Simulation method and device of workflow, equipment and storage medium

Abstract

The invention provides a workflow simulation method and device, equipment and a storage medium, and the method comprises the steps: firstly obtaining a simulation parameter inputted by a user, and calling a pre-trained machine learning model to carry out the prediction of an execution risk of the simulation parameter; then, the simulation parameters are adaptively adjusted according to the execution risk, and a simulation environment is configured according to the adjusted simulation parameters; next, a workflow engine is called to execute a workflow in the simulation environment according to a preset simulation route, a flow monitor is dynamically injected in the execution process of the workflow engine, current flow data is recorded by adopting a logic snapshot, and the current flow data is marked as logic deletion; and finally, after the workflow is executed, summarizing the flow data and generating a simulation report, and updating the simulation parameters based on a comparison result of the simulation report and a preset standard. The problem that normal services are possibly interfered when an existing workflow is verified and simulated is solved.

Description

Technical Field

[0001] The present invention relates to the field of workflow simulation, and in particular to a workflow simulation method, device, equipment and storage medium. Background Art

[0002] During the implementation of a workflow management system, ensuring that process definitions meet launch requirements is crucial. Verification of configured processes is often required, including analysis of step-by-step process personnel, the flow of conditional branches, step-by-step form control, and the accuracy of step-by-step feedback records. However, existing technologies for process verification suffer from inefficiencies and interference with normal business operations due to the process data generated during verification, which can affect the production environment.

[0003] In view of this, this application is filed. Summary of the Invention

[0004] The present invention discloses a workflow simulation method, device, equipment and storage medium, aiming to solve the problem that normal business may be interfered with during existing workflow verification simulation.

[0005] A first embodiment of the present invention provides a workflow simulation method, comprising:

[0006] Obtain simulation parameters input by the user, and call a pre-trained machine learning model to predict execution risks of the simulation parameters;

[0007] Adaptively adjusting the simulation parameters according to the execution risk, and configuring the simulation environment according to the adjusted simulation parameters;

[0008] Invoking a workflow engine to execute a workflow in the simulation environment according to a preset simulation route, dynamically injecting a process listener into the execution process of the workflow engine, and using a logical snapshot to record current process data, wherein the current process data is marked as logically deleted;

[0009] After executing the workflow, the process data is aggregated and a simulation report is generated, and the simulation parameters are updated based on a comparison result between the simulation report and a preset standard.

[0010] Preferably, the simulation parameters input by the user include: initiator, handler, number of executions, link configuration information, conditional branch rules and environment adaptation parameters.

[0011] Preferably, the pre-trained machine learning model is a supervised learning model trained based on historical workflow data, and the supervised learning model is used to evaluate the execution risk of simulation parameters, and the execution risk includes node blocking, abnormal jump or approval bottleneck.

[0012] Preferably, the injection nodes of the process listener include: process node entry, node completion, task allocation or abnormal interruption.

[0013] Preferably, it also includes:

[0014] When the simulation is submitted, if the process fails to find the handler, copy person or supervisor of the next link, the process engine will package this information and return it to the process simulation call. The simulation call will parse the returned information to find the content that requires human-computer interaction, update the process data according to the simulation operation parameters configured in the simulation operation settings, and continue to submit the updated process data to the process engine for processing.

[0015] A second embodiment of the present invention provides a workflow simulation device, comprising:

[0016] An execution risk prediction unit, configured to obtain simulation parameters input by a user and invoke a pre-trained machine learning model to predict execution risks for the simulation parameters;

[0017] a simulation environment configuration unit, configured to adaptively adjust the simulation parameters according to the execution risk, and configure the simulation environment according to the adjusted simulation parameters;

[0018] A workflow execution unit is configured to call a workflow engine to execute a workflow in the simulation environment according to a preset simulation route, dynamically inject a process listener into the execution process of the workflow engine, and use a logical snapshot to record current process data, wherein the current process data is marked as logically deleted;

[0019] The simulation parameter updating unit is used to summarize the process data and generate a simulation report after executing the workflow, and update the simulation parameters based on the comparison result between the simulation report and the preset standard.

[0020] A third embodiment of the present invention provides a workflow simulation device, including a memory and a processor, wherein the memory stores a computer program, and the computer program can be executed by the processor to implement a workflow simulation method as described in any one of the above.

[0021] A fourth embodiment of the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program can be executed by a processor of a device where the computer-readable storage medium is located to implement a workflow simulation method as described in any one of the above items.

[0022] Based on the workflow simulation method, device, equipment and storage medium provided by the present invention, the simulation parameters input by the user are first obtained, and a pre-trained machine learning model is called to predict the execution risk of the simulation parameters; then, the simulation parameters are adaptively adjusted according to the execution risk, and the simulation environment is configured according to the adjusted simulation parameters; then, the workflow engine is called to execute the workflow according to the preset simulation route in the simulation environment, and a process listener is dynamically injected during the execution of the workflow engine, and a logical snapshot is used to record the current process data, wherein the current process data is marked as logically deleted; finally, after the workflow is executed, the process data is summarized and a simulation report is generated, and the simulation parameters are updated based on the comparison results of the simulation report and the preset standards. The problem that the existing workflow verification simulation may interfere with normal business is solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 1 is a flow chart of a method for simulating a workflow provided by a first embodiment of the present invention;

[0024] Figure 2 This is a module diagram of a workflow simulation device provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0026] In order to better understand the technical solution of the present invention, the embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0027] It should be understood that the embodiments described are only a portion of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative work are within the scope of protection of the present invention.

[0028] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The singular forms "a", "an", "the" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise.

[0029] It should be understood that the term "and / or" as used herein is merely a description of the relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0030] The word "if," as used herein, may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to the determination" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.

[0031] The "first" and "second" mentioned in the embodiments are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It is understood that the specific order or precedence of "first" and "second" can be interchanged where appropriate. It should be understood that the objects distinguished by "first" and "second" can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein.

[0032] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0033] The present invention discloses a workflow simulation method, device, equipment and storage medium, aiming to solve the problem that normal business may be interfered with during existing workflow verification simulation.

[0034] A first embodiment of the present invention provides a workflow simulation method, which can be executed by a workflow simulation device (hereinafter referred to as the simulation device), and in particular, by one or more processors in the simulation device, to implement at least the following steps:

[0035] S101, obtaining simulation parameters input by a user, and calling a pre-trained machine learning model to predict execution risks of the simulation parameters;

[0036] In this embodiment, the simulation device can be a terminal with data processing capabilities such as a desktop computer, laptop computer, workstation, server, etc. The simulation device can be installed with a corresponding operating system and application software, and the functions required by this embodiment can be realized through the combination of the operating system and application software.

[0037] In a possible implementation of the present invention, the simulation device first obtains the simulation parameters input by the user, and these parameters are set through the parameter configuration interface of the simulation page. The simulation parameters input by the user specifically include: initiator, handler, number of executions, link configuration information, conditional branch rules, and environment adaptation parameters. Among them, the initiator parameter is used to simulate the applicant initiation process of different positions; the handler parameter is used to specify the actual processing personnel of each link of the process; the number of executions parameter is used to set the number of times the simulation is repeated, so as to facilitate the discovery of probabilistic problems; the link configuration information includes the start execution link, the pause execution link, and the end execution link, etc., which can be used to skip the intermediate links that do not need to be simulated to speed up the simulation process; the conditional branch rules are used to verify the correctness of the branch selection of the process under different conditions; and the environment adaptation parameters are used to ensure the adaptability of the simulation in the actual production environment.

[0038] After obtaining the above-mentioned simulation parameters, the simulation device calls a pre-trained machine learning model to perform a predictive execution risk assessment on the simulation parameters. The pre-trained machine learning model is a supervised learning model trained based on historical workflow data. By analyzing a large amount of historical workflow execution data, it learns various types of problems that may occur during the workflow execution process and their characteristic patterns. This supervised learning model is specifically used to evaluate the execution risks of simulation parameters, including potential problems such as node blocking, abnormal jumps or approval bottlenecks. It should be noted that by using a pre-trained machine learning model to predict the risks of simulation parameters, potential problems can be identified before the simulation is executed, avoiding the inefficiency of traditional methods that require multiple trial and error to discover problems.

[0039] Node blocking risk refers to the risk that, under specific parameter configurations, a workflow link may be unable to proceed due to improper configuration of the handler, insufficient permissions, or incorrect condition settings. Abnormal jump risk refers to the possibility of unexpected flow paths during process execution, such as incorrect flow caused by conditional branch logic errors. Approval bottleneck risk refers to the situation where process processing efficiency is severely reduced due to specific link configurations, such as delays that may occur in complex approval processes with multiple signatures. Risk prediction results are visualized on the simulation page, including risk level, probability of occurrence, and possible impact range.

[0040] S102, adaptively adjusting the simulation parameters according to the execution risk, and configuring the simulation environment according to the adjusted simulation parameters;

[0041] In this embodiment, after the simulation device performs a risk assessment on the simulation parameters, it will automatically adjust the simulation parameters according to the detected execution risks to optimize the simulation effect and improve the accuracy of process verification. When the simulation device finds that a specific parameter combination may lead to risks such as node blocking, abnormal jumps or approval bottlenecks, it will trigger the adaptive adjustment mechanism. Specifically, the adaptive adjustment mechanism first analyzes the root cause of the risk. For example, when it is detected that there is a risk of node blocking in a specific link, the simulation device will analyze whether the handler configuration, authority settings and preconditions of the node are reasonable. If the risk caused by improper handler configuration is identified, the simulation device will automatically recommend a suitable alternative handler from the organizational structure and provide this as an adjustment suggestion to the user. The user can choose to accept the simulation device's suggestions for automatic adjustment, or manually modify the parameters.

[0042] To address the risk of abnormal jumps caused by conditional branches, the simulation device analyzes the logical relationships of the conditional expressions, identifies conditional settings that may lead to logical conflicts or infinite loops, and provides optimized conditional expressions. In this embodiment, this not only involves syntax correction but also involves verifying the rationality of business logic to ensure that the process flows according to the expected path under various conditions.

[0043] For approval bottleneck risks, the simulation device will calculate the expected processing time and resource usage of each link. When a link is identified as a potential bottleneck, the simulation device will recommend measures such as adjusting the parallel processing strategy of the link, delegating approval authority, or adding backup approvers to improve the overall efficiency of the process.

[0044] Furthermore, the adaptive adjustment mechanism also considers the impact of environmental factors on process execution. For example, in high-concurrency scenarios, the simulator will recommend increasing the number of executions to test the stability of the process under high load. In complex business scenarios, the simulator will recommend expanding the level of detail in the configuration information to ensure that all edge cases are fully tested.

[0045] Based on the adjusted simulation parameters, the simulation device automatically configures the simulation environment. This includes assigning appropriate role permissions for the adjusted handler parameters, preparing corresponding test data for the modified conditional branches, and setting appropriate monitoring points for the optimized process path. During the simulation environment configuration process, the simulation device ensures that all necessary resources are correctly initialized, including form data, user permissions, and system status.

[0046] S103, calling a workflow engine to execute a workflow in the simulation environment according to a preset simulation route, dynamically injecting a process listener into the execution process of the workflow engine, and using a logical snapshot to record current process data, wherein the current process data is marked as logically deleted;

[0047] After the simulation environment is configured, the simulation device executes the process simulation by invoking the actual workflow engine rather than a dedicated simulation flow. Using the exact same workflow engine as the production environment ensures the authenticity and reliability of the simulation results, avoiding potential behavioral differences between the simulation-specific engine and the production engine. It's important to note that the process simulation and the workflow engine are two independent components. The process simulation dynamically injects a process listener by invoking the workflow engine, recording the entire workflow engine execution process in real time.

[0048] As the workflow engine executes the workflow along a pre-set simulation route, process listeners are dynamically injected into the workflow engine's execution. These listeners are triggered at key points in the process's execution, including node entry, node completion, task assignment, and abnormal interruption. Through these listeners, the simulation device can fully capture every aspect of the process's execution, enabling comprehensive monitoring of the workflow's operational status.

[0049] During process execution, the system uses logical snapshot technology to record current process data. These snapshots record key data such as the status of each process node, form data, and information about the person handling the process. Form data snapshots generated by each stage can be viewed by switching between stages, and the correctness of form control information at each stage can be verified by switching between pending and completed tasks. This snapshot mechanism allows users to easily view data changes at each stage and verify that form control at each stage of the process meets expectations.

[0050] Furthermore, in this embodiment, all process data generated through simulation is marked for logical deletion, which ensures that the simulation data does not interfere with actual business data. Users will not see the simulation data in the process pending or completed data. When simulating in an online environment, there will be no impact on user data. This logical deletion mechanism overcomes the limitation of using an isolated environment in traditional process verification, allowing simulation to be performed directly in the production environment, greatly improving the authenticity of verification.

[0051] The process listener design employs a multi-level injection node strategy, monitoring key aspects of the process. The process node entry monitoring point captures events when the process reaches a new node, recording the node's basic information and arrival time. The node completion monitoring point captures events when a node completes processing, recording the processing result and completion time. The task assignment monitoring point captures events when a task is assigned to a specific handler, recording the task assignment logic and results. The exception interruption monitoring point captures exceptions during process execution, recording the cause of the exception and related data.

[0052] When a process encounters a situation requiring human-computer interaction during execution, and the process cannot find the next step's handler, copy recipient, or supervisor during simulation submission, the process engine encapsulates this information and returns it to the process simulation caller. After parsing this information requiring human-computer interaction, the simulation caller updates the process data based on the simulation run parameters configured in the simulation run settings and submits it to the process engine for further processing. This automates human-computer interaction during simulation, enabling simulation testing of complex processes without manual intervention.

[0053] S104 , after executing the workflow, the process data is aggregated and a simulation report is generated, and the simulation parameters are updated based on a comparison result between the simulation report and a preset standard.

[0054] After the workflow simulation is complete, the simulation device automatically collects and summarizes all process data. This data includes comprehensive information such as the actual execution path of the process, processing time at each node, changes in form data, handler analysis, and conditional branch selection results. Specifically, the simulation report records the handler analysis of each link, and verifies whether the handler analysis results in the report are consistent with the expected results of the process link configuration. The handling process in the report records the handling opinions of the handlers at each link, which can be used to verify the accuracy of the information recorded. The simulation device integrates this information to generate a comprehensive simulation report.

[0055] The simulation report adopts a multi-level structure. The first level displays basic information and overall execution results of the process simulation, including total execution time, number of flow nodes, and whether it was successfully completed. The second level details the execution status of each link in the process, including the analysis results of the handlers at each link, processing time, processing opinions, and form data changes. The third level focuses on the analysis of key nodes and abnormal situations, such as the judgment basis of conditional branches and the change history of form fields. This allows users to analyze process execution from different dimensions.

[0056] After generating a simulation report, the simulator compares and analyzes it against pre-set standards. These standards may include the expected execution path of the process, the expected processing time range for each node, the expected trend of changes in form fields, and the expected results of the handler's analysis. For example, for a procurement approval process, the pre-set standards may stipulate that if the purchase amount exceeds 100,000 yuan, the process must go through the general manager's approval process; the processing time for each approval step should not exceed 24 hours; and the handler for a specific step should be the applicant's direct supervisor.

[0057] By comparing simulation reports with pre-set standards, the simulation equipment can identify deviations and issues in process execution. For example, if the actual handler at a certain stage differs from the expected one, it may indicate an error in the handler parsing rule configuration; if the actual selection result of a conditional branch differs from the expected one, it may indicate a logical problem in the conditional expression; if the processing time at a certain stage far exceeds expectations, it may indicate an efficiency bottleneck in that stage.

[0058] Provide data support for further optimization. Based on this simulation report, the simulation device uses an embedded feedback adjustment algorithm to automatically update and adjust the original simulation parameters (specifically: the simulation device first extracts key performance indicators from the simulation report, such as the execution time of each process node, the consistency of approval opinions, the correctness of branch selection, and the frequency of abnormal events. These indicators are compared with pre-set ideal standards or thresholds to calculate errors or deviations. Subsequently, the feedback adjustment algorithm weightedly integrates these errors and uses a strategy similar to gradient descent or PID controller to calculate the adjustment range for each simulation parameter (including initiator, handler, number of executions, link configuration, conditional branch rules, etc.).

[0059] It's important to note that these adjustments involve not only basic parameters like the initiator, handler, and number of executions, but also configuration details and conditional branching rules for each process step, making the entire simulation environment adaptive and continuously optimized. Feedback-based parameter updates not only enable dynamic monitoring of process execution effectiveness, but also overcome the limitations of traditional simulation methods' fixed parameter configurations. By continuously correcting and optimizing simulation parameters, potential approval bottlenecks or abnormal flows can be proactively prevented and corrected, thereby improving the reliability and execution efficiency of the entire process system in real-world production environments.

[0060] A second embodiment of the present invention provides a workflow simulation device, comprising:

[0061] An execution risk prediction unit 201 is configured to obtain simulation parameters input by a user and use a pre-trained machine learning model to predict execution risks for the simulation parameters.

[0062] A simulation environment configuration unit 202 is configured to adaptively adjust the simulation parameters according to the execution risk, and configure the simulation environment according to the adjusted simulation parameters;

[0063] The workflow execution unit 203 is used to call the workflow engine to execute the workflow according to the preset simulation route in the simulation environment, dynamically inject a process listener into the execution process of the workflow engine, and use a logical snapshot to record the current process data, wherein the current process data is marked as logically deleted;

[0064] The simulation parameter updating unit 204 is configured to aggregate the process data and generate a simulation report after executing the workflow, and update the simulation parameters based on a comparison result between the simulation report and a preset standard.

[0065] A third embodiment of the present invention provides a workflow simulation device, including a memory and a processor, wherein the memory stores a computer program, and the computer program can be executed by the processor to implement a workflow simulation method as described in any one of the above.

[0066] A fourth embodiment of the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program can be executed by a processor of a device where the computer-readable storage medium is located to implement a workflow simulation method as described in any one of the above items.

[0067] Based on the workflow simulation method, device, equipment and storage medium provided by the present invention, the simulation parameters input by the user are first obtained, and a pre-trained machine learning model is called to predict the execution risk of the simulation parameters; then, the simulation parameters are adaptively adjusted according to the execution risk, and the simulation environment is configured according to the adjusted simulation parameters; then, the workflow engine is called to execute the workflow according to the preset simulation route in the simulation environment, and a process listener is dynamically injected during the execution of the workflow engine, and a logical snapshot is used to record the current process data, wherein the current process data is marked as logically deleted; finally, after the workflow is executed, the process data is summarized and a simulation report is generated, and the simulation parameters are updated based on the comparison results of the simulation report and the preset standards. The problem that the existing workflow verification simulation may interfere with normal business is solved.

[0068] For example, the computer program described in the third and fourth embodiments of the present invention can be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules can be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the simulation device for implementing a workflow. For example, the device described in the second embodiment of the present invention.

[0069] The processor may be a central processing unit (CPU), or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the method for simulating a workflow, and utilizes various interfaces and lines to connect the various parts of the method for simulating a workflow.

[0070] The memory can be used to store the computer program and / or module, and the processor realizes various functions of a workflow simulation method by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function (such as a sound playback function, a text conversion function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, text message data, etc.). In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0071] Wherein, if the implemented module is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of each of the above-mentioned method embodiments. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0072] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without inventive effort.

[0073] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A workflow simulation method, characterized in that: include: Obtain simulation parameters input by the user, and call a pre-trained machine learning model to predict execution risks of the simulation parameters; Adaptively adjusting the simulation parameters according to the execution risk, and configuring the simulation environment according to the adjusted simulation parameters; Invoking a workflow engine to execute a workflow in the simulation environment according to a preset simulation route, dynamically injecting a process listener into the execution process of the workflow engine, and using a logical snapshot to record current process data, wherein the current process data is marked as logically deleted; After executing the workflow, the process data is aggregated and a simulation report is generated, and the simulation parameters are updated based on a comparison result between the simulation report and a preset standard.

2. The method for simulating a workflow according to claim 1, wherein: The simulation parameters input by the user include: initiator, handler, number of executions, link configuration information, conditional branch rules and environment adaptation parameters.

3. The workflow simulation method according to claim 1, characterized in that: The pre-trained machine learning model is a supervised learning model trained based on historical workflow data. The supervised learning model is used to evaluate the execution risks of simulation parameters, which include node blocking, abnormal jumps, or approval bottlenecks.

4. The method for simulating a workflow according to claim 1, wherein: The injection nodes of the process listener include: process node entry, node completion, task allocation or abnormal interruption.

5. The workflow simulation method according to claim 1, characterized in that: Also includes: When the simulation is submitted, if the process fails to find the handler, copy person or supervisor of the next link, the process engine will package this information and return it to the process simulation call. The simulation call will parse the returned information to find the content that requires human-computer interaction, update the process data according to the simulation operation parameters configured in the simulation operation settings, and continue to submit the updated process data to the process engine for processing.

6. A workflow simulation device, characterized in that: include: An execution risk prediction unit, configured to obtain simulation parameters input by a user and invoke a pre-trained machine learning model to predict execution risks for the simulation parameters; a simulation environment configuration unit, configured to adaptively adjust the simulation parameters according to the execution risk, and configure the simulation environment according to the adjusted simulation parameters; A workflow execution unit is configured to call a workflow engine to execute a workflow in the simulation environment according to a preset simulation route, dynamically inject a process listener into the execution process of the workflow engine, and use a logical snapshot to record current process data, wherein the current process data is marked as logically deleted; The simulation parameter updating unit is used to summarize the process data and generate a simulation report after executing the workflow, and update the simulation parameters based on the comparison result between the simulation report and the preset standard.

7. A workflow simulation device, characterized in that: The system comprises a memory and a processor, wherein the memory stores a computer program, and the computer program can be executed by the processor to implement a workflow simulation method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that A computer program is stored, and the computer program can be executed by a processor of the device where the computer-readable storage medium is located to implement a workflow simulation method as described in any one of claims 1 to 5.

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