Abnormality detection emergency processing system and method based on artificial intelligence

Abstract

The invention relates to the technical field of artificial intelligence, and discloses an anomaly detection emergency processing system and method based on artificial intelligence, and the system comprises a data collection module; a data preprocessing module; an anomaly detection module; an emergency decision module; an emergency execution module; a real-time monitoring and state feedback module; a multi-mode communication and coordination module; a man-machine interaction and visualization module; and a knowledge updating and model iteration module. The method is reasonable in design, the accuracy and timeliness of anomaly detection are remarkably improved through a multi-source heterogeneous data fusion and dynamic threshold adjustment technology, and the model robustness is enhanced in combination with incremental learning and an adversarial training mechanism; the intelligent decision-making module realizes multi-objective optimization processing by relying on a knowledge graph and a digital twinborn pre-judgment risk; redundant fault-tolerant execution and distributed consistency guarantee ensure high reliability of the system, and a man-machine cooperation mechanism considers both automation efficiency and manual intervention accuracy.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to an artificial intelligence-based anomaly detection emergency processing system and method. Background Art

[0002] In the Industrial Internet of Things (IIoT) and intelligent manufacturing sectors, the reliability of anomaly detection and emergency response systems is directly linked to production safety and operational efficiency. Traditional approaches typically rely on a single data source (such as a SCADA system) and fixed threshold rules, resulting in three drawbacks: First, protocol differences and transmission delays across heterogeneous data sources hinder the effective integration of multimodal information, and data fragmentation can easily lead to misjudgments; second, static rule-based detection models cannot adapt to complex scenarios such as dynamic equipment aging and sudden changes in operating conditions, resulting in weak defenses against attacks; and third, emergency decision-making often relies on manual experience, lacking global risk prediction and self-optimization mechanisms. Delayed response can lead to secondary failures.

[0003] Existing technologies attempt to improve detection accuracy through machine learning, but face two major bottlenecks: Offline training models struggle to adapt to the drift of streaming data distribution and are prone to catastrophic forgetting during online updates. Furthermore, the separation of detection and execution modules results in a lack of feedback loops, and the rigidity of the rule base hinders effective response. Furthermore, industrial sites are prone to frequent network fluctuations and hardware failures, and traditional systems lack redundant fault tolerance and consistency assurance mechanisms. Command conflicts or execution timeouts can lead to systemic failures. Therefore, we propose an AI-based anomaly detection and emergency response system and method to address this issue. Summary of the Invention

[0004] The purpose of the present invention is to solve the shortcomings mentioned in the above background technology and to propose an artificial intelligence-based anomaly detection emergency processing system and method.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: An artificial intelligence-based anomaly detection and emergency response system, comprising: A data acquisition module that collects data from multiple data sources, including but not limited to sensor networks, database systems, log files, and network traffic; The data preprocessing module performs preprocessing operations on the collected raw data, including but not limited to cleaning, normalization and feature extraction; The anomaly detection module uses a combination of multiple artificial intelligence algorithms to perform anomaly detection; The emergency decision-making module makes intelligent decisions based on the abnormal information provided by the anomaly detection module, combined with the pre-set emergency rule base and knowledge graph. The anomaly detection module outputs the abnormal results and related information and passes them to the emergency decision-making module; The emergency execution module takes corresponding emergency measures according to the plan formulated by the emergency decision module; The real-time monitoring and status feedback module continuously tracks the input status of the data acquisition module, monitors the operating status of each module, triggers the abnormality self-healing mechanism, and dynamically updates the sensitivity threshold of the anomaly detection algorithm based on historical data to avoid false positives or missed positives. Multimodal communication and coordination module, standardizing inter-module communication protocols to ensure real-time and sequential transmission of abnormal information; The human-computer interaction and visualization module displays real-time data streams, anomaly distribution heat maps, and emergency response execution progress, allowing operations personnel to manually adjust the emergency rule base, mark misjudgment samples, or override automatic decisions. The knowledge update and model iteration module incrementally trains the detection model based on newly generated abnormal data, automatically extracts effective strategies from emergency execution results, and updates the knowledge graph and emergency rules.

[0006] Preferably, the data acquisition module includes: Multi-source heterogeneous adaptation unit provides standardized interface protocol stacks for different data sources; Intelligent scheduling and caching units allocate transmission bandwidth based on data urgency. Lightweight cache pools are deployed at edge nodes close to data sources to perform operations such as deduplication and timestamp alignment to reduce network transmission pressure. When the network is interrupted, data is cached locally and retransmitted according to transaction consistency rules after recovery to avoid data loss. The security isolation and audit unit divides data flow security domains through FPGA or trusted execution environment, such as physical isolation of sensor data and network traffic.

[0007] Preferably, the data preprocessing module includes: The adaptive cleaning strategy unit dynamically identifies dirty data types based on the rule engine and machine learning model, automatically matches cleaning strategies, and uses a sliding window dynamic threshold method for time series data to avoid incorrect cleaning of seasonal data caused by fixed thresholds. The data quality assessment unit quantifies data distribution deviation through information entropy and KL divergence, triggers early warning, generates quality reports and feeds back to the acquisition module to guide sampling rate adjustment or equipment maintenance.

[0008] Preferably, the anomaly detection module includes: The hybrid model integration unit adopts the Stacking integration framework, uses XGBoost to dynamically weight the output of each base model, and switches the dominant model according to the scenario; Incremental learning and reasoning unit, deploying TensorRT or ONNX Runtime to achieve low-latency model reasoning, supporting online fine-tuning, and continuously optimizing model parameters using streaming data; The adversarial robustness enhancement unit introduces adversarial training and attention mechanisms to defend against adversarial attacks on AI models, implements Jacobian matrix monitoring on input data, and detects abnormal gradient mutations.

[0009] Preferably, the emergency decision-making module includes: Multi-objective optimization engine unit, based on NSGA-II algorithm to balance security and combine knowledge graph reasoning with device association impact; Dynamic rule evolution unit, which uses reinforcement learning to automatically optimize the weights of the rule base, and the reward function is based on the historical treatment effect score; The cross-domain collaborative decision-making unit integrates data from multiple plant areas, generates a global optimal strategy, calls on digital twins to simulate execution effects, and predicts potential secondary risks.

[0010] Preferably, the emergency execution module includes: The execution feedback closed-loop unit collects execution results in real time, evaluates the effectiveness of measures through the Kalman filter algorithm, and feeds back to the decision-making module for iterative optimization; The redundant fault-tolerant execution unit adopts a triple-module redundancy design. Three actuators receive instructions in parallel and output them according to the majority voting mechanism. It has a built-in hardware watchdog and automatically switches to the backup channel if there is no response after a timeout.

[0011] Preferably, the multimodal communication and coordination module includes: The protocol intelligent conversion unit has a built-in industrial protocol library, automatically identifies and converts data formats, and uses NLP technology to parse unstructured instructions and convert them into machine-operable commands; Low-latency message bus unit, using Zero-Copy technology and RDMA network acceleration to achieve microsecond-level message delivery; The distributed consistency unit implements multi-node state synchronization based on the Raft algorithm, ensures strong consistency of instruction execution, and uses CRDT to handle the final consistency of data in a weak network environment.

[0012] The present invention also provides an artificial intelligence-based anomaly detection and emergency processing method, which is applied to the above-mentioned artificial intelligence-based anomaly detection and emergency processing system, comprising the following steps: S1. Multi-source data collection and secure transmission: This involves connecting to sensors, databases, logs, and other data sources through a multi-source adapter unit. Physical isolation is achieved using a standardized protocol stack, bandwidth is allocated based on data priority, and a cache pool is deployed at the edge node to perform deduplication and timestamp alignment. Local caching and transactional consistency are implemented during network outages. Sensitive data streams are encrypted in a trusted execution environment, and the complete data flow path is recorded. S2. Adaptive data preprocessing and quality assessment: The rule engine identifies dirty data types, uses a sliding window dynamic threshold to process seasonal fluctuations in time series data, extracts mixed features from the time and frequency domains, uses the mutual information method to screen highly correlated features, calculates the KL divergence to monitor data distribution deviations, and triggers sampling rate adjustment instructions to the acquisition module. S3, Multi-model Collaborative Anomaly Detection: The Stacking framework integrates deep learning and traditional algorithms, XGBoost dynamically weights the outputs of each model, adversarial training enhances robustness, TensorRT accelerates online inference, uses streaming data to fine-tune model parameters, uses the Jacobian matrix to monitor gradient anomalies, automatically adjusts detection thresholds based on historical false alarm rates, and uses an attention mechanism to focus on key feature dimensions. S4. Intelligent emergency decision generation: The NSGA-II algorithm balances safety, cost, and efficiency. Digital twins simulate policy execution effects. Reinforcement learning optimizes rule weights based on treatment effect scores. Knowledge graphs infer the impact of equipment associations. Multi-site topology data is integrated to predict secondary risks and generate global treatment plans. S5, Redundant Fault-Tolerant Emergency Execution: The three actuators vote on the output of a majority vote, the hardware watchdog monitors and automatically switches channels when timeout occurs, the Kalman filter analyzes execution data, dynamically modifies control parameters, and feeds back execution delay, completion rate, and other indicators to the decision module for iterative strategy. S6, Real-time Monitoring and Status Tuning: Tracks the CPU, memory, and latency metrics of each module, triggers module-level restart and degradation when anomalies occur, automatically switches to a backup data source when a data flow interruption is detected, rolls back to a historical stable version when model performance degrades, automatically scales edge computing nodes based on traffic prediction, and distributes detection tasks using a load balancing algorithm. S7, Multimodal Communication Coordination: The industrial protocol library automatically parses protocols such as Modbus / OPC UA, the NLP engine converts natural language instructions, the RDMA network achieves microsecond-level cross-node communication, Zero-Copy technology reduces kernel state copying, the Raft algorithm strongly synchronizes key instructions, and the CRDT algorithm handles asynchronous data synchronization in weak network environments; S8. Human-machine collaboration and visualization: 3D topology maps present anomaly propagation paths, and heat maps display real-time risk level distribution. Operations and maintenance personnel can annotate false positive samples, override automatic decisions, adjust rule trigger thresholds, record all manual intervention operations, and associate timestamps with data snapshots for subsequent analysis. S9. Knowledge iteration and system upgrade: Automatically label new abnormal samples, enhance the model's generalization ability through comparative learning, extract patterns from successful handling cases, update the emergency rule base and knowledge graph association relationship, run the new model in parallel in the shadow system, and fully launch it after A / B testing verifies the effect.

[0013] Compared with the existing technology, the present invention provides an artificial intelligence-based anomaly detection and emergency response system and method, which has the following beneficial effects: 1. Multi-source heterogeneous adapter units integrate multi-dimensional data sources such as sensors, logs, and network traffic. A standardized protocol stack is used to achieve physical isolation and secure transmission. This technology, combined with sliding window dynamic threshold cleaning and time-frequency domain hybrid feature extraction, effectively eliminates data silos. The edge intelligent cache pool optimizes data consistency through deduplication and timestamp alignment, ensuring the spatiotemporal correlation of cross-modal data. This fusion mechanism can accurately capture hidden anomalies that are difficult to detect with traditional single-source detection, such as the combined effects of sensor drift and network attacks. This significantly improves the sensitivity and accuracy of early anomaly identification and avoids misjudgments or missed detections caused by data fragmentation. 2. Based on online incremental learning and adversarial robustness enhancement technology, the system continuously optimizes the dynamic thresholds and feature weights of the anomaly detection model. It monitors gradient mutations in input data using the Jacobian matrix and, combined with the NSGA-II multi-objective optimization algorithm, balances security and processing efficiency to achieve real-time tuning of detection strategies. When encountering new adversarial attacks or data distribution shifts, the streaming fine-tuning mechanism can quickly adapt to new scenarios, while the attention mechanism focuses on key feature dimensions to reduce noise interference. This dynamic adjustment capability enables the system to maintain high stability in complex industrial environments, effectively addressing challenges brought about by uncertain factors such as equipment aging and sudden changes in operating conditions. 3. Relying on the knowledge update and model iteration module, the system automatically extracts effective strategies from historical disposal cases, optimizes the weights of the emergency rule base through reinforcement learning, and enhances the model's generalization capabilities using comparative learning. Digital twin technology simulates the potential secondary risks of disposal plans, and combines it with Kalman filtering to evaluate execution effects, forming a feedback loop. This self-evolutionary mechanism not only achieves the coordinated optimization of detection models and decision rules, but also accumulates expert experience to form a reusable knowledge graph, allowing the system to gradually approach the optimal disposal logic over the long term, significantly reducing manual maintenance costs. 4. Data flow security domains are divided through FPGAs or trusted execution environments, implementing physical isolation and encrypted transmission. Combined with triple-module redundant actuators and a hardware watchdog mechanism, a full-link protection system from data acquisition to instruction execution is constructed. Adversarial training technology defends against injection attacks on AI models. The Raft algorithm and CRDT technology respectively ensure strong consistency and eventual consistency, preventing instruction conflicts caused by network partitions. The audit unit fully records data flow paths and manual intervention operations to ensure that abnormal events can be traced. This architecture not only improves the system's anti-attack capabilities, but also meets the stringent requirements of industrial scenarios for data integrity and business continuity. 5. The visualization module dynamically displays abnormal propagation paths and risk levels through 3D topology maps and heat maps, supporting operation and maintenance personnel to quickly locate core fault points. The rule library weight adjustment interface and false positive sample labeling function allow human experts to intervene in the decision-making process, forming a collaborative model of "machine-based, human-corrected". The natural language processing engine converts unstructured instructions into machine-executable commands, lowering the operational threshold. This design not only retains the efficient response advantage of the automated system, but also compensates for the algorithm's shortcomings in complex logical reasoning and empirical judgment through the human-computer interaction mechanism, achieving a balance between handling efficiency and decision-making quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a system block diagram of an artificial intelligence-based anomaly detection and emergency response system proposed by the present invention; Figure 2 This is a flow chart of an artificial intelligence-based anomaly detection emergency processing method proposed by the present invention. DETAILED DESCRIPTION

[0015] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0016] Reference Figure 1 , an artificial intelligence-based anomaly detection and emergency response system, comprising: A data acquisition module that collects data from multiple data sources, including but not limited to sensor networks, database systems, log files, and network traffic; The data preprocessing module performs preprocessing operations on the collected raw data, including but not limited to cleaning, normalization and feature extraction; The anomaly detection module uses a combination of multiple artificial intelligence algorithms to perform anomaly detection; The emergency decision-making module makes intelligent decisions based on the abnormal information provided by the anomaly detection module, combined with the pre-set emergency rule base and knowledge graph. The anomaly detection module outputs the abnormal results and related information and passes them to the emergency decision-making module; The emergency execution module takes corresponding emergency measures according to the plan formulated by the emergency decision module; The real-time monitoring and status feedback module continuously tracks the input status of the data acquisition module, monitors the operating status of each module, triggers the abnormality self-healing mechanism, and dynamically updates the sensitivity threshold of the anomaly detection algorithm based on historical data to avoid false positives or missed positives. Multimodal communication and coordination module, standardizing inter-module communication protocols to ensure real-time and sequential transmission of abnormal information; The human-computer interaction and visualization module displays real-time data streams, anomaly distribution heat maps, and emergency response execution progress, allowing operations personnel to manually adjust the emergency rule base, mark misjudgment samples, or override automatic decisions. The knowledge update and model iteration module incrementally trains the detection model based on newly generated abnormal data, automatically extracts effective strategies from emergency execution results, and updates the knowledge graph and emergency rules.

[0017] In this embodiment, the data acquisition module includes: Multi-source heterogeneous adaptation unit provides standardized interface protocol stacks for different data sources; Intelligent scheduling and caching units allocate transmission bandwidth based on data urgency. Lightweight cache pools are deployed at edge nodes close to data sources to perform operations such as deduplication and timestamp alignment to reduce network transmission pressure. When the network is interrupted, data is cached locally and retransmitted according to transaction consistency rules after recovery to avoid data loss. The security isolation and audit unit divides data flow security domains through FPGA or trusted execution environment, such as physical isolation of sensor data and network traffic.

[0018] In this embodiment, the data preprocessing module includes: The adaptive cleaning strategy unit dynamically identifies dirty data types based on the rule engine and machine learning model, automatically matches cleaning strategies, and uses a sliding window dynamic threshold method for time series data to avoid incorrect cleaning of seasonal data caused by fixed thresholds. The data quality assessment unit quantifies data distribution deviation through information entropy and KL divergence, triggers early warning, generates quality reports and feeds back to the acquisition module to guide sampling rate adjustment or equipment maintenance.

[0019] In this embodiment, the anomaly detection module includes: The hybrid model integration unit adopts the Stacking integration framework, uses XGBoost to dynamically weight the output of each base model, and switches the dominant model according to the scenario; Incremental learning and reasoning unit, deploying TensorRT or ONNX Runtime to achieve low-latency model reasoning, supporting online fine-tuning, and continuously optimizing model parameters using streaming data; The adversarial robustness enhancement unit introduces adversarial training and attention mechanisms to defend against adversarial attacks on AI models, implements Jacobian matrix monitoring on input data, and detects abnormal gradient mutations.

[0020] In this embodiment, the emergency decision-making module includes: Multi-objective optimization engine unit, based on NSGA-II algorithm to balance security and combine knowledge graph reasoning with device association impact; Dynamic rule evolution unit, which uses reinforcement learning to automatically optimize the weights of the rule base, and the reward function is based on the historical treatment effect score; The cross-domain collaborative decision-making unit integrates data from multiple plant areas, generates a global optimal strategy, calls on digital twins to simulate execution effects, and predicts potential secondary risks.

[0021] In this embodiment, the emergency execution module includes: The execution feedback closed-loop unit collects execution results in real time, evaluates the effectiveness of measures through the Kalman filter algorithm, and feeds back to the decision-making module for iterative optimization; The redundant fault-tolerant execution unit adopts a triple-module redundancy design. Three actuators receive instructions in parallel and output them according to the majority voting mechanism. It has a built-in hardware watchdog and automatically switches to the backup channel if there is no response after a timeout.

[0022] In this embodiment, the multimodal communication and coordination module includes: The protocol intelligent conversion unit has a built-in industrial protocol library, automatically identifies and converts data formats, and uses NLP technology to parse unstructured instructions and convert them into machine-operable commands; Low-latency message bus unit, using Zero-Copy technology and RDMA network acceleration to achieve microsecond-level message delivery; The distributed consistency unit implements multi-node state synchronization based on the Raft algorithm, ensures strong consistency of instruction execution, and uses CRDT to handle the final consistency of data in a weak network environment.

[0023] Reference Figure 2 The present invention also provides an artificial intelligence-based anomaly detection emergency processing method, which is applied to the above-mentioned artificial intelligence-based anomaly detection emergency processing system, comprising the following steps: S1. Multi-source data collection and secure transmission: This involves connecting to sensors, databases, logs, and other data sources through a multi-source adapter unit. Physical isolation is achieved using a standardized protocol stack, bandwidth is allocated based on data priority, and a cache pool is deployed at the edge node to perform deduplication and timestamp alignment. Local caching and transactional consistency are implemented during network outages. Sensitive data streams are encrypted in a trusted execution environment, and the complete data flow path is recorded. S2. Adaptive data preprocessing and quality assessment: The rule engine identifies dirty data types, uses a sliding window dynamic threshold to process seasonal fluctuations in time series data, extracts mixed features from the time and frequency domains, uses the mutual information method to screen highly correlated features, calculates the KL divergence to monitor data distribution deviations, and triggers sampling rate adjustment instructions to the acquisition module. S3, Multi-model Collaborative Anomaly Detection: The Stacking framework integrates deep learning and traditional algorithms, XGBoost dynamically weights the outputs of each model, adversarial training enhances robustness, TensorRT accelerates online inference, uses streaming data to fine-tune model parameters, uses the Jacobian matrix to monitor gradient anomalies, automatically adjusts detection thresholds based on historical false alarm rates, and uses an attention mechanism to focus on key feature dimensions. S4. Intelligent emergency decision generation: The NSGA-II algorithm balances safety, cost, and efficiency. Digital twins simulate policy execution effects. Reinforcement learning optimizes rule weights based on treatment effect scores. Knowledge graphs infer the impact of equipment associations. Multi-site topology data is integrated to predict secondary risks and generate global treatment plans. S5, Redundant Fault-Tolerant Emergency Execution: The three actuators vote on the output of a majority vote, the hardware watchdog monitors and automatically switches channels when timeout occurs, the Kalman filter analyzes execution data, dynamically modifies control parameters, and feeds back execution delay, completion rate, and other indicators to the decision module for iterative strategy. S6, Real-time Monitoring and Status Tuning: Tracks the CPU, memory, and latency metrics of each module, triggers module-level restart and degradation when anomalies occur, automatically switches to a backup data source when a data flow interruption is detected, rolls back to a historical stable version when model performance degrades, automatically scales edge computing nodes based on traffic prediction, and distributes detection tasks using a load balancing algorithm. S7, Multimodal Communication Coordination: The industrial protocol library automatically parses protocols such as Modbus / OPC UA, the NLP engine converts natural language instructions, the RDMA network achieves microsecond-level cross-node communication, Zero-Copy technology reduces kernel state copying, the Raft algorithm strongly synchronizes key instructions, and the CRDT algorithm handles asynchronous data synchronization in weak network environments; S8. Human-machine collaboration and visualization: 3D topology maps present anomaly propagation paths, and heat maps display real-time risk level distribution. Operations and maintenance personnel can annotate false positive samples, override automatic decisions, adjust rule trigger thresholds, record all manual intervention operations, and associate timestamps with data snapshots for subsequent analysis. S9. Knowledge iteration and system upgrade: Automatically label new abnormal samples, enhance the model's generalization ability through comparative learning, extract patterns from successful handling cases, update the emergency rule base and knowledge graph association relationship, run the new model in parallel in the shadow system, and fully launch it after A / B testing verifies the effect.

[0024] In this embodiment, an adaptation unit is first deployed at the multi-source heterogeneous data end, and the sensor network, database and log system are accessed through a standardized protocol stack. The edge node is configured with an intelligent scheduling and caching unit, and the transmission bandwidth is dynamically allocated according to the data priority and timestamp alignment and deduplication are performed. During the network interruption, the local cache pool is enabled to temporarily store data and retransmit it according to the transaction consistency rules after recovery to ensure data integrity and timeliness; in the preprocessing stage, an adaptive cleaning strategy unit is used to dynamically identify data anomaly patterns, and the periodic fluctuations of time series data are processed based on the sliding window dynamic threshold. At the same time, the time-frequency domain mixed features are extracted and the key feature vectors are screened through the mutual information method. The data quality assessment unit calculates the distribution offset index in real time and feeds it back to the acquisition module to adjust the sampling frequency; the anomaly detection module integrates deep learning models and traditional algorithms, dynamically weights the outputs of each base model based on the Stacking framework, and uses the streaming data in combination with the online incremental learning mechanism to fine-tune the model parameters, and enhances the model's robustness to noise and adversarial samples through adversarial training; the emergency decision module calls the knowledge graph and Historical disposal cases generate multi-objective optimization strategies, use digital twin technology to simulate execution effects and predict potential risks, and the dynamic rule evolution unit adjusts the rule base weights based on real-time feedback. During the execution phase, instructions are received in parallel through three-module redundant executors and output according to a majority voting mechanism. The hardware watchdog monitors execution timeouts and automatically switches to the backup channel. The execution effect is evaluated by the Kalman filter and fed back to the decision module for iterative optimization. The real-time monitoring module tracks the resource status of each node throughout the entire process. When the network fluctuates, the CRDT algorithm is triggered to ensure the eventual consistency of the data. The self-healing mechanism automatically switches to redundant nodes or rolls back to a stable version when a module failure is detected. The human-computer interaction interface visualizes the distribution of anomalies and the progress of disposal through 3D topology maps and heat maps, supporting operation and maintenance personnel to manually mark false alarm samples or adjust decision thresholds. The knowledge update module automatically extracts the characteristic patterns of successful disposal cases, incrementally trains the detection model and updates the knowledge graph association relationship. The new model is verified by the shadow system and then released to the production environment in grayscale, forming a closed-loop operation body from data perception to autonomous evolution.

Claims

1. An artificial intelligence-based anomaly detection and emergency response system, characterized in that: include: A data acquisition module that collects data from multiple data sources, including but not limited to sensor networks, database systems, log files, and network traffic; The data preprocessing module performs preprocessing operations on the collected raw data, including but not limited to cleaning, normalization and feature extraction; The anomaly detection module uses a combination of multiple artificial intelligence algorithms to perform anomaly detection; The emergency decision-making module makes intelligent decisions based on the abnormal information provided by the anomaly detection module, combined with the pre-set emergency rule base and knowledge graph. The anomaly detection module outputs the abnormal results and related information and passes them to the emergency decision-making module; The emergency execution module takes corresponding emergency measures according to the plan formulated by the emergency decision module; The real-time monitoring and status feedback module continuously tracks the input status of the data acquisition module, monitors the operating status of each module, triggers the abnormality self-healing mechanism, and dynamically updates the sensitivity threshold of the anomaly detection algorithm based on historical data to avoid false positives or missed positives. Multimodal communication and coordination module, standardizing inter-module communication protocols to ensure real-time and sequential transmission of abnormal information; The human-computer interaction and visualization module displays real-time data streams, anomaly distribution heat maps, and emergency response execution progress, allowing operations personnel to manually adjust the emergency rule base, mark misjudgment samples, or override automatic decisions. The knowledge update and model iteration module incrementally trains the detection model based on newly generated abnormal data, automatically extracts effective strategies from emergency execution results, and updates the knowledge graph and emergency rules.

2. The artificial intelligence-based anomaly detection and emergency response system according to claim 1 is characterized in that: The data acquisition module includes: Multi-source heterogeneous adaptation unit provides standardized interface protocol stacks for different data sources; Intelligent scheduling and caching units allocate transmission bandwidth based on data urgency. Lightweight cache pools are deployed at edge nodes close to data sources to perform operations such as deduplication and timestamp alignment to reduce network transmission pressure. When the network is interrupted, data is cached locally and retransmitted according to transaction consistency rules after recovery to avoid data loss. The security isolation and audit unit divides data flow security domains through FPGA or trusted execution environment, such as physical isolation of sensor data and network traffic.

3. The artificial intelligence-based anomaly detection and emergency response system according to claim 1 is characterized in that: The data preprocessing module includes: The adaptive cleaning strategy unit dynamically identifies dirty data types based on the rule engine and machine learning model, automatically matches cleaning strategies, and uses a sliding window dynamic threshold method for time series data to avoid incorrect cleaning of seasonal data caused by fixed thresholds. The data quality assessment unit quantifies data distribution deviation through information entropy and KL divergence, triggers early warning, generates quality reports and feeds back to the acquisition module to guide sampling rate adjustment or equipment maintenance.

4. The artificial intelligence-based anomaly detection and emergency response system according to claim 1 is characterized in that: The anomaly detection module includes: The hybrid model integration unit adopts the Stacking integration framework, uses XGBoost to dynamically weight the output of each base model, and switches the dominant model according to the scenario; Incremental learning and reasoning unit, deploying TensorRT or ONNX Runtime to achieve low-latency model reasoning, supporting online fine-tuning, and continuously optimizing model parameters using streaming data; The adversarial robustness enhancement unit introduces adversarial training and attention mechanisms to defend against adversarial attacks on AI models, implements Jacobian matrix monitoring on input data, and detects abnormal gradient mutations.

5. The artificial intelligence-based anomaly detection and emergency response system according to claim 1 is characterized in that: The emergency decision-making module includes: Multi-objective optimization engine unit, based on NSGA-II algorithm to balance security and combine knowledge graph reasoning with device association impact; Dynamic rule evolution unit, which uses reinforcement learning to automatically optimize the weights of the rule base, and the reward function is based on the historical treatment effect score; The cross-domain collaborative decision-making unit integrates data from multiple plant areas, generates a global optimal strategy, calls on digital twins to simulate execution effects, and predicts potential secondary risks.

6. The artificial intelligence-based anomaly detection and emergency response system according to claim 1, characterized in that: The emergency execution module includes: The execution feedback closed-loop unit collects execution results in real time, evaluates the effectiveness of measures through the Kalman filter algorithm, and feeds back to the decision-making module for iterative optimization; The redundant fault-tolerant execution unit adopts a triple-module redundancy design. Three actuators receive instructions in parallel and output them according to the majority voting mechanism. It has a built-in hardware watchdog and automatically switches to the backup channel if there is no response after a timeout.

7. The artificial intelligence-based anomaly detection and emergency response system according to claim 1 is characterized in that: The multimodal communication and coordination module includes: The protocol intelligent conversion unit has a built-in industrial protocol library, automatically identifies and converts data formats, and uses NLP technology to parse unstructured instructions and convert them into machine-operable commands; Low-latency message bus unit, using Zero-Copy technology and RDMA network acceleration to achieve microsecond-level message delivery; The distributed consistency unit implements multi-node state synchronization based on the Raft algorithm, ensures strong consistency of instruction execution, and uses CRDT to handle the final consistency of data in a weak network environment.

8. An artificial intelligence-based anomaly detection and emergency processing method, applied to the artificial intelligence-based anomaly detection and emergency processing system according to claim 7, characterized in that: The following steps are involved: S1. Multi-source data collection and secure transmission: This involves connecting to sensors, databases, logs, and other data sources through a multi-source adapter unit. Physical isolation is achieved using a standardized protocol stack, bandwidth is allocated based on data priority, and a cache pool is deployed at the edge node to perform deduplication and timestamp alignment. Local caching and transactional consistency are implemented during network outages. Sensitive data streams are encrypted in a trusted execution environment, and the complete data flow path is recorded. S2. Adaptive data preprocessing and quality assessment: The rule engine identifies dirty data types, uses a sliding window dynamic threshold to process seasonal fluctuations in time series data, extracts mixed features from the time and frequency domains, uses the mutual information method to screen highly correlated features, calculates the KL divergence to monitor data distribution deviations, and triggers sampling rate adjustment instructions to the acquisition module. S3, Multi-model Collaborative Anomaly Detection: The Stacking framework integrates deep learning and traditional algorithms, XGBoost dynamically weights the outputs of each model, adversarial training enhances robustness, TensorRT accelerates online inference, uses streaming data to fine-tune model parameters, uses the Jacobian matrix to monitor gradient anomalies, automatically adjusts detection thresholds based on historical false alarm rates, and uses an attention mechanism to focus on key feature dimensions. S4. Intelligent emergency decision generation: The NSGA-II algorithm balances safety, cost, and efficiency. Digital twins simulate policy execution effects. Reinforcement learning optimizes rule weights based on treatment effect scores. Knowledge graphs infer the impact of equipment associations. Multi-site topology data is integrated to predict secondary risks and generate global treatment plans. S5, Redundant Fault-Tolerant Emergency Execution: The three actuators vote on the output of a majority vote, the hardware watchdog monitors and automatically switches channels when timeout occurs, the Kalman filter analyzes execution data, dynamically modifies control parameters, and feeds back execution delay, completion rate, and other indicators to the decision module for iterative strategy. S6, Real-time Monitoring and Status Tuning: Tracks the CPU, memory, and latency metrics of each module, triggers module-level restart and degradation when anomalies occur, automatically switches to a backup data source when a data flow interruption is detected, rolls back to a historical stable version when model performance degrades, automatically scales edge computing nodes based on traffic prediction, and distributes detection tasks using a load balancing algorithm. S7, Multimodal Communication Coordination: The industrial protocol library automatically parses protocols such as Modbus / OPCUA, the NLP engine converts natural language instructions, the RDMA network achieves microsecond-level cross-node communication, Zero-Copy technology reduces kernel state copying, the Raft algorithm strongly synchronizes key instructions, and the CRDT algorithm handles asynchronous data synchronization in weak network environments; S8. Human-machine collaboration and visualization: 3D topology maps present anomaly propagation paths, and heat maps display real-time risk level distribution. Operations and maintenance personnel can annotate false positive samples, override automatic decisions, adjust rule trigger thresholds, record all manual intervention operations, and associate timestamps with data snapshots for subsequent analysis. S9. Knowledge iteration and system upgrade: Automatically label new abnormal samples, enhance the model's generalization ability through comparative learning, extract patterns from successful handling cases, update the emergency rule base and knowledge graph association relationship, run the new model in parallel in the shadow system, and fully launch it after A / B testing verifies the effect.

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