjusting task priority in an advanced approval process, including rearranging approval node sequences, determining execution sequences among nodes, distributing approval roles of corresponding authorities and adjusting path dependency relationships;

2. The method for optimizing an OA collaborative workflow of an enterprise according to claim 1, wherein the method for optimizing the OA collaborative workflow of the enterprise analyzes the acquired condition information, evaluates whether the approval task needs to be switched from a normal approval process to an advanced approval process, and determines whether the flow switching operation of the approval task needs to be executed according to the evaluation result, and specifically comprises the steps of:

3. The optimization method for the enterprise OA collaborative workflow of claim 2, wherein the task priority adjustment factor and path complexity index acquisition logic is as follows:

，

In the method, in the process of the invention, Is a path complexity index.

4. The optimization method for OA collaborative workflow in an enterprise according to claim 3 wherein the generated task priority adjustment coefficientsAnd path complexity indexConstructing a switching evaluation model, and generating switching evaluation coefficients through weighted summationAnd evaluate the generated switching coefficientWith a preset handover evaluation coefficient thresholdComparing, namely evaluating whether the approval task needs to be switched from the conventional approval process to the advanced approval process according to the comparison result, and determining whether the process switching operation of the approval task needs to be executed according to the evaluation result, wherein the specific comparison analysis is as follows:

5. The method for optimizing an OA collaborative workflow for an enterprise according to claim 4, wherein the method for switching the approval task from the conventional approval process to the advanced approval process according to the evaluation result and dynamically adjusting the approval path during the switching process comprises: when the evaluation result is that the approval task needs to be switched from the conventional approval process to the advanced approval process, executing the process switching operation of the approval task; in the switching process, dynamically adjusting the approval paths, including rearranging the order of approval nodes, determining the execution order among the nodes, distributing approval roles according to the authority corresponding to the nodes and adjusting the path dependency relationship.

6. The optimization method for the OA collaborative workflow of the enterprise of claim 5, wherein the path adaptability coefficients and task matching index for each approval path are obtained by logic comprising:

，

7. The optimization method for OA collaborative workflow in an enterprise according to claim 6 wherein the path adaptation coefficients for each validation path generated areIndex of matching degree with taskConstructing a path selection evaluation model, generating path selection evaluation coefficients of all the approval paths through weighted summation, and generating the path selection evaluation coefficients of all the approval pathsWith a preset path selection evaluation coefficient threshold valueComparing, and dividing each approval path into a preferred approval path and a non-preferred approval path according to a comparison result, wherein the specific dividing process is as follows:

If it is Dividing the approval path into preferred approval paths;

If it is The approval path is divided into non-preferred approval paths.

8. The optimization method for OA collaborative workflow in an enterprise of claim 7, wherein all approval paths divided into preferred approval paths are evaluated for path selectionConstructing sets in order of sizeAggregation of，And taking the approval path corresponding to the maximum value of the path selection evaluation coefficient in all the approval paths divided into the preferred approval paths as the optimal approval path.