Process modification auxiliary method based on workflow engine

Abstract

The invention relates to a process modification auxiliary method based on a workflow engine. The process modification auxiliary method based on the workflow engine can greatly reduce the risk of process modification and improve the robustness of a service system. Besides, the influence range of process modification can be reflected simply and clearly through the visual flow chart, so that the user can focus on the affected area, and the safety of business circulation is improved. The method has the specific effects that the efficiency and accuracy of process modification are improved, and manual intervention and errors are reduced; the influence range of flow modification is accurately determined through the flow chart, and the modification risk is reduced; automatic modification and simulation test functions ensure stability and reliability after process modification.

Description

Technical Field

[0001] The present invention belongs to the technical field of process optimization, and in particular to a process modification auxiliary method based on a workflow engine. Background Art

[0002] As enterprises expand in size and their business becomes more complex, traditional process management methods can no longer meet the needs of modern enterprises. Workflow engines, as an important tool for managing, automating, and optimizing enterprise processes, are increasingly being used. However, in actual applications, process modification and optimization often face many challenges, such as cumbersome modification processes, error-prone processes, and difficulty in determining the scope of impact.

[0003] For example, CN114819553A discloses a dynamic configuration method based on a workflow engine, which discloses modifying a process according to process configuration information to obtain a workflow node; however, this modification of the process does not determine the scope of the modification, nor can it provide modification suggestions for the process modification. Therefore, the flexibility and robustness of this process modification method based on a workflow engine have certain defects. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art and propose a process modification assistance method based on a workflow engine, which helps users to modify and optimize business processes efficiently and accurately in an automated and intelligent manner, and at the same time accurately determines the impact scope of the process modification in combination with a flowchart.

[0005] The present invention solves the technical problem by adopting the following technical solutions:

[0006] A workflow engine-based process modification auxiliary method comprises the following steps:

[0007] S1: Process analysis, obtain the process and conduct a comprehensive analysis of the existing process, where the process includes the process structure, task nodes, trigger conditions and execution data, generate a detailed flowchart, and display each link and logical relationship of the process;

[0008] S2: Intelligent recommendation of process modification, based on the results of the process analysis module, using machine learning algorithms and expert knowledge base to recommend process modification solutions to users;

[0009] S3: Determine the scope of impact, and analyze the scope of impact of the recommended modification plan in combination with the flowchart; identify the links that may be affected around the modification point and evaluate their degree of impact; mark the affected area on the flowchart;

[0010] S4: Automated modification process: According to the modification plan confirmed by the user, the process is automatically modified in the workflow engine; during the modification process, it is ensured that the links not marked as affected areas are not affected;

[0011] S5: Simulation test and verification: simulate the modified process to verify whether the modification effect meets expectations, pay special attention to the links in the affected area to ensure their normal operation;

[0012] S6: Process publishing and monitoring: publish the modified process to the workflow engine, monitor the execution of the process in real time, and ensure that the process can run normally in actual applications.

[0013] Moreover, the process in S1 is obtained through robotic process automation and process recorder.

[0014] Furthermore, the S2 comprises the following steps:

[0015] S2.1: Process analysis;

[0016] S2.2: Application of machine learning algorithms;

[0017] S2.3: Expert knowledge base support;

[0018] S2.4: Implementation and evaluation of recommended solutions.

[0019] Furthermore, the S3 determines the impact range of the process modification through the matrix method and the circle extrapolation method.

[0020] Moreover, the specific implementation method of S4 is: automatically modify the process, according to the process modification suggestions recommended in the above steps, the user selects and confirms them, automatically parses the process definition, identifies the affected area, automatically modifies, verifies and updates, including the following steps:

[0021] S4.1: Parsing process definition:

[0022] S4.2: Identify the impact area:

[0023] S4.3: Apply the modification plan.

[0024] Moreover, the specific implementation method of S5 is as follows: Combine the above steps to test the modified process, automatically write test cases according to the process definition and the configuration files of the process nodes, and then test the modified business process, focusing on the data flow; among them, write different test cases according to different process routing conditions to determine whether the operation of the process meets the user's expectations, and use simulation tools or test environments to verify the correctness of the modification. Check whether the modification has achieved the expected effect, and confirm that the links not marked as affected areas are not affected.

[0025] Moreover, the specific implementation method of S6 is: process publishing and monitoring, updating the process definition that has passed the test in S5 to the workflow engine, modifying the database involved, and reloading the configuration file; and continuously paying attention to the operation of the business to ensure that the modified process can operate normally, and performing rollback and alarm operations when problems occur.

[0026] The advantages and positive effects of the present invention are:

[0027] The process modification auxiliary method based on the workflow engine of the present invention can significantly reduce the risk of process modification and improve the robustness of the business system; in addition, the visual flowchart can concisely and clearly reflect the scope of influence of the process modification, allowing users to focus on the affected area and improve the security of business flow. The specific effects include the following: improving the efficiency and accuracy of process modification, reducing manual intervention and errors; accurately determining the scope of influence of process modification through flowcharts, reducing modification risks; automated modification and simulation test functions ensure the stability and reliability of the modified process. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 Flowchart of the present invention DETAILED DESCRIPTION

[0029] The present invention is further described in detail below with reference to the accompanying drawings.

[0030] In the power industry, financial management involves many complex processes, such as electricity bill settlement, cost control, and fund scheduling. The efficient operation of these processes is directly related to the economic benefits and operational efficiency of the enterprise. Traditional manual or semi-automated process management methods have problems such as low efficiency, prone to errors, and difficult adjustments. Therefore, the introduction of automated management tools based on workflow engines, combined with intelligent analysis and recommendation technology, has become an important means to optimize the financial processes of the power industry.

[0031] like Figure 1 As shown, the process modification auxiliary method and system based on the workflow engine involved in this application are introduced in detail below through the financial related business of the power industry.

[0032] S1: Process Analysis

[0033] For a specific financial process in the power industry (such as the "monthly electricity bill settlement process"), first obtain all relevant data of the process through the system interface, including process definition files, historical execution records, task node configurations, etc. Use graphical tools to automatically generate a detailed flowchart that clearly shows each link, task node, data flow, and logical judgment conditions from the beginning to the end of the process. At the same time, key data fields, processing logic, and other information in the process are stored in the database in the form of metadata, providing a basis for subsequent analysis.

[0034] In S1, automatic acquisition of business processes can be achieved through a variety of technical means, including Robotic Process Automation (RPA) and Process Recorder. Robotic Process Automation (RPA) is an automation technology that uses software robots to simulate human operations on computers to automatically perform repetitive, rule-based tasks. RPA robots can perform tasks such as logging into the system, copying and pasting data, processing Excel and emails, and filling out forms. Process Recorder is a tool that records manual operations in business processes by monitoring users' keyboard and mouse operations. Based on these records, the process recorder can generate process definition documents and analysis reports so that business analysts and process developers can better understand business details.

[0035] S2: Intelligent Recommendation for Process Modification

[0036] Based on the flowchart and metadata generated in step S1, combined with the professional knowledge and historical data of financial management in the power industry, machine learning algorithms (such as decision trees, random forests, etc.) are used to analyze the bottlenecks, high-frequency error points and potential optimization space in the process. At the same time, the knowledge base of financial experts in the power industry is introduced, and algorithm recommendations and expert experience are combined to provide users with customized process modification solutions. For example, for the "invoice verification" node in the electricity bill settlement process, it is recommended to introduce OCR (optical character recognition) technology to automatically compare invoice information and reduce manual review time.

[0037] In S2, artificial intelligence is used to intelligently recommend process modification suggestions, including the following means:

[0038] S2.1 Process Analysis

[0039] Data collection: First, collect various data related to the business process, including process execution time, error rate, resource usage, user feedback, etc.

[0040] Data analysis: Use statistical analysis, data mining and other methods to conduct in-depth analysis of the collected data to identify bottlenecks, inefficiencies and potential problems in the process.

[0041] Problem location: Based on the data analysis results, accurately locate the problems in the process, such as long node processing time, inaccurate condition judgment, unreasonable trigger setting, etc.

[0042] S2.2 Application of machine learning algorithms

[0043] Model training: Using historical data and expert-labeled samples, machine learning models are trained to identify patterns in processes, predict process performance, and identify potential opportunities for improvement.

[0044] Intelligent recommendation: Based on the prediction results of the machine learning model and the problem location of the process analysis module, it intelligently recommends appropriate process modification solutions. These solutions may include but are not limited to node adjustment, condition optimization, trigger setting, etc.

[0045] Node adjustment: It is recommended to remove unnecessary nodes, merge similar nodes, or reorder nodes to improve process efficiency.

[0046] Condition optimization: Based on the results of data analysis, optimize the conditional judgment logic in the process, reduce misjudgments and missed judgments, and improve process accuracy.

[0047] Trigger setting: It is recommended to set reasonable triggers to automatically start or stop certain links in the process under specific conditions to improve the flexibility and response speed of the process.

[0048] S2.3 Expert knowledge base support

[0049] Knowledge base construction: Build a knowledge base containing industry best practices, historical success cases, and expert experience. These knowledge bases can provide supplementary information and verification basis for machine learning algorithms.

[0050] Decision support: When modifying the plan in the intelligent recommendation process, the recommended plan is verified and optimized by combining the information and experience in the expert knowledge base. This ensures that the recommended plan is in line with industry standards and is both operational and practical.

[0051] S2.4 Implementation and evaluation of recommended solutions

[0052] Solution implementation: Implement the recommended process modification plan to transform and optimize business processes.

[0053] Effect evaluation: Verify the effectiveness of the recommended solution by continuously monitoring and evaluating process performance. If problems or deficiencies are found, make timely adjustments and improvements.

[0054] Continuous optimization: Based on the evaluation results and new business needs, we continuously adjust and optimize the recommended process modification plan to adapt to the ever-changing market environment and business needs.

[0055] Intelligent process modification recommendations combine process analysis modules, machine learning algorithms, and expert knowledge bases to provide users with scientific, practical, and actionable process improvement suggestions. This recommendation method helps companies improve the efficiency and effectiveness of business processes, reduce operating costs, and increase customer satisfaction and competitiveness.

[0056] S3: Determine the scope of impact

[0057] After recommending a modification plan, the system automatically highlights the modification point and its direct and indirect impact links on the flowchart. Graph theory algorithms are used to calculate the impact path, assess the importance of each affected link and the risks that the modification may bring. Through color coding or annotation, the impact range is intuitively displayed on the flowchart to help users quickly understand the consequences of the modification.

[0058] In S3, the impact scope of process modification can be determined through matrix method, circle extrapolation method, etc.

[0059] Although the circle extrapolation method was originally used mainly for traffic impact analysis, its idea based on distance and correlation can be flexibly applied to other fields. Taking the financial management business of the power industry as an example, the use of the circle extrapolation method to obtain the area of ​​influence includes the following steps:

[0060] S3.1 Circle division:

[0061] Inner circle: the links that are directly affected by the modification of the financial management process, such as the step where the modification point is located and its upstream and downstream steps, such as the change in the processing flow of a specific account in accounting.

[0062] Outer circle: indirectly affected links, which may not be directly connected to the modification points, but due to the integrity and correlation of financial management, their operation will also be affected, such as fund allocation, cost control and other related processes.

[0063] S3.2 Assessment of impact:

[0064] For each link in the circle, evaluate the specific impact of the modification of the financial management process on it. This can be done by analyzing the changes in financial indicators, the extension or reduction of process processing time, the increase or decrease of error rate, etc.

[0065] Use quantitative or qualitative methods to measure the impact and ensure the objectivity and accuracy of the evaluation results.

[0066] S3.3 Determine the boundaries of the impact area:

[0067] According to the set impact threshold and evaluation results, determine which links are within the impact range. If the impact of a link exceeds the set threshold, it will be included in the impact range.

[0068] It is important to note that the boundaries of the impact range may not be fixed. As process modifications progress and the effects become apparent, the impact range may need to be reassessed and adjusted.

[0069] Use special colors or symbols to mark the affected areas on the flowchart, such as red for major impact areas and yellow for minor impact areas. Use flowcharts to show users the areas affected by process modifications, which need to be focused on during subsequent testing, verification, and execution of new business processes.

[0070] S4: Automated modification process

[0071] After the user confirms the modification plan, the system automatically executes the modification operation in the workflow engine according to the preset script or API interface. During the modification process, the system adopts a transaction management mechanism to ensure the atomicity and consistency of the modification operation. At the same time, a protection strategy is implemented for links that are not marked as affected areas to prevent unexpected changes.

[0072] In S4, the process is automatically modified. According to the process modification suggestions recommended in the previous steps, the user selects and confirms them, automatically parses the process definition, identifies the affected area, and automatically modifies, verifies and updates. The specific steps include the following.

[0073] S4.1 Parsing process definition:

[0074] Use the tools or APIs provided by the workflow engine to load and parse existing process definitions.

[0075] Convert process definitions into internal data structures or object models for programmatic manipulation.

[0076] S4.2 Identify the impact area:

[0077] Based on the results of the preliminary analysis, mark the affected links in the process definition.

[0078] Make sure these labels match those in the flowchart.

[0079] S4.3 Application modification plan:

[0080] Write automation scripts or programs to modify the process definition according to the modification plan.

[0081] Modifications may include adding, deleting, modifying nodes, paths, conditions, etc.

[0082] During the modification process, logical judgment is used to ensure that only the links marked as the affected area are affected.

[0083] According to the process configuration file, the function corresponding to the process node is automatically modified, including modification of parameters, process routing conditions, etc.; and the trigger is redefined.

[0084] S5: Simulation test and verification

[0085] After the modification is completed, the system automatically starts the simulation test environment to simulate the process operation in the real scenario. Through the preset test cases and randomly generated test data, the modification effect is fully verified. Special attention is paid to the links in the affected area to ensure that their logic is correct, the data is correct, and the performance meets the standards. During the test, the system records detailed logs to facilitate problem tracking and resolution.

[0086] In S5, simulate the test and verify the process modification content. Combined with the previous steps, test the modified process. Automatically write test cases based on the process definition and the configuration files of the process nodes, and then test the modified business process, focusing on the data flow; among them, write different test cases according to different process routing conditions to determine whether the operation of the process meets user expectations. Use simulation tools or test environments to verify the correctness of the modification. Check whether the modification has achieved the expected effect, and confirm that the links not marked as affected areas are not affected.

[0087] S6: Process Release and Monitoring

[0088] After the test verification, the system will publish the modified process to the official workflow engine and update the configuration and permission settings of the relevant systems. At the same time, the real-time monitoring function is enabled to monitor the execution of the process in all aspects. Including but not limited to task execution status, processing time, exception records, etc. Through the dashboard, email notification or mobile application, the process operation status is reported to the relevant personnel in a timely manner to ensure that the process can run efficiently and stably in actual applications.

[0089] In S6, the process is published and monitored. The process definition that passed the test in step S5 is updated to the workflow engine. At the same time, the database involved is modified, and the configuration file is reloaded. And the operation of the business is continuously monitored to ensure that the modified process can operate normally. At the same time, operations such as rollback and alarm are performed when problems occur.

[0090] In addition, the present application also relates to a process modification auxiliary system based on a workflow engine, which includes the following modules:

[0091] Process analysis module: conducts a comprehensive analysis of the existing process, including process structure, task nodes, trigger conditions, execution data, etc., generates a detailed flowchart, and displays each link and logical relationship of the process.

[0092] Process modification intelligent recommendation module: Based on the results of the process analysis module, it uses machine learning algorithms and expert knowledge base to recommend appropriate process modification plans for users; the recommended modification plans include but are not limited to node adjustment, condition optimization, trigger setting, etc.

[0093] Determine the impact scope module: Combined with the flowchart, analyze the impact scope of the recommended modification plan; identify the links around the modification point that may be affected and evaluate their impact level; mark the impact area on the flowchart so that users can intuitively understand the impact scope of the modification.

[0094] Automatic process modification module: automatically modify the process in the workflow engine according to the modification plan confirmed by the user; during the modification process, ensure that the links not marked as affected areas are not affected.

[0095] Simulation test and verification module: Conduct simulation tests on the modified process to verify whether the modification effect meets expectations, pay special attention to the links in the affected area, and ensure their normal operation.

[0096] Process publishing and monitoring module: Publish the modified process to the workflow engine, monitor the execution of the process in real time, and ensure that the process can run normally in actual applications.

[0097] Preferably, the process analysis module also implements the following functions. Automatic acquisition of business processes can be achieved through a variety of technical means, including robotic process automation (RPA) and process recorders. Robotic Process Automation (RPA) is an automation technology that uses software robots to simulate human operations on computers to automatically perform repetitive, rule-based tasks. RPA robots can perform tasks such as logging into the system, copying and pasting data, processing Excel and emails, and filling out forms. Process Recorder is a tool that records manual operations in business processes by monitoring the user's keyboard and mouse operations. Based on these records, the process recorder can generate process definition documents and analysis reports so that business analysts and process developers can better understand the business details.

[0098] Preferably, the process modification intelligent recommendation module also implements the following functions. Using artificial intelligence means to intelligently recommend process modification suggestions also includes the following means:

[0099] S2.1 Process Analysis

[0100] Data collection: First, collect various data related to the business process, including process execution time, error rate, resource usage, user feedback, etc.

[0101] Data analysis: Use statistical analysis, data mining and other methods to conduct in-depth analysis of the collected data to identify bottlenecks, inefficiencies and potential problems in the process.

[0102] Problem location: Based on the data analysis results, accurately locate the problems in the process, such as long node processing time, inaccurate condition judgment, unreasonable trigger setting, etc.

[0103] S2.2 Application of machine learning algorithms

[0104] Model training: Using historical data and expert-labeled samples, machine learning models are trained to identify patterns in processes, predict process performance, and identify potential opportunities for improvement.

[0105] Intelligent recommendation: Based on the prediction results of the machine learning model and the problem location of the process analysis module, it intelligently recommends appropriate process modification solutions. These solutions may include but are not limited to node adjustment, condition optimization, trigger setting, etc.

[0106] Node adjustment: It is recommended to remove unnecessary nodes, merge similar nodes, or reorder nodes to improve process efficiency.

[0107] Condition optimization: Based on the results of data analysis, optimize the conditional judgment logic in the process, reduce misjudgments and missed judgments, and improve process accuracy.

[0108] Trigger setting: It is recommended to set reasonable triggers to automatically start or stop certain links in the process under specific conditions to improve the flexibility and response speed of the process.

[0109] S2.3 Expert knowledge base support

[0110] Knowledge base construction: Build a knowledge base containing industry best practices, historical success cases, and expert experience. These knowledge bases can provide supplementary information and verification basis for machine learning algorithms.

[0111] Decision support: When modifying the plan in the intelligent recommendation process, the recommended plan is verified and optimized by combining the information and experience in the expert knowledge base. This ensures that the recommended plan is in line with industry standards and is both operational and practical.

[0112] S2.4 Implementation and evaluation of recommended solutions

[0113] Solution implementation: Implement the recommended process modification plan to transform and optimize business processes.

[0114] Effect evaluation: Verify the effectiveness of the recommended solution by continuously monitoring and evaluating process performance. If problems or deficiencies are found, make timely adjustments and improvements.

[0115] Continuous optimization: Based on the evaluation results and new business needs, we continuously adjust and optimize the recommended process modification plan to adapt to the ever-changing market environment and business needs.

[0116] Intelligent process modification recommendations combine process analysis modules, machine learning algorithms, and expert knowledge bases to provide users with scientific, practical, and actionable process improvement suggestions. This recommendation method helps companies improve the efficiency and effectiveness of business processes, reduce operating costs, and increase customer satisfaction and competitiveness.

[0117] Preferably, the module for determining the impact range also implements the following functions: The impact range of the process modification can be determined by using a matrix method, a circle extrapolation method, or the like.

[0118] Taking the financial management business of the power industry as an example, the following steps are briefly explained to obtain the area of ​​influence using the circle extrapolation method:

[0119] S3.1 Circle division:

[0120] Inner circle: the links that are directly affected by the modification of the financial management process, such as the step where the modification point is located and its upstream and downstream steps, such as the change in the processing flow of a specific account in accounting.

[0121] Outer circle: indirectly affected links, which may not be directly connected to the modification points, but due to the integrity and correlation of financial management, their operation will also be affected, such as fund allocation, cost control and other related processes.

[0122] S3.2 Assessment of impact:

[0123] For each link in the circle, evaluate the specific impact of the modification of the financial management process on it. This can be done by analyzing the changes in financial indicators, the extension or reduction of process processing time, the increase or decrease of error rate, etc.

[0124] Use quantitative or qualitative methods to measure the impact and ensure the objectivity and accuracy of the evaluation results.

[0125] S3.3 Determine the boundaries of the impact area:

[0126] According to the set impact threshold and evaluation results, determine which links are within the impact range. If the impact of a link exceeds the set threshold, it will be included in the impact range.

[0127] It is important to note that the boundaries of the impact range may not be fixed. As process modifications progress and the effects become apparent, the impact range may need to be reassessed and adjusted.

[0128] Use special colors or symbols to mark the affected areas on the flowchart, such as red for major impact areas and yellow for minor impact areas. Use flowcharts to show users the areas affected by process modifications, which need to be focused on during subsequent testing, verification, and execution of new business processes.

[0129] Preferably, the automatic process modification module also implements the following functions: Automatic process modification, based on the process modification suggestions recommended in the above steps, the user selects and confirms them, automatically parses the process definition, identifies the affected area, automatically modifies, verifies and updates. The specific steps include the following.

[0130] S4.1 Parsing process definition:

[0131] Use the tools or APIs provided by the workflow engine to load and parse existing process definitions.

[0132] Convert process definitions into internal data structures or object models for programmatic manipulation.

[0133] S4.2 Identify the impact area:

[0134] Based on the results of the preliminary analysis, mark the affected links in the process definition.

[0135] Make sure these labels match those in the flowchart.

[0136] S4.3 Application modification plan:

[0137] Write automation scripts or programs to modify the process definition according to the modification plan.

[0138] Modifications may include adding, deleting, modifying nodes, paths, conditions, etc.

[0139] During the modification process, logical judgment is used to ensure that only the links marked as the affected area are affected.

[0140] According to the process configuration file, the function corresponding to the process node is automatically modified, including modification of parameters, process routing conditions, etc.; and the trigger is redefined.

[0141] Preferably, the simulation test and verification module also implements the following functions: simulate and verify the process modification content. Combined with the above steps, test the modified process. Automatically write test cases based on the process definition and the configuration files of the process nodes, and then test the modified business process, focusing on the data flow; wherein, write different test cases according to different process routing conditions to determine whether the operation of the process meets user expectations. Use simulation tools or test environments to verify the correctness of the modifications. Check whether the modifications have achieved the expected results, and confirm that the links not marked as affected areas are not affected.

[0142] Preferably, the process publishing and monitoring module also implements the following functions: process publishing and monitoring, updating the process definition that has passed the test in the above steps to the workflow engine, and modifying the database involved, reloading the configuration file, etc. And continue to pay attention to the operation of the business to ensure that the modified process can operate normally, and perform operations such as rollback and alarm when problems occur.

[0143] Embodiment 2:

[0144] Taking the procurement process of a certain power company as an example, the process includes multiple links such as demand proposal, approval, procurement, and acceptance. Through the process modification auxiliary method of the present invention, the process analysis is first performed and a flow chart is drawn. Subsequently, the intelligent recommendation module recommends changing the approval link from serial to parallel based on the analysis results to improve the approval efficiency.

[0145] In the impact scope determination module, the flowchart is used to identify the subsequent links that may be affected by the modification of the approval link, such as the execution order and triggering conditions of the procurement link. These impact areas are marked on the flowchart and their impact is evaluated.

[0146] After the user confirms the modification plan, the automated modification module automatically completes the modification in the workflow engine. During the modification process, ensure that other links that are not marked as affected areas are not affected. Finally, verify the modification effect through simulation testing to ensure that the process optimization can improve the approval efficiency and does not affect the normal operation of other links.

[0147] It should be emphasized that the embodiments described in the present invention are illustrative rather than restrictive. Therefore, the present invention includes but is not limited to the embodiments described in the specific implementation manner. Any other implementation manners derived by those skilled in the art based on the technical solution of the present invention also fall within the scope of protection of the present invention.

Claims

1. A process modification auxiliary method based on a workflow engine, characterized in that: Includes the following step: S1: Process analysis, obtain the process and conduct a comprehensive analysis of the existing process, where the process includes the process structure, task nodes, trigger conditions and execution data, generate a detailed flowchart, and display each link and logical relationship of the process; S2: Intelligent recommendation of process modification, based on the results of the process analysis module, using machine learning algorithms and expert knowledge base to recommend process modification solutions to users; S3: Determine the scope of impact, and analyze the scope of impact of the recommended modification plan in combination with the flowchart; identify the links that may be affected around the modification point and evaluate their degree of impact; mark the affected area on the flowchart; S4: Automated modification process: According to the modification plan confirmed by the user, the process is automatically modified in the workflow engine; during the modification process, it is ensured that the links not marked as affected areas are not affected; S5: Simulation test and verification: simulate the modified process to verify whether the modification effect meets expectations, pay special attention to the links in the affected area to ensure their normal operation; S6: Process publishing and monitoring: publish the modified process to the workflow engine, monitor the execution of the process in real time, and ensure that the process can run normally in actual applications.

2. The process modification auxiliary method based on workflow engine according to claim 1 is characterized in that: The process in S1 is obtained through robotic process automation and process recorder.

3. The process modification auxiliary method based on workflow engine according to claim 1 is characterized in that: The S2 comprises the following steps: S2.1: Process analysis; S2.2: Application of machine learning algorithms; S2.3: Expert knowledge base support; S2.4: Implementation and evaluation of recommended solutions.

4. The process modification auxiliary method based on workflow engine according to claim 1 is characterized in that: The S3 determines the impact scope of the process modification through the matrix method and the circle extrapolation method.

5. The process modification auxiliary method based on workflow engine according to claim 1 is characterized in that: The specific implementation method of S4 is: automatically modify the process, according to the process modification suggestions recommended in the above steps, the user selects and confirms them, automatically parses the process definition, identifies the affected area, automatically modifies, verifies and updates, including the following steps: S4.1: Parsing process definition: S4.2: Identify the impact area: S4.3: Apply the modification plan.

6. The process modification auxiliary method based on workflow engine according to claim 1 is characterized in that: The specific implementation method of S5 is as follows: Combine the above steps to test the modified process, automatically write test cases according to the process definition and the configuration files of the process nodes, and then test the modified business process, focusing on the data flow; write different test cases according to different process routing conditions, judge whether the operation of the process meets the user's expectations, and use simulation tools or test environments to verify the correctness of the modification. Check whether the modification has achieved the expected effect, and confirm that the links not marked as affected areas are not affected.

7. The process modification auxiliary method based on workflow engine according to claim 1 is characterized in that: The specific implementation method of S6 is: process publishing and monitoring, updating the process definition that has passed the test in S5 to the workflow engine, modifying the database involved, and reloading the configuration file; And continue to pay attention to the operation of the business to ensure that the modified process can operate normally, and perform rollback and alarm operations when problems arise.

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