Workflow condition node management method based on dynamic rule engine

Abstract

The present invention proposes a workflow condition node management method based on a dynamic rule engine. The method obtains business flow requirements and configures task node attributes. Based on the task node attributes, the method adapts the business flow requirements to obtain a process template model. The method sets rule conditions, binds the rule conditions to the task nodes, and updates the process template model. The method generates a process instance based on the updated process template model, parses the rule conditions, initializes the task allocation logic, and pushes pending tasks to users. The user executes the pending task, triggering the rule engine to perform condition judgment, updating the process instance status, and obtaining an updated process instance status. Based on the updated process instance status, a visual interface is generated to display the process progress, and a real-time warning is issued for timeout or abnormal tasks. The method obtains a monitoring report and the process instance status after abnormal processing. The present invention can decompose tasks to make complex tasks easier to manage and reduce the difficulty of execution.

Description

Technical Field

[0001] The present invention relates to the field of office automation, and in particular to a workflow condition node management method based on a dynamic rule engine. Background Art

[0002] A workflow engine is a software system designed to automate, manage, and optimize business processes. It defines, executes, and monitors a series of interconnected tasks or activities that flow according to pre-set rules and conditions to achieve specific business objectives. The core value of a workflow engine lies in improving business efficiency, reducing human error, ensuring process consistency and compliance, and providing transparency and traceability. With the acceleration of digital transformation, workflow engines have become a critical component of enterprise application architectures, supporting a wide range of needs, from simple approval processes to complex cross-departmental collaboration.

[0003] The existing process engines on the market lack flexibility, cannot respond to changes in a timely manner, lack interoperability with each other, cannot quickly provide operation and maintenance information, have relatively simple functions, simple UI interfaces, and limited advanced functions.

[0004] For complex and ever-changing business scenarios, highly standardized products must be developed, with consideration for future flexible expansion. Architectural design requires a distinction between standard functions and business functions, decoupling and integrating code. At the design level, user experience must be considered. Summary of the Invention

[0005] In view of the above situation, the main purpose of the present invention is to propose a workflow condition node management method and system based on a dynamic rule engine to solve the above technical problems.

[0006] The present invention proposes a workflow condition node management method based on a dynamic rule engine, the method comprising the following steps:

[0007] Step 1: Obtain business flow requirements and configure task node attributes. According to the task node attributes, adapt the business flow requirements to obtain a process template model.

[0008] Step 2: Set the rule conditions, bind the rule conditions to the task nodes, and update the process template model to obtain the updated process template model;

[0009] Step 3: Generate a process instance based on the updated process template model, parse the rule conditions, initialize the task allocation logic, and push the to-do tasks to the user;

[0010] Step 4: The user executes the to-do task, triggering the rule engine to perform condition judgment, update the process instance status, and obtain the updated process instance status;

[0011] Step 5: Generate a visual interface based on the updated process instance status to display the process progress, and issue real-time warnings for timeouts or abnormal tasks, and obtain a monitoring report and the process instance status after abnormal handling.

[0012] Compared with the prior art, the present invention has the following beneficial effects:

[0013] 1) Improved efficiency: Task decomposition makes complex tasks easier to manage and reduces the difficulty of execution. Task sequencing optimizes the execution process, avoiding duplication or omissions. Task allocation ensures the rational use of resources and improves overall efficiency.

[0014] 2) Promote collaboration: Workflow clarifies everyone's responsibilities and reduces communication costs. Team members can better coordinate and improve overall collaboration.

[0015] 3) Complete functions: Existing process engines mostly focus on basic approval functions, while this invention achieves the integration of "rule-driven + approval execution" through deep collaboration between condition nodes and approval nodes (such as approval results triggering branch processes), solving the "functional separation" problem of traditional systems.

[0016] 4) User experience: Traditional systems rely on code development and adjustment processes. This invention significantly reduces the usage threshold for non-technical personnel through a graphical configuration interface and real-time visual monitoring.

[0017] 5) Traceability and Room for Improvement: Workflows record the execution status of each step, facilitating post-analysis. Process execution data is stored, allowing users to analyze historical data using native SQL, APIs, or custom reporting tools. Common analysis scenarios include process performance analysis, task processing efficiency analysis, and process anomaly analysis. Practical cases demonstrate that analysis based on historical data can help users identify process bottlenecks and optimize task allocation strategies, thereby improving the efficiency and quality of business processes.

[0018] 6) Standardization and consistency: Workflows use standardized processes to ensure consistent results each time they are executed. This reduces the impact of human factors on results and improves reliability.

[0019] The process engine supports multiple approval operations and can respond to various business scenarios and needs more flexibly. If any abnormal situation occurs during the approval process, the administrator can also perform emergency processing through the back-end system to improve the adaptability and reliability of the process.

[0020] Through dynamic binding of process modeling and rules driven by business needs; automated condition judgment and task allocation based on rule engines; combined with visual monitoring and real-time warnings to improve exception handling efficiency; and support for multi-dimensional data analysis to promote continuous process optimization, it can quickly respond to business changes, reduce manual configuration costs, ensure consistency and compliance of process execution, and significantly improve the agility and reliability of enterprise business processes through a closed-loop feedback mechanism.

[0021] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through embodiments of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a flowchart of the workflow condition node management method based on the dynamic rule engine proposed by the present invention. DETAILED DESCRIPTION

[0023] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and are not to be construed as limiting the present invention.

[0024] These and other aspects of the embodiments of the present invention will become clear with reference to the following description and accompanying drawings. In these descriptions and accompanying drawings, some specific implementations of the embodiments of the present invention are specifically disclosed to illustrate some ways of implementing the principles of the embodiments of the present invention, but it should be understood that the scope of the embodiments of the present invention is not limited thereto.

[0025] See also Figure 1 This embodiment provides a workflow condition node management method based on a dynamic rule engine, the method comprising the following steps:

[0026] Step 1: Obtain business flow requirements and configure task node attributes (such as approvers, rule conditions, and timeout policies). Based on the task node attributes, adapt the business flow requirements to obtain a process template model.

[0027] As a preferred embodiment of the present invention, obtaining business flow requirements, configuring task node attributes, and adapting the business flow requirements according to the task node attributes to obtain a process template model specifically includes the following steps:

[0028] Extract task nodes and business logic from business flow requirements. Task nodes include approval nodes and condition nodes.

[0029] Configure attribute parameters for approval nodes;

[0030] According to the business logic, the approval node after configuring the attribute parameters is associated with the condition node to generate a process template model that includes the flow relationship between nodes and the rule logic;

[0031] Perform syntax verification and node logic conflict detection on the process template model to ensure the model's executable, and then store the process template model in the template library.

[0032] Step 2: Set the rule conditions, bind the rule conditions to the task nodes, and update the process template model to obtain the updated process template model;

[0033] As a preferred embodiment of the present invention, setting rule conditions, binding the rule conditions to task nodes, and updating the process template model to obtain the updated process template model specifically includes the following steps:

[0034] Locate the condition node from the process template model and enter the rule condition expression through the rule editor;

[0035] Configure priority weights and conflict resolution strategies for multiple rule conditions;

[0036] Bind rule conditions to condition nodes to generate metadata that can be parsed by the rule engine;

[0037] Perform metadata simulation to verify the coordination of approval flow and conditional branches, and update the rule logic in the process template model.

[0038] As a preferred embodiment of the present invention, binding the rule conditions with the condition nodes to generate metadata that can be parsed by the rule engine specifically includes the following steps:

[0039] Preset the business field comparison table and extract the business fields in the business field comparison table;

[0040] Analyze the mathematical operators and logical relationships in the rule condition expressions, and decompose the parallel relationships in the logical relationships into independent judgment conditions to obtain the rule elements;

[0041] Combine business fields and rule elements to form a structured rule element list;

[0042] The target node in the positioning condition node is established based on the judgment rules in the structured rule element list. When the rule conditions are met, a forward flow association is established; when the conditions are not met, a reverse flow association is set to obtain a node binding relationship record table;

[0043] According to the priority weight and conflict resolution strategy, a priority is assigned to the next condition node in the forward flow association or reverse flow association, and a node binding relationship record table with priority is obtained;

[0044] The node binding relationship record table with priority is machine-readable and encapsulated to obtain metadata that can be parsed by the rule engine.

[0045] Step 3: Generate a process instance based on the updated process template model, parse the rule conditions, initialize the task allocation logic, and push the to-do tasks to the user;

[0046] As a preferred embodiment of the present invention, generating a process instance based on the updated process template model, parsing rule conditions, initializing task allocation logic, and pushing to-do tasks to users specifically include the following steps:

[0047] Load the updated process template model from the template library and associate it with business data (such as contract number and amount) to generate a process instance;

[0048] Call the rule engine to parse the rule expressions in the conditional nodes of the process instance and dynamically calculate the task allocation path;

[0049] Based on the task assignment path, push the to-do tasks to the target user's to-do list or system interface, such as email, API callback, etc.

[0050] Record the initial status and task queue information of the process instance and synchronize them to the database and cache layer.

[0051] To better reduce database access pressure, data during process execution is stored separately as hot data and cold data. This is combined with a cache mechanism to optimize data access efficiency and generate real-time processing data and historical archived data. The steps to optimize data access efficiency and generate real-time processing data and historical archived data are as follows:

[0052] Identify frequently accessed data (hot data) and infrequently accessed data (cold data) in process instances through the data classification module;

[0053] Store hot data in an in-memory database and set up multi-level cache to respond to process status queries and operation requests in real time;

[0054] Compress cold data and archive it, generating archive logs and historical execution records;

[0055] The cache synchronization mechanism ensures the consistency of hot data and the database, reducing the access pressure on the main database.

[0056] As a preferred embodiment of the present invention, calling a rule engine to parse the rule expression in the condition node in the process instance and dynamically calculating the task allocation path specifically includes the following steps:

[0057] Obtain business data, approver roles, and the number of tasks currently pending for the approver during the process instantiation phase;

[0058] Get the urgency coefficient based on the timeliness information of business data; get the basic score of the role based on the approver's role; get the load pressure value based on the number of tasks currently pending for the approver and the approver's task processing capacity;

[0059] Convert the urgency coefficient into a gain multiplier, adjust the role's basic score based on the gain multiplier and the load pressure value, and obtain a dynamic scoring table for candidate nodes;

[0060] Set the admission threshold, and filter the dynamic scoring table of candidate nodes according to the admission threshold to generate a preliminary path list;

[0061] Use the timeliness information of business data and project risk levels to build risk timeliness weights;

[0062] Statistics on historical approval efficiency and current rule triggering frequency are collected, and the risk timeliness weight is dynamically and adaptively adjusted based on the historical approval efficiency and current rule triggering frequency to generate dynamic risk timeliness weight;

[0063] The dynamic risk time weight is used to perform weighted adjustment on the preliminary path list to obtain the final path ranking list, and tasks are allocated based on the final path ranking list.

[0064] In the above-mentioned solution, the present invention has designed a multi-dimensional dynamic decision-making mechanism. By dynamically coupling and calculating five dimensions of data—role permissions, real-time load, time pressure, risk level, and historical performance—this mechanism enables task allocation paths to automatically adapt to changing business scenarios (e.g., automatically offloading tasks during peak hours), effectively preventing resource idleness or overload caused by fixed thresholds. Furthermore, the system can automatically adjust the weighting between risk and time pressure. Compared to traditional methods that require periodic manual parameter calibration, this feature enables the system to continuously evolve.

[0065] Step 4: The user executes the to-do task, triggering the rule engine to perform condition judgment, update the process instance status, and obtain the updated process instance status;

[0066] As a preferred embodiment of the present invention, the user executes a to-do task, triggering the rule engine to perform condition judgment and update the process instance state. Obtaining the updated process instance state specifically includes the following steps:

[0067] Users receive to-do tasks through the front-end interface and perform approval, rejection or transfer operations;

[0068] Trigger the rule engine to perform conditional judgment on the current operation (such as "resubmit to the previous node after rejection"); update the process instance status (such as "approved" or "returned for modification") based on the judgment result, and trigger the next node task allocation;

[0069] Write the status change information of the process instance status into the execution log and synchronize it to the database and message queue for the monitoring module to call.

[0070] As a preferred embodiment of the present invention, triggering the rule engine to perform conditional judgment on the current operation (such as "resubmit to the previous node after rejection"); updating the process instance status (such as "approved" or "returned for modification") based on the judgment result, and triggering the next node task allocation specifically includes the following steps:

[0071] Parse the user operation type, obtain the type tag, and extract the risk feature data of the current contract and the target approver load status;

[0072] Divert processing based on the operation type tag. If it is an approved operation, jump to the risk assessment branch; if it is a rejected operation, enter the rejection reason analysis branch; if it is a transfer operation, start the path conflict detection branch;

[0073] If the system is in the risk review branch, it will conduct a risk quantitative analysis of the risk characteristic data of the current contract based on historical approval records to obtain a dynamic risk score report;

[0074] Set a basic weight based on the current approver's rank, and adjust the basic weight based on the impact of the current pending tasks on approval efficiency to obtain the actual effective weight.

[0075] Based on the actual effective weight and dynamic risk score report, parallel candidate approval paths are generated for approvers in departmental collaboration relationships, and approval path decision instructions are obtained to update the process instance status;

[0076] If the application is in the rejection analysis branch, the rejection reason text is obtained and the reason keywords in the rejection reason text are identified;

[0077] Based on the reason keywords, search for the frequency of similar rejections in history to calculate the severity index of the current rejection reason and obtain a rejection feature analysis report;

[0078] Based on the number of similar rejections and severity indexes in the rejection feature analysis report, different emergency levels are assigned to the current approval process, and a process upgrade decision document is obtained. This document overwrites the original approval path to update the process instance status.

[0079] If it is in the path conflict detection branch, an initial priority ranking is generated according to the approver's rank, and the initial priority ranking is dynamically adjusted according to the current load of the target approver to obtain a dynamic priority matrix;

[0080] Detect the duplicate occupancy of approvers in multiple optional paths in the dynamic priority matrix. Based on the duplicate occupancy of approvers, select the approver combination with the highest priority and the lowest load, generate a new approval path that avoids resource conflicts, and replace the original conflicting path with the new approval path that avoids resource conflicts to update the process instance status.

[0081] In the above solution, the present invention conducts a three-dimensional coupled analysis of user behavior data (operation type), business feature data (contract risk), and system resource data (approver load). This design enables decision-making logic to simultaneously meet the triple requirements of business compliance, operational rationality, and system stability, effectively reducing the rate of judgment errors. Furthermore, the designed dynamic priority matrix prevents resource deadlock by precalculating the following parameters: the probability of cross-approval path conflicts, the predicted task saturation of approvers, and the estimated delay for cross-departmental collaboration.

[0082] Step 5: Generate a visual interface based on the updated process instance status to display the process progress, and issue real-time warnings for timeouts or abnormal tasks, and obtain a monitoring report and the process instance status after abnormal handling.

[0083] As a preferred embodiment of the present invention, generating a visual interface to display the process progress based on the updated process instance status, and providing real-time warnings for timeout or abnormal tasks, and obtaining a monitoring report and the process instance status after abnormal processing specifically include the following steps:

[0084] Obtain real-time status data of process instances from the front-end monitoring interface and render it into a flowchart to show the node completion progress;

[0085] Based on the node completion progress, the early warning module scans for timed tasks (e.g., timeouts without approval) or abnormal status (e.g., three consecutive rejections), triggering SMS or in-site notifications.

[0086] Generate monitoring reports based on aggregated log data, including task processing timeliness, rule triggering frequency, and abnormal event distribution;

[0087] Generate optimization suggestions based on the report analysis results (such as adjusting rule thresholds or adding parallel approval nodes) and update them to the template library.

[0088] In this embodiment, the present invention adopts a front-end and back-end separation development model, using Vue.js for the front-end and Java for the back-end, supporting Sass deployment. It is built on the process engine Flowable and complies with the BPMN2.0 specification for process function design and implementation.

[0089] It should be understood that, although the various steps in the flow chart of each embodiment of the present invention are shown in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0090] It should be understood that various components of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0091] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0092] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A workflow condition node management method based on a dynamic rule engine, characterized in that: The method comprises the following steps: Step 1: Obtain business flow requirements and configure task node attributes. According to the task node attributes, adapt the business flow requirements to obtain a process template model. Step 2: Set the rule conditions, bind the rule conditions to the task nodes, and update the process template model to obtain the updated process template model; Step 3: Generate a process instance based on the updated process template model, parse the rule conditions, initialize the task allocation logic, and push the to-do tasks to the user. The specific steps include the following: Load the updated process template model from the template library and associate it with business data to generate a process instance; Call the rule engine to parse the rule expressions in the conditional nodes of the process instance and dynamically calculate the task allocation path; Based on the task allocation path, push the to-do tasks to the target user's to-do list or system interface; Record the initial status and task queue information of the process instance and synchronize them to the database and cache layer; Step 4: The user executes the to-do task, triggering the rule engine to perform conditional judgment, update the process instance status, and obtain the updated process instance status. The specific steps include the following: Users receive to-do tasks through the front-end interface and perform approval, rejection or transfer operations; Trigger the rule engine to perform conditional judgment on the current operation; update the process instance status based on the judgment result and trigger the next node task allocation; Write the status change information of the process instance status into the execution log and synchronize it to the database and message queue for the monitoring module to call; Step 5: Generate a visual interface based on the updated process instance status to display the process progress, and issue real-time warnings for timeouts or abnormal tasks, and obtain a monitoring report and the process instance status after abnormal handling; The following steps are involved in calling the rule engine to parse the rule expression in the condition node of the process instance and dynamically calculating the task allocation path: Obtain business data, approver roles, and the number of tasks currently pending for the approver during the process instantiation phase; Get the urgency coefficient based on the timeliness information of business data; get the basic score of the role based on the approver's role; get the load pressure value based on the number of tasks currently pending for the approver and the approver's task processing capacity; Convert the urgency coefficient into a gain multiplier, adjust the role's basic score based on the gain multiplier and the load pressure value, and obtain a dynamic scoring table for candidate nodes; Set the admission threshold, and filter the dynamic scoring table of candidate nodes according to the admission threshold to generate a preliminary path list; Use the timeliness information of business data and project risk levels to build risk timeliness weights; Statistics on historical approval efficiency and current rule triggering frequency are collected, and the risk timeliness weight is dynamically and adaptively adjusted based on the historical approval efficiency and current rule triggering frequency to generate dynamic risk timeliness weight; Use dynamic risk-time weights to adjust the preliminary path list to obtain the final path ranking list, and then allocate tasks based on the final path ranking list; The triggering rule engine performs conditional judgment on the current operation; updates the process instance status based on the judgment result, and triggers the next node task allocation, which specifically includes the following steps: Parse the user operation type, obtain the type tag, and extract the risk feature data of the current contract and the target approver load status; Divert processing based on the operation type tag. If it is an approved operation, jump to the risk assessment branch; if it is a rejected operation, enter the rejection reason analysis branch; if it is a transfer operation, start the path conflict detection branch; If the system is in the risk review branch, it will conduct a risk quantitative analysis of the risk characteristic data of the current contract based on historical approval records to obtain a dynamic risk score report; Set a basic weight based on the current approver's rank, and adjust the basic weight based on the impact of the current pending tasks on approval efficiency to obtain the actual effective weight. Based on the actual effective weight and dynamic risk score report, parallel candidate approval paths are generated for approvers in departmental collaboration relationships, and approval path decision instructions are obtained to update the process instance status; If the application is in the rejection analysis branch, the rejection reason text is obtained and the reason keywords in the rejection reason text are identified; Based on the reason keywords, search for the frequency of similar rejections in history to calculate the severity index of the current rejection reason and obtain a rejection feature analysis report; Based on the number of similar rejections and severity indexes in the rejection feature analysis report, different emergency levels are assigned to the current approval process, and a process upgrade decision document is obtained. This document overwrites the original approval path to update the process instance status. If it is in the path conflict detection branch, an initial priority ranking is generated according to the approver's rank, and the initial priority ranking is dynamically adjusted according to the current load of the target approver to obtain a dynamic priority matrix; Detect the duplicate occupancy of approvers in multiple optional paths in the dynamic priority matrix. Based on the duplicate occupancy of approvers, select the approver combination with the highest priority and the lowest load, generate a new approval path that avoids resource conflicts, and replace the original conflicting path with the new approval path that avoids resource conflicts to update the process instance status.

2. The workflow condition node management method based on dynamic rule engine according to claim 1, characterized in that: In step 1, the business flow requirements are obtained, and the task node attributes are configured. According to the task node attributes, the business flow requirements are adapted to obtain the process template model, which specifically includes the following steps: Extract task nodes and business logic from business flow requirements. Task nodes include approval nodes and condition nodes. Configure attribute parameters for approval nodes; According to the business logic, the approval node after configuring the attribute parameters is associated with the condition node to generate a process template model that includes the flow relationship between nodes and the rule logic; Perform syntax verification and node logic conflict detection on the process template model, and store the process template model in the template library.

3. The workflow condition node management method based on dynamic rule engine according to claim 2, characterized in that: In step 2, setting rule conditions, binding the rule conditions to task nodes, and updating the process template model to obtain the updated process template model specifically includes the following steps: Locate the condition node from the process template model and enter the rule condition expression through the rule editor; Configure priority weights and conflict resolution strategies for multiple rule conditions; Bind rule conditions to condition nodes to generate metadata that can be parsed by the rule engine; Perform metadata simulation to verify the coordination of approval flow and conditional branches, and update the rule logic in the process template model.

4. The workflow condition node management method based on dynamic rule engine according to claim 3 is characterized in that: Binding rule conditions to conditional nodes to generate metadata that can be parsed by the rule engine involves the following steps: Preset the business field comparison table and extract the business fields in the business field comparison table; Analyze the mathematical operators and logical relationships in the rule condition expressions, and decompose the parallel relationships in the logical relationships into independent judgment conditions to obtain the rule elements; Combine business fields and rule elements to form a structured rule element list; The target node in the positioning condition node is established based on the judgment rules in the structured rule element list. When the rule conditions are met, a forward flow association is established; When the condition is not met, set the reverse flow association to obtain the node binding relationship record table; According to the priority weight and conflict resolution strategy, a priority is assigned to the next condition node in the forward flow association or reverse flow association, and a node binding relationship record table with priority is obtained; The node binding relationship record table with priority is machine-readable and encapsulated to obtain metadata that can be parsed by the rule engine.

5. The workflow condition node management method based on dynamic rule engine according to claim 1, characterized in that: In step 5, a visual interface is generated based on the updated process instance status to display the process progress, and a real-time warning is issued for timeout or abnormal tasks. The monitoring report and the process instance status after abnormal handling are obtained. Specifically, the following steps are included: Obtain real-time status data of process instances from the front-end monitoring interface and render it into a flowchart to show the node completion progress; According to the node completion progress, the early warning module scans for timed tasks or abnormal status, triggering SMS or in-site message notifications; Generate monitoring reports based on aggregated log data, including task processing timeliness, rule triggering frequency, and abnormal event distribution; Generate optimization suggestions based on the report analysis results and update them to the template library.

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