Authentication process management system and method for multi-node dynamic warning and compliance supervision

Abstract

The invention relates to the technical field of intelligent process management, and discloses a multi-node dynamic warning and compliance supervision authentication process management system, which comprises a dynamic rule generation module, a real-time data integration module, a predictive intervention module, a compliance verification module and a closed-loop feedback module. The efficiency and compliance weight are dynamically optimized through reinforcement learning and Monte Carlo tree search, a global task dependency relationship is constructed in combination with a distributed graph database, the time sequence convolutional network is used for predicting the overdue probability, the priority is adjusted based on the game theory, and dynamic resource allocation is achieved. Meanwhile, compliance verification logic is generated based on natural language analysis, cross-department knowledge migration and credible evidence storage are completed through federated learning and a block chain technology, and rule strategies and model parameters are optimized in a closed-loop mode through an incremental updating mechanism. According to the method, the problems of rule stiffness, data island and supervision lag of a traditional system are solved, the process efficiency can be improved, the compliance risk is reduced, and self-adaptive continuous optimization is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent process management, and particularly to an authentication process management system and method for multi-node dynamic warning and compliance supervision. Background Art

[0002] Currently, the current authentication process management system generally uses a predefined static rule library to drive task execution. Its rule strategy is usually manually solidified based on historical experience and is difficult to adapt to dynamic business scenarios and real-time compliance requirements. Especially in a complex environment with multi-department collaboration and heterogeneous data sources coexisting, the dynamic coupling of task dependencies and resource constraint conditions often leads to rule conflicts or execution deviations, resulting in a decline in process efficiency and an increase in compliance risks.

[0003] Traditional systems rely on isolated data storage architectures, and the correlation relationships of core elements such as tasks, resources, and departments lack global modeling, resulting in the prediction of overdue risks and root cause analysis being limited to a local perspective and making it difficult to accurately locate key bottleneck nodes across levels and systems.

[0004] In terms of compliance supervision, existing technologies mostly adopt a post-event manual review and passive response mechanism, which cannot achieve automated verification and trusted evidence storage driven by real-time data, and it is difficult to effectively share cross-department compliance knowledge due to data privacy restrictions.

[0005] In addition, the resource allocation strategy highly depends on manual experience to set priorities and lacks the ability of quantitative risk assessment and dynamic adjustment, resulting in low resource utilization and rigid task scheduling. Summary of the Invention

[0006] In view of the deficiencies of the prior art, the present invention provides an authentication process management system and method for multi-node dynamic warning and compliance supervision, which solves the problems of low efficiency and uncontrollable compliance risks caused by rule rigidity, data islands, and lagging supervision in the existing authentication process management system.

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions: An authentication process management system for multi-node dynamic warning and compliance supervision, including: A dynamic rule generation module, used to generate a dynamic rule strategy based on the real-time business status; A real-time data integration module, connected to the dynamic rule generation module, used to integrate multi-source data and construct a global task dependency relationship; A predictive intervention module, connected to the real-time data integration module, used to predict the overdue risk according to the global task dependency relationship and dynamically adjust the task priority; A compliance verification module, connected to the predictive intervention module, used to perform automated compliance verification based on the dynamic rule strategy and generate evidence; Closed-loop feedback module, connected to the dynamic rule generation module, real-time data integration module, and compliance verification module, for optimizing rule strategies and model parameters based on compliance verification results and task execution data.

[0008] Preferably, the dynamic rule generation module generates rule strategies through the following formula: ; Where: ; Represents the number of violations; : The total number of tasks to be processed within the current decision-making cycle; : The total number of violation events detected by the system within the same cycle.

[0009] , Are weight coefficients dynamically adjusted through Monte Carlo tree search.

[0010] Preferably, the predictive intervention module calculates the dynamic priority through the following formula: ; Where: Represents the task urgency, Represents the scope of influence, Represents the resource requirement; Is the overdue probability predicted by the temporal convolutional network; Is the slope adjustment parameter, Is the overdue probability threshold.

[0011] Preferably, the compliance verification module realizes the joint optimization of federated learning through the following formula: ; Where: Is the loss of the shared model on the global data ; Represents the shared model, used to learn cross-departmental general compliance patterns; Is the loss of the local model on the departmental data ; Represents the local model, used to adapt to the characteristics of departmental private data; , Are balancing weights.

[0012] Preferably, the real-time data integration module includes: A distributed graph database for storing nodes and dependencies of tasks, resources, and departments; A causal inference model for analyzing the root nodes of task overdue based on graph neural networks.

[0013] Preferably, the closed-loop feedback module optimizes the system in the following ways: Incrementally update the parameters of the reinforcement learning strategy; Incrementally update the causal inference model of the graph neural network.

[0014] The authentication process management method for multi-node dynamic warning and compliance supervision includes the following steps: S1. Dynamically generate rule policies, where the rule policies are based on real-time business status and historical execution data; S2. Integrate multi-source data and construct a global task dependency relationship to support overdue risk prediction; S3. According to the global task dependency relationship and dynamic rule policies, predict the task overdue probability and dynamically adjust the priority; S4. Based on the adjusted priority and dynamic rule policies, perform automated compliance verification and generate non-tamperable evidence; S5. According to the compliance verification results and task execution data, perform closed-loop optimization of rule policies and prediction models.

[0015] Preferably, the dynamically generating rule policies includes the following steps: Dynamically adjust the weights of efficiency and compliance objectives based on the reinforcement learning algorithm; Optimize the weight allocation of the multi-objective equation through Monte Carlo tree search.

[0016] Preferably, the integrating multi-source data includes the following steps: Construct a global task dependency graph and store the relationships between nodes and edges through a graph database; Locate the root nodes of task overdue based on the causal inference model.

[0017] Preferably, the dynamically adjusting priority includes the following steps: Use a temporal convolutional network to predict the overdue probability; Combine game theory algorithms to calculate the priority and trigger resource reallocation.

[0018] The present invention provides an authentication process management system and method for multi-node dynamic warning and compliance supervision. It has the following beneficial effects: 1. Through the reinforcement learning and Monte Carlo tree search mechanism of the dynamic rule generation module, the present invention can perceive the changes in the business state in real time and dynamically balance the weight of efficiency and compliance goals. Automatically adjust the rule strategy based on real-time feedback data, solve the pain points of lagging rule updates and high manual intervention costs, and significantly improve the policy flexibility in complex scenarios.

[0019] 2. Through the distributed graph database and causal inference model of the real-time data integration module, the present invention uniformly models the scattered task, resource, and department data into a global dependency graph, and combines graph neural networks to analyze the causal chain of task overdue. Compared with the manual investigation of traditional isolated systems, it can automatically identify the root cause nodes across departments and levels (such as upstream resource bottlenecks), achieving a qualitative improvement in the efficiency of risk traceability.

[0020] 3. Based on the automated verification logic generation of natural language parsing, cross-departmental knowledge transfer of federated learning, and blockchain evidence storage technology, the compliance review is transformed from passive response to active prevention. Realize the sharing of compliance experience among multiple departments under the premise of ensuring privacy, and at the same time meet the strong audit requirements through immutable records, forming a trusted supervision closed-loop for the entire process.

[0021] 4. Through the temporal convolutional network and game theory algorithm of the predictive intervention module, the priority decision driven by manual experience is transformed into a quantitative model calculation, combined with the dynamic resource preemption and task parallelization mechanism, to solve the problems of extensive resource allocation and slow response in traditional processes. Especially in the scenario of a sudden increase in emergency tasks, it can scientifically allocate resources and avoid systemic overdue risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is the system architecture diagram of the present invention; Figure 2 is the method flow diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0024] Embodiment: Please refer to the attached Figure 1 , the embodiment of the present invention provides an authentication process management system for multi-node dynamic warning and compliance supervision, including: A dynamic rule generation module for generating dynamic rule strategies based on real-time business states; The dynamic rule generation module is used to generate dynamic rule policies based on real-time business status and historical data. The core lies in achieving an adaptive balance between efficiency and compliance goals through mathematical models and algorithms. The implementation of this module includes the following technical elements: Definition of State Space and Action Space To describe the real-time context of the authentication process, a state space is defined It includes the following variables: Task type (such as document review, on-site inspection, compliance re-review); Resource load rate (the real-time occupancy ratio of computing resources and human resources); Compliance standard version (the version of the regulatory provisions that the current task needs to meet); Task priority (the initial priority set based on business requirements).

[0025] Action Space It is defined as the adjustment operation of the weight coefficients of efficiency and compliance goals: : The weight of the efficiency goal Incremental adjustment; : The weight of the compliance goal Incremental adjustment.

[0026] The relevance between states and actions is reflected in that changes in task type and resource load rate require dynamic adjustment of weights. For example, when the resource load is high, compliance should be prioritized.

[0027] Hierarchical Reward Function Design To quantify the conflicts and collaborations between efficiency and compliance goals, a hierarchical reward function is constructed: represents the task actual elapsed time); represents the task number of violations); ; , : The dynamic weight coefficient, satisfying + = 1; : The actual elapsed time from task start to completion; : The number of violations due to missing materials or process errors.

[0028] : The total number of tasks to be processed within the current decision cycle; : The total number of violation events detected by the system within the same period.

[0029] By adjusting with , the system can flexibly respond to business requirements. For example, during the peak period of compliance audits, increase to reduce the risk of violations.

[0030] Reinforcement learning policy optimization Update the policy network parameters using the Deep Deterministic Policy Gradient (DDPG) algorithm: Action selection: The policy network outputs an action based on the current state ; Reward calculation: According to the formula ; Network update: Minimize the Bellman error function: ; where is the action-value network, is the discount factor, are the target network parameters.

[0031] DDPG is applicable to continuous action spaces, can smoothly adjust the weight coefficients, and avoid policy oscillations caused by discrete actions.

[0032] Monte Carlo Tree Search (MCTS) weight optimization To solve the weight allocation problem of multi-objective optimization, introduce Monte Carlo Tree Search: Tree structure construction: Use the current state as the root node and generate child nodes for possible actions ; Simulation and backtracking: Evaluate the action value by simulating future state sequences, and backtrack to update the node visit counts and values; Weight selection: Select , the global optimal allocation based on the optimal path.

[0033] MCTS solves the problem that traditional greedy algorithms are prone to falling into local optima through exploration-exploitation balance. For example, in a resource conflict scenario, MCTS can find that it is necessary to temporarily reduce to avoid systemic violation risks.

[0034] The dynamic rule generation module finally outputs a rule policy library, including the following: Rule conditions: Based on state variables (such as "resource load rate > 70% and task type = on-site inspection"); Rule actions: The adjusted weight coefficients , and related operations (such as triggering resource preemption); Policy priority: Sort the rules according to the MCTS evaluation results to ensure that high-value rules are executed first.

[0035] When the resource load rate exceeds the threshold, the module generates a rule "If the resource load rate > 80% and the compliance standard version ≥ 2.0, then set = 0.8" to forcibly increase the compliance priority.

[0036] This implementation method realizes the adaptive generation of authentication process rules through the collaboration of mathematical modeling and algorithms, and can respond to dynamic business requirements and balance efficiency and compliance goals.

[0037] A real-time data integration module, connected to the dynamic rule generation module, is used to integrate multi-source data and build global task dependencies; The real-time data integration module is used to integrate multi-source heterogeneous data and build global task dependencies. The core lies in realizing the real-time perception of task status and the root cause analysis of overdue through a distributed graph database and a causal inference model. The implementation method of this module includes the following technical elements: Construction of the global task dependency graph To describe the complex dependencies among tasks, resources, and departments in the authentication process, first construct a global task dependency graph based on the graph database. Nodes are defined as tasks (Task), resources (Resource), and departments (Department), and edges are defined as dependency relationships (such as "Task A depends on Resource B" and "Task C belongs to Department D"). The adjacency matrix stores the node connection relationships, and the node feature matrix stores attribute information (such as task duration, resource capacity, and department permissions). Using a distributed graph database (such as Neo4j) to store data can support high-concurrency reading and writing and complex queries.

[0038] Traditional relational databases are difficult to efficiently handle multi-hop queries and dynamic dependencies. The graph database intuitively expresses the task topology through the node-edge structure, providing a data basis for subsequent causal reasoning.

[0039] Event-driven data synchronization To achieve the real-time integration of multi-source data (department system logs, API interfaces, manual input), adopt an event-driven architecture (EDA) to listen for data change events. When the task status is updated or the resource allocation changes, trigger an event task ID, change type, timestamp , and update the graph database through the event handling function: INSERT / UPDATE WHERE Task ID = Target Node.

[0040] Exemplary Event: Task The event of extended task duration will update the feature vector of its corresponding node and synchronously adjust the expected completion time of dependent tasks as well.

[0041] The event-driven mechanism ensures the consistency between the graph database and the real-time business status, providing the latest input for the predictive intervention module.

[0042] Causal Inference Model Construction To locate the root cause node of task overdue, a causal inference model is constructed based on the Graph Neural Network (GNN) and the Structural Causal Model (SCM). First, the node embedding features are extracted through GNN: ; : The node embedding feature matrix of the th layer; : The degree matrix, ; : The trainable weight matrix of the th layer; : The non-linear activation function (such as ReLU).

[0043] Subsequently, based on the structural causal model, calculate the contribution degree of node to the overdue probability: ; The partial derivative quantifies the impact of node feature changes on the overdue probability. The larger the gradient value, the more likely the node is the key root cause.

[0044] Domain Knowledge Fusion and Root Cause Correction To avoid the bias of a pure data-driven model, a domain knowledge graph (such as "Three-level review tasks must be executed serially") is further introduced, and the causal inference results are corrected through a rule engine. The specific process is as follows: Output the list of root cause nodes from the causal inference model ; Match the predefined patterns in the domain knowledge graph (such as "Resource Conflict", "Process Violation"); Reduce the confidence or filter the root cause nodes that do not conform to the domain knowledge.

[0045] Exemplary correction: If the causal reasoning output is "translation delay causes overdue", but the domain knowledge stipulates that "translation tasks allow parallel outsourcing", then this root cause is determined to be of low confidence and manual review is triggered.

[0046] The interaction logic between the real-time data integration module and the dynamic rule generation module and the predictive intervention module is as follows: Dynamic rule generation module: Receives the global task dependency relationship output by the graph database for conditional matching of rule strategies (such as "if the number of dependent tasks > 5, then increase the compliance weight"); Predictive intervention module: Receives the causal reasoning results (list of root cause nodes) for influence scope quantification in dynamic priority calculation Quantification; Closed-loop feedback module: Receives the overdue verification results for incrementally updating the GNN model parameters and the domain knowledge graph Through the real-time data closed-loop, the system can dynamically correct the bias of the causal reasoning model and improve the accuracy of root cause localization.

[0047] This implementation method combines mathematical modeling with domain knowledge, can accurately locate the root cause of task overdue, and provides real-time and structured context data for downstream modules.

[0048] The predictive intervention module, connected to the real-time data integration module, is used to predict the overdue risk according to the global task dependency relationship and dynamically adjust the task priority; The predictive intervention module is used to predict the task overdue risk and dynamically adjust the priority. Its core lies in realizing multi-dimensional feature fusion and scientific decision-making through the temporal convolutional network and game theory algorithms. The implementation method of this module includes the following technical elements: To fuse heterogeneous data (temporal, categorical, numerical) and capture long-term dependencies, a multi-head TCN model is first constructed. The input features are divided into three categories: temporal data (task duration sequence), categorical data (task type encoding), and numerical data (resource load rate). Each type of data is processed through an independent TCN channel: Temporal data channel: Uses dilated convolution (DilatedConv1D) to extract multi-scale temporal features, and the dilation coefficient preferably increases exponentially (such as 1, 2, 4); Categorical data channel: Encodes discrete types into dense vectors through an embedding layer and then reduces the dimension through a 1D convolutional layer; Numerical data channel: Directly inputs a 1D convolutional layer to extract local features.

[0049] The feature fusion formula is: ; , , : represent time series, category, and numerical input features respectively; : Attention weight matrix, used to adaptively weight the contribution of different channels.

[0050] Traditional single models are difficult to process heterogeneous data. Multi-head TCN can improve the accuracy of overdue prediction through channel processing and attention fusion.

[0051] Calculation of overdue probability Fusion Features Input the fully connected layer and output the overdue probability through the Sigmoid function: ; , : Fully connected layer weights and bias parameters; : Sigmoid function, mapping the output to the [0,1] interval.

[0052] As the core input of dynamic priority calculation, the overdue probability directly affects the resource reallocation decision.

[0053] In order to balance the task urgency, impact scope and resource requirements, a priority formula based on game theory is constructed: ; : Task urgency, calculated backwards from the deadline (e.g. ); S i : Impact scope, quantified by the number of dependent tasks (e.g. ); : Resource requirements, normalized by CPU / memory usage; : Parameters for adjusting the slope of the Sigmoid curve; : Overdue probability threshold, used to trigger intervention actions.

[0054] By introducing the Sigmoid function, the nonlinear impact of overdue probability on priority can be quantified to avoid excessive response to low-risk tasks.

[0055] when > The following intervention actions are automatically triggered: Resource preemption: Pause or downgrade low-priority tasks to release resources for high-risk tasks; Task parallelization: Split serial tasks into parallel subtasks and accelerate them through distributed computing; Process Acceleration: Bypass non-critical review processes (such as formal review) and directly enter the core approval process.

[0056] If the overdue probability of task X = 0.45 ( = 0.3), then trigger resource preemption and temporarily allocate the CPU quota of task Y to X.

[0057] The interaction logic between the predictive intervention module and upstream and downstream modules is as follows: Real-time data integration module: Receive the global task dependency graph for calculating the scope of influence ; Dynamic rule generation module: Obtain the current weight policy (such as , ) for adjusting the trigger threshold of resource preemption; Compliance verification module: Write the intervention action record into the blockchain for evidence preservation to ensure the traceability of operations.

[0058] Through cross-module collaboration, the system can achieve dynamic resource optimization while ensuring compliance.

[0059] This implementation method can achieve dynamic adjustment of task priorities and resource reallocation by combining mathematical models and business logics, reducing the overdue risk and improving the process efficiency.

[0060] The compliance verification module, connected to the predictive intervention module, is used to perform automated compliance verification based on the dynamic rule policy and generate evidence preservation; The compliance verification module is used to perform automated compliance verification and generate immutable evidence preservation. The core lies in achieving regulation parsing and cross-departmental knowledge transfer through natural language processing and federated learning. The implementation method of this module includes the following technical elements: To convert the regulation text into executable verification logic, first use a pre-trained language model (such as BERT) to parse the regulation clauses. The specific process is as follows: Condition-action pair extraction: Identify the conditional statements (such as "review level ≥ 3") and associated actions (such as "allow to enter the next link") in the regulation by fine-tuning the BERT model; Logical mapping: Map the natural language conditions to predefined API calls, for example, map "review level" to the interface; Executable rule generation: Output a standardized rule template: , : Predefined data interfaces (such as obtaining the review level, verifying the integrity of materials); , : Threshold parameter (such as the audit level threshold = 3) Traditional manual coding rules are difficult to adapt to the dynamic update of regulations, and NLP parsing realizes the automatic conversion from clauses to code.

[0061] To achieve cross-departmental compliance knowledge sharing and protect data privacy, a federated learning framework is adopted to jointly train a shared model and local models: Shared model training: Train the model on the global dataset to learn the general compliance pattern; Local model fine-tuning: Each department fine-tunes the model on its private dataset to adapt to the business characteristics; Joint optimization of the objective function: ; is the loss of the shared model on the global data represents the shared model, which is used to learn the cross-departmental general compliance pattern; is the loss of the local model on the departmental data represents the local model, which is used to adapt to the characteristics of the department's private data : Balancing weight, preferably + = 1.

[0062] Federated learning ensures the sharing of compliance experiences among departments, while avoiding the leakage of sensitive data, and supports the cross-departmental knowledge transfer in the above.

[0063] To ensure the immutability of the verification results, blockchain technology is used to record key operations: Content of evidence deposit: Task ID, compliance result, timestamp, operator's signature; Smart contract trigger: When the compliance verification is completed, the smart contract is automatically called to write to the blockchain; Format of data on the chain: ; : Hash value of the previous block; : Operator's digital signature based on public-private key encryption.

[0064] Traditional database evidence deposit is easily tampered with, and blockchain ensures data credibility through the hash chain and distributed consensus mechanism.

[0065] ​​​​​The interaction logic between the compliance verification module and other modules is as follows: Dynamic rule generation module: Receives the current compliance weight , and determines the verification strictness (e.g., full-scale verification is enabled when > 0.7); Predictive intervention module: Adjusts the task priority according to the compliance verification result (e.g., tasks with failed verification are downgraded); Closed-loop feedback module: Feeds back the violation records and verification logs to the rule generation module to drive policy optimization.

[0066] Exemplary collaboration: When the dynamic rule generation module increases the compliance weight, the compliance verification module adds verification items (such as supplementing environmental compliance reviews).

[0067] This implementation method can achieve efficient and reliable compliance verification through the combination of automated parsing, federated learning, and blockchain technology, and can adapt to the complex business environment of multi-department collaboration The closed-loop feedback module is connected to the dynamic rule generation module, the real-time data integration module, and the compliance verification module, and is used to optimize the rule policy and model parameters according to the compliance verification result and task execution data.

[0068] The closed-loop feedback module is used to optimize the system rules and model parameters according to the compliance verification result and task execution data. The core lies in achieving self-iterative learning through an incremental update mechanism. The implementation method of this module includes the following technical elements: To balance exploration and exploitation, first store the task execution data in the experience pool , where is the state vector, is the action vector, is the immediate reward. The prioritized experience replay (Prioritized Experience Replay) mechanism is adopted to allocate sampling weights according to the TD error : ; ; ; Prioritized sampling improves the utilization rate of high-value experiences and accelerates the convergence of the policy network.

[0069] Dynamically adjust the multi-objective weights based on the task execution results , , and adopt MCTS to explore the optimal weight allocation path: Tree construction: Use the current state as the root node to generate action branches =( , ); Simulation and evaluation: Calculate the action value by simulating the future state sequence ; Policy update: Update the node weights according to the access times and value, and select the optimal branch: ; : State 's access times; : Access times of state-action pairs.

[0070] MCTS breaks through the local optimal limit and ensures the global rationality of weight distribution.

[0071] To improve the root cause localization accuracy, incrementally update the parameters of the graph neural network (GNN) based on new task data. The loss function is defined as the prediction error of the root cause contribution: ; : Node contribution predicted by the model; : Actually verified contribution (such as manually labeled root cause).

[0072] The parameter update formula is: ; Incremental learning avoids the computational overhead of full retraining and adapts to the dynamic business environment.

[0073] To integrate newly discovered compliance rules and process patterns, convert high-frequency trigger policies into domain knowledge through a rule engine: Pattern extraction: Statistically analyze high-frequency condition-action pairs in the rule library (such as "resource load rate > 80% → increase compliance weight"); Knowledge encoding: Encode condition-action pairs as node-edge relationships in the graph database; Conflict detection: Compare new and old knowledge and remove conflicting rules (such as the old rule "load rate > 70% → improve efficiency").

[0074] If the new rule "block the process if the environmental review fails" appears more frequently than the threshold, add it to the knowledge graph.

[0075] The interaction logic of the closed-loop feedback module with upstream and downstream modules is as follows: Dynamic rule generation module: Receive the optimized weights , and updated policies ; Real-time data integration module: Receive incrementally updated Parameter to improve the accuracy of causal inference; Compliance verification module: Obtain new domain knowledge to enhance the completeness of the verification rule library.

[0076] Through the cross-module data closed-loop, the system can continuously adapt to business changes and form the ability of self-evolution.

[0077] This implementation method can achieve the adaptive iteration of rule strategies and inference models through mathematical modeling and incremental learning mechanisms, ensuring the long-term effectiveness of the system in a dynamic environment.

[0078] Please refer to the appendix Figure 2 Another embodiment of the present invention provides a method for managing the authentication process of multi-node dynamic warning and compliance supervision, including the following steps: S1. Dynamically generate rule strategies, where the rule strategies are based on real-time business status and historical execution data; S2. Integrate multi-source data to construct a global task dependency relationship to support overdue risk prediction; S3. Predict the task overdue probability and dynamically adjust the priority according to the global task dependency relationship and dynamic rule strategies; S4. Perform automated compliance verification based on the adjusted priority and dynamic rule strategies to generate an immutable evidence deposit; S5. Optimize the rule strategies and prediction models according to the compliance verification results and task execution data closed-loop.

[0079] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A certification process management system for multi-node dynamic warning and compliance supervision, characterized in that Including: A dynamic rule generation module, used to generate dynamic rule policies based on real-time business status; A real-time data integration module, connected to the dynamic rule generation module, used to integrate multi-source data and construct global task dependencies; A predictive intervention module, connected to the real-time data integration module, used to predict overdue risks according to the global task dependencies and dynamically adjust task priorities; A compliance verification module, connected to the predictive intervention module, used to perform automated compliance verification based on the dynamic rule policies and generate digital evidence; A closed-loop feedback module, connected to the dynamic rule generation module, the real-time data integration module and the compliance verification module, used to optimize rule policies and model parameters according to compliance verification results and task execution data.

2. The authentication process management system for multi-node dynamic warning and compliance supervision according to claim 1, characterized in that, The dynamic rule generation module generates rule policies through the following formula: ； Where: , : The total number of tasks to be processed in the current decision-making cycle; Indicates the number of violations, : The total number of violation events detected by the system within the same period; , is a weight coefficient dynamically adjusted by Monte Carlo tree search.

3. The authentication process management system for multi-node dynamic warning and compliance supervision according to claim 1, wherein The predictive intervention module calculates dynamic priorities through the following formula: ； Where: Indicates the task urgency, Indicates the impact scope, Indicates the resource requirements; is the overdue probability predicted by the temporal convolutional network; is the slope adjustment parameter, is the overdue probability threshold.

4. The authentication process management system for multi-node dynamic warning and compliance supervision according to claim 1, characterized in that, The compliance verification module realizes the joint optimization of federated learning through the following formula: ； Where: For the loss of the shared model on the global data on, represents a shared model for learning cross-departmental general compliance patterns; is the loss of the local model on the department data and represents the local model for adapting to the characteristics of department private data; represents the local model for adapting to the characteristics of department private data; , as the balance weight.

5. The authentication process management system for multi-node dynamic warning and compliance supervision according to claim 1, wherein The real-time data integration module includes: A distributed graph database, used to store nodes and dependencies of tasks, resources and departments; A causal inference model, based on a graph neural network to analyze the root nodes of task overdue.

6. The authentication process management system for multi-node dynamic warning and compliance supervision according to claim 1, wherein The closed-loop feedback module optimizes the system in the following ways: Incrementally update the parameters of the reinforcement learning strategy; Incrementally update the causal inference model of the graph neural network.

7. Method for managing the authentication process of multi-node dynamic warning and compliance supervision, adopting the multi-node dynamic warning and compliance supervision authentication process management system described in any one of claims 1-6, characterized in that Including the following steps: Dynamically generate rule policies, which are based on real-time business status and historical execution data; Integrate multi-source data and construct global task dependencies to support overdue risk prediction; According to the global task dependencies and dynamic rule policies, predict the task overdue probability and dynamically adjust priorities; Perform automated compliance verification based on the adjusted priorities and dynamic rule policies, and generate tamper-proof digital evidence; Closely optimize rule policies and prediction models according to compliance verification results and task execution data.

8. The authentication process management method for multi-node dynamic warning and compliance supervision according to claim 7, characterized in that, The dynamic generation of rule policies includes the following steps: Dynamically adjust the weights of efficiency and compliance objectives based on the reinforcement learning algorithm; Optimize the weight allocation of the multi-objective equation through Monte Carlo tree search.

9. The authentication process management method for multi-node dynamic warning and compliance supervision according to claim 7, wherein The integration of multi-source data includes the following steps: Construct a global task dependency graph and store the relationship between nodes and edges through a graph database; Locate the root nodes of task overdue based on the causal inference model.

10. The authentication process management method for multi-node dynamic warning and compliance supervision according to claim 7, characterized in that, The dynamic adjustment of priorities includes the following steps: Use a temporal convolutional network to predict the overdue probability; Combine game theory algorithms to calculate priorities and trigger resource reallocation.

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