18289 results about "Anomaly detection" patented technology

The present disclosure relates to a blockchain-based authentication system for electric vehicles and drones, and a method for authenticating electric vehicles or drones in a smart city. The proposed blockchain-based authentication system enhances security, privacy, and scalability for electric vehicles (EVs) and drones in smart city environments. It employs a consortium blockchain managed by city authorities and stakeholders, utilizing smart contracts for identity registration, credential issuance, and access control. Device nodes store cryptographic keys securely, while an optional authentication server facilitates off-chain integration. The authentication operation of the system includes, ensuring tamper-proof identity verification through on-chain validation. Authentication attempts are immutably recorded for auditing and anomaly detection. By eliminating single points of failure and strengthening data privacy, this decentralized authentication framework offers a scalable and secure solution for integrating EVs and drones into smart city infrastructures.

The invention provides a petrochemical production process anomaly diagnosis and optimization method and system integrated with a knowledge graph, and relates to the technical field of petrochemical production, and the method comprises the steps: collecting technological parameters and equipment state data, carrying out the preprocessing and feature extraction through adaptive wavelet threshold denoising and depth variational mode decomposition, and carrying out the feature extraction; a heterogeneous graph neural network is constructed, a causal relationship graph is constructed in combination with a neural causal discovery network, the causal relationship graph is optimized through knowledge distillation, missing relationships are complemented to obtain a knowledge graph, real-time features are compared with normal feature distribution of a pre-trained contrast diffusion model, and after anomaly detection is triggered, the real-time features are extracted. A causal inference engine is combined with a knowledge graph to analyze an anomaly propagation path, an anomaly root and a prediction trend are determined based on multi-agent reinforcement learning, a multi-objective optimization problem is constructed according to the knowledge graph, an optimization strategy is generated and dynamically adjusted, and an optimization result is evaluated by combining a knowledge distillation hybrid model. And finally, progressive optimization is realized and the knowledge graph is updated.

The present invention relates to the technical field of monitoring, in particular to a remote monitoring method and system for an aviation obstruction light. The method comprises the following steps: on the basis of an external sensor, acquiring electromagnetic signals sent by an aviation obstruction light; and by means of using a signal processing algorithm, processing the obtained original signals to eliminate noise interference and standardize the signal format, so as to generate signal-purified data. Using a support vector machine and a random forest algorithm in the present invention enhances the fault mode identification capability and the accuracy of predicting device performance degradation trends, and substantially improves the reliability of fault prediction; the combination of a Kalman filter and a multi-level decision tree provides powerful support for the integration and analysis of multi-source data, thereby ensuring the comprehensiveness and effectiveness of decision-making support information; and using a self-organizing map network and U matrix visualization technology not only shows advantages in the aspects of data mode identification and anomaly detection, but also improves the interpretability of data analysis by means of visual image displaying.

The invention discloses an equipment anomaly detection method and system based on multi-source heterogeneous data, and the method comprises the steps: obtaining a real-time multi-source monitoring data flow of production equipment in a smart park, carrying out the cross-modal data alignment processing of the real-time multi-source monitoring data flow, and generating a target monitoring data set, and executing a dynamic feature extraction operation in each edge computing node, generating a multi-dimensional equipment state feature set based on the target monitoring data set, inputting the multi-dimensional equipment state feature set into the trained anomaly detection integrated model, generating equipment anomaly probability distribution through a multi-level feature fusion strategy, and outputting the equipment anomaly probability distribution. Determining the abnormity type and abnormity confidence of the target equipment according to the equipment abnormity probability distribution, generating an equipment maintenance instruction set based on the abnormity type and the abnormity confidence, and sending the equipment maintenance instruction set to an equipment management terminal of the smart park to trigger abnormity processing operation; therefore, the automation degree and decision reliability of the equipment maintenance response of the smart park are improved.

A network mapping behavior anomaly detection method and system based on machine learning is provided. The method includes: collecting dual-source traffic data, generating a structured log data set through dual-source log fusion engine; performing subgraph matching calculation to obtain a mapping behavior deviation degree; generating communication data containing a watermark identifier in a session corresponding communication path; verifying whether attack events carry the watermark identifier; generating a network mapping behavior anomaly detection report. According to the disclosure, an adaptive attack behavior model is constructed through a multi-modal feature vector based on structured logs and a graph protocol mapping rule base, so that the cognitive robustness to protocol camouflage and path drift is fundamentally enhanced, a real-time verification chain of detection results is built, and traditional passive detection is transformed into self-proof active defense through cross verification of watermark carrying state and behavior trajectory.

The invention relates to the technical field of data processing, and discloses a financial transaction anomaly detection and risk assessment method and device based on artificial intelligence. The method comprises the following steps: collecting and preprocessing multi-source financial transaction related data to obtain a multi-dimensional transaction data set; performing feature engineering and abnormal mode recognition on the multi-dimensional transaction data set to obtain an abnormal transaction recognition result; based on the abnormal transaction identification result and the multi-dimensional transaction data, constructing a financial domain knowledge graph to obtain a relation network between transaction entities; performing multi-dimensional risk assessment on the relation network among the transaction entities to obtain a risk score and risk description data; based on the risk score, performing time sequence analysis and trend extrapolation on the risk description data to obtain a dynamic risk early warning signal; and performing scene simulation and multi-factor analysis on the dynamic risk early warning signal to obtain a risk management strategy suggestion. The efficiency and accuracy of financial transaction anomaly detection and risk assessment based on artificial intelligence are improved.

The invention provides a method for participating in primary frequency modulation control of a power system based on a modular large-scale hydrogen production power supply, and the method comprises the steps: obtaining an original data flow from a high-precision synchronous phasor measurement unit, an electrolytic cell and a submodule, and carrying out the multi-scale decomposition and noise reduction processing after time synchronization and anomaly detection restoration; performing state estimation by using unscented Kalman filtering, extracting frequency disturbance characteristics in combination with empirical mode decomposition and Hilbert transform, and estimating the inertia of the power grid; an electrolytic cell dynamic model is established, and parameter identification and optimization are carried out through a recursive least square method; a control strategy is designed based on a multi-time scale control framework, virtual synchronous generator parameters are optimized through online Gaussian process regression, and frequency modulation control is realized in combination with scene tree random model predictive control and minimum-maximum regret learning; and finally, carrying out coordination control through a distributed consistency algorithm and carrying out multi-target performance evaluation optimization. And the frequency modulation performance and the operation efficiency of the system are improved.

The invention discloses a multi-modal AI-based power equipment anomaly detection method and system, and the method comprises the steps: synchronously collecting electrical, mechanical and thermal modal data of power equipment through an edge computing node, carrying out the load adaptive dynamic preprocessing, and uploading the data to a cloud end; the cloud constructs a multi-modal feature extraction network based on a structural causal model, analyzes a causal path between modals through a Bayesian network and performs weighted fusion on feature vectors; capturing device state mutation by using a gating attention mechanism, and updating the feature vector; executing time-space consistency verification of the equipment group to identify regional group abnormality and suppress single-point misinformation; generating an interpretable report containing an abnormal root cause analysis and priority ranking maintenance strategy; and establishing a closed-loop feedback mechanism to correct the cause and effect probability distribution of the Bayesian network model. The system comprises a multi-modal sensor array, an edge computing node and a cloud analysis platform, wherein the cloud analysis platform is integrated with a causal reasoning engine, a space-time consistency verification module and the like. According to the invention, by analyzing the multi-modal deep causal association, the method adapts to the dynamic change of the equipment, reduces the false alarm rate, generates an interpretable report, supports closed-loop self-optimization, and improves the anomaly detection accuracy and operation and maintenance decision efficiency of the power equipment.

The invention belongs to the technical field of equipment management, and discloses an industrial equipment fault prediction and health management method based on multi-sensor fusion, and the method comprises the steps: obtaining multi-source sensing data of industrial equipment, carrying out the signal decoupling analysis, and obtaining a decoupling characteristic spectrum; performing frequency domain conversion and modulation analysis to form a multi-dimensional characteristic spectrum system; analyzing the modal correlation of the multi-dimensional feature pedigree to obtain a fault feature mapping network; a mixed time sequence prediction model is constructed, residual life prediction and degradation trend evaluation are carried out, and an equipment health trend graph is obtained; establishing a health state evaluation index system, and performing reliability evaluation to obtain an equipment health state report; and generating a maintenance decision suggestion, and realizing real-time anomaly detection and maintenance suggestion pushing through edge calculation. Through multi-sensor data fusion and advanced analysis technologies, early warning and accurate prediction of industrial equipment faults are realized, and the operation reliability and production efficiency of the industrial equipment are remarkably improved.

The invention relates to the technical field of deep learning, and discloses an MES digital collaborative management method and system based on deep learning, and the method comprises the steps: collecting multi-source heterogeneous data, and constructing a dimensionless multi-dimensional data set; analyzing a dynamic association relationship among the data items through node feature embedding and edge relationship learning, and generating a production line operation dependency relationship graph; performing collaborative anomaly detection and root cause positioning in combination with the dynamic association weight between the data items to obtain causal association data between the abnormal event and the data parameters; constructing a digital twinborn simulation environment to simulate an influence path of heterogeneous data parameter intervention production disturbance on collaborative anomaly so as to evaluate an anomaly influence result; constructing an iterative scheduling model according to a deep learning framework, and iteratively generating a multi-objective optimized collaborative scheduling strategy to realize dynamic configuration and exception prevention among heterogeneous data; therefore, collaborative optimization management of production operation parameter configuration and abnormity prevention of multi-source heterogeneous data in large-scale production with high dimension, high complexity and dynamic change can be realized.

The invention belongs to the technical field of automobile parts, and discloses an automobile part enterprise supply chain risk early warning method based on artificial intelligence. Comprising the steps that supply chain data are collected and processed, and a graph is constructed; evaluating the suppliers based on the atlas to generate a portrait matrix; on the basis of the portrait matrix and in combination with the production parameters, model training is performed, a prediction engine is constructed, and a part quality risk prediction result is generated; performing anomaly detection on the nodes to form a monitoring network, and generating a risk assessment result; constructing a supply chain network topology model based on the map, and performing risk propagation path analysis to generate a risk conduction map; establishing a risk assessment model, integrating the risk prediction result, the risk assessment result and the risk conduction diagram, and performing integrated assessment on the risk of each link of the supply chain to form a scoring system; based on a scoring system, a dynamic risk early warning threshold is generated, a risk response decision tree is constructed, intelligent risk response suggestions are provided, and the enterprise risk disposal efficiency is improved.

The invention provides an intelligent Internet of Things public security management and control system and method based on multi-source data fusion, and belongs to the technical field of Internet of Things security. According to the system, unified collection and standardized processing of multi-source data are achieved by recognizing a system operation scene and loading corresponding model parameters and response strategies, and a unified semantic representation structure is constructed. And the system executes anomaly detection at an edge node, completes risk scoring and grading alarm judgment in combination with a semantic structure and anomaly feature information, and generates a control response instruction based on strategy matching. Meanwhile, dynamic optimization of the scoring model and identity authentication, behavior auditing and data compliance export in the operation process are supported. The system has intelligent identification, adaptive analysis and response closed loop capabilities, and improves the precision and credibility of public security management in the Internet of Things environment.

The invention discloses a server cooperative control method, a storage medium and electronic equipment, and relates to the technical field of firmware, and the method comprises the steps: employing a programmable logic device to actively take over the control authority of target equipment based on the operation parameters of a sensing device in a server starting stage, stable operation of equipment before initialization of the substrate management control device is not completed is ensured, a mechanism that a preset communication link and the substrate management control device are subjected to interactive verification is adopted, and the functional integrity and communication reliability of the substrate management control device during permission transfer are ensured through bidirectional communication verification. A dynamic permission recovery strategy based on real-time anomaly detection is adopted, control right reverse switching is triggered through a sensing device and communication link state dual monitoring mechanism, and the purpose of actively taking over equipment management responsibilities when a substrate management control device runs abnormally is achieved. Therefore, double improvement of system fault self-healing capability and equipment control continuity is realized, and the problem of difficulty in cooperative control among heterogeneous units is solved.

The invention relates to the technical field of judicial data security, and discloses a judicial system confidential data security circulation method based on a block chain technology. Collecting multi-source judicial data, identifying sensitive information through a large language model, and performing differential privacy desensitization processing; constructing an SM4 encryption and TLS 1.3 end-to-end secure channel, generating a data hash fingerprint, and writing the data hash fingerprint into an alliance chain evidence based on a PBFT consensus mechanism; designing a multi-modal classification engine to perform feature extraction and intelligent classification; establishing a hybrid authority model to integrate an XACML strategy and a Kafka queue, and implementing dynamic authority control in combination with an RBAC / ABAC mechanism; a hierarchical encryption storage architecture is constructed, and homomorphic encryption retrieval and erasure code distributed storage are adopted; constructing a judicial knowledge graph based on a BERT model; and deploying a block chain auditing system, and combining LSTM anomaly detection and a DREAD risk assessment model to form a closed-loop risk control system. According to the method, the problems of security risk and privacy disclosure in judicial data cross-department circulation are solved.

The invention relates to the technical field of computer networks, and discloses a computing power network real-time performance evaluation method based on an analytic hierarchy process, and the method comprises the steps: collecting a node operation state and task demand data through a sensor, and generating a local performance index in combination with an edge quantum algorithm; simulating a future network state by using digital twinning, and fusing to generate a multi-dimensional performance data set; the AHP weight is dynamically adjusted based on resource deviation and a geological classification model, high-frequency updating is started for high load / fault, and the weight range is expanded for low load; introducing a risk assessment algorithm to quantify a performance-cost-carbon effect conflict level, and triggering resource recovery, optimization prompt or single index suggestion; scheduling strategies are triggered in a grading mode according to evaluation results, and active intervention is started in combination with anomaly detection; the AHP weight is dynamically updated through reinforcement learning, and quantum-classical hybrid algorithm parameters and block chain verification weight are automatically optimized. The real-time response efficiency and the resource utilization rate of the computing power network can be improved.

The present invention relates to a railway anomaly detection method and system based on multi-modal data fusion. The method comprises: separately encoding acquired modal data in a railway environment, and concatenating encoded modal data features, wherein the modal data comprises a one-dimensional vibration signal, two-dimensional image data, and 3D point cloud information; performing automatic weight classification on the concatenated multi-modal data features on the basis of an attention mechanism to obtain a weighted feature vector fused with multi-modal information; and adding a location code to the feature vector and using same as an input of an SAM encoder to obtain a segmentation result, and determining an anomaly condition of a railway on the basis of the segmentation result. The present invention achieves high monitoring accuracy, a real-time response capability, and specificity.

The invention discloses an industrial equipment real-time monitoring system based on edge computing, and the system comprises the following steps: collecting, processing and analyzing the operation data of industrial equipment through distributed edge computing nodes, constructing a node cooperation mechanism, and completing the processing of the data; the dynamic data scheduling module is used for dynamically optimizing a data transmission path and task allocation according to node performance; the intelligent model dynamic migration module is used for adjusting lightweight model deployment in combination with node resource conditions; the multi-modal fault sensing module is used for extracting and analyzing multi-modal features by using a cross-node collaborative algorithm; the edge node self-adaptive management module is used for adjusting node operation parameters in real time and repairing an abnormal state; and the equipment relevance reasoning module is used for constructing an equipment relevance model through a graph calculation method and generating a real-time relevance graph. According to the method, efficient monitoring and anomaly detection of industrial equipment are realized through resource optimization and intelligent analysis of collaborative edge computing nodes.

The invention discloses a smart road driving time prediction method based on multi-source data fusion and deep learning, and the method comprises the steps: constructing a multi-source data collection and fusion platform, developing a spatial-temporal feature deep learning model, deploying a real-time data processing and anomaly detection system, building a self-adaptive prediction optimization mechanism, and constructing a smart traffic application service platform. Comprehensive acquisition, deep analysis, real-time processing, accurate prediction and intelligent service of traffic data are realized. The method specifically comprises the following steps: deploying an Internet of Things sensor network to collect multi-source data, and designing an adaptive weighted fusion algorithm to construct a multi-dimensional dynamic traffic database; constructing an LSTM-GNN hybrid neural network architecture to extract spatio-temporal features; real-time data processing and anomaly detection are carried out by adopting a streaming computing framework and various technologies; designing a multi-objective optimization algorithm and a reinforcement learning model to realize adaptive prediction optimization; and developing a visual decision support system and a personalized navigation service. The system effectively improves the traffic management efficiency and the public travel experience.

The invention provides a remote office network security protection method and system based on big data, and the method comprises the steps: firstly obtaining network activity data, including traffic, equipment states and user behavior data, of a plurality of terminal equipment in a remote office scene, and then carrying out the multi-dimensional security feature analysis of the network activity data, after a dynamic security feature set containing traffic, device fingerprints and behavior pattern features is generated, abnormal behaviors are identified through a multi-layer anomaly detection model, a detection result with an anomaly type and confidence is output, a target protection strategy is matched from a preset library according to the result, the weight is dynamically adjusted in combination with the anomaly confidence, and a real-time protection strategy instruction is generated. And finally, executing a protection strategy instruction to synchronously regulate and control network connection, equipment permission and a user access path, and feeding back regulated and controlled network state data to optimize abnormal identification logic, thereby realizing comprehensive and intelligent remote office network security protection.

The invention relates to a network public opinion intelligent classification and emergency decision-making system based on multi-modal fusion and dynamic evolution, and belongs to the field of network public opinion monitoring and big data analysis and artificial intelligence. The system comprises a multi-source data acquisition and preprocessing module used for crawling multi-modal data, constructing a propagation path map after preprocessing, and identifying key propagation nodes; the multi-dimensional classification engine module is used for carrying out conflict intensity quantification on public opinion events and dynamically updating a rule word bank to keep the adaptability of a conflict intensity quantification model; the event graph construction and anomaly detection module is used for constructing a public opinion propagation path and public opinion event generality logic chain mode, monitoring public opinion propagation speed and giving an alarm; the stakeholder dynamic risk assessment module is used for finely classifying network public opinion participants, providing a basis for differential propagation intervention and simulating public opinion evolution to carry out risk simulation; and the intelligent decision-making and emergency response module executes different levels of emergency measures based on the risk index according to the hierarchical response strategy.

The invention discloses a self-adaptive operation and maintenance root cause positioning method and system based on deep learning. The method comprises the steps that firstly, multi-source data such as system logs, performance indexes, alarm events and configuration changes are collected, and time sequence features, text features and associated features are extracted; performing anomaly detection in combination with a 3-sigma criterion and an auto-encoder; when an exception is found, reasoning diagnosis is carried out by utilizing the fault knowledge graph, and fault diagnosis is realized by combining an XGBoost classification algorithm; and then fault propagation analysis and root cause positioning are carried out, and a fault processing flow is continuously optimized. The problems of incomplete fault features, low detection accuracy, limited diagnosis capability and the like in an existing operation and maintenance system are solved, more accurate fault detection and root cause positioning are realized through a mode of combining multi-source data fusion, deep learning detection and knowledge graph reasoning, and meanwhile, the method has adaptive optimization capability and is suitable for large-scale popularization and application. And the operation and maintenance fault processing efficiency is obviously improved.

The invention relates to the technical field of data analysis, and discloses a business data analysis method, device and equipment based on big data and a storage medium, the method comprises the following steps: collecting and preprocessing original log data to obtain standardized log data, and performing adaptive multi-modal feature extraction to obtain comprehensive feature representation; inputting a heterogeneous graph neural network to carry out anomaly detection and multi-level root cause analysis to obtain a hierarchical anomaly event; executing task scheduling and resource allocation of reinforcement learning to obtain a task allocation scheme; performing deep learning enhanced root cause reasoning and knowledge graph construction on the task allocation scheme to obtain a system state diagram and a causal relationship; and generating a multi-dimensional task data analysis report. According to the method, comprehensive analysis, anomaly detection, root cause reasoning and intelligent scheduling of log data are realized. The reliability and performance of the system can be improved, and more intuitive and accurate decision support can be provided for operation and maintenance personnel.

The invention relates to the technical field of building energy consumption analysis, and discloses a building energy consumption analysis method and system based on artificial intelligence. The method comprises the following steps: collecting building environment data to form an energy consumption basic data set; processing the data set to generate an energy consumption feature vector; constructing a prediction model to obtain an energy consumption predictor; a predictor is used for comparing actual data to identify abnormity and generate a report; formulating an optimization scheme based on the report to generate a control instruction; and executing instruction record change data to update the feature library to complete a closed loop. According to the invention, closed-loop management of accurate prediction, anomaly detection, optimization control and effect evaluation of building energy consumption is realized, so that the building energy utilization efficiency is improved, and energy waste is reduced.

An example method includes for each training iteration in a series of training iterations: receiving, for a first set of unlabeled metrics data, a set of respective labels associated with an account in a set of accounts. The first set of unlabeled metrics data and a second set of unlabeled metrics data can be processed to generate network inputs representing a respective account in the set of accounts. The network inputs can be processed using a teacher neural network that generates an anomaly prediction output for each network input. A student neural network can be trained to optimize a loss function, which can include minimizing a loss term measuring a difference between the anomaly prediction output and the student network's proposed anomaly prediction output and minimizing a loss term measuring a difference between the proposed anomaly prediction output and the account's associated label.

The invention relates to the technical field of energy management, and discloses a multi-source photovoltaic energy storage collaborative management method and system. The method comprises the steps of obtaining a multi-source photovoltaic system data generation task set; constructing a multi-dimensional data fusion model, and defining a collaborative optimization coordinate system; determining an initial cooperative constraint condition; co-scheduling simulation is executed based on the model to generate a preliminary strategy; and dynamically correcting and generating a multi-source collaborative optimization strategy by using a self-adaptive optimization algorithm. The method also relates to refining processing of each axis in the model, adoption of an improved algorithm optimization strategy, a calculation framework and a fault-tolerant mechanism in simulation, anomaly detection and re-optimization, construction of a scene library for predicting conflicts and the like. The system comprises a data acquisition and classification module, a model construction module, a constraint setting module, a simulation scheduling module and a strategy optimization module. The energy utilization efficiency of the multi-source photovoltaic energy storage system can be effectively improved, stable operation of the system is guaranteed, and equipment loss and cost are reduced.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an intelligent anomaly recognition and intervention processing method, device, equipment and medium. The method comprises the following steps: carrying out feature fusion by using a gating fusion network and generating a preliminary abnormal score, determining a reconstruction error through an auto-encoder and triggering abnormal early warning, calculating a causal effect value of key features in combination with a causal graph model and anti-factual reasoning, and calibrating the abnormal score to generate a final abnormal score and an intervention instruction. And executing an intervention action and recording a result. According to the method, the multi-dimensional feature information and the causal reasoning mechanism are fused, the self-encoder reconstruction error is combined to carry out anomaly judgment, the intervention instruction is generated and executed, closed-loop control of anomaly detection, reasoning analysis and intervention execution is achieved, and the recognition accuracy of complex events and the system response capacity are improved.

The invention discloses a numerical control tool machining monitoring method. The method comprises the following steps that firstly, data collection and primary processing are conducted; 2, constructing a digital twinborn model; 3, adjusting a self-adaptive threshold value; 4, performing prediction maintenance and fault early warning; and 5, optimizing machining parameters in real time. Multi-dimensional data such as temperature, vibration, stress and cutting force are collected in real time through a multi-sensor system, high-frequency sampling and signal denoising technologies are combined, comprehensiveness and accuracy of monitoring data are ensured, and a foundation is laid for follow-up analysis; the virtual machining model is constructed through the digital twinning technology, real-time simulation and synchronization of the machining process are achieved, the current state of the tool can be comprehensively reflected, anomaly detection and pre-judgment are conducted in combination with historical data and real-time data, and the fault monitoring accuracy is improved.

The invention provides an early warning analysis method based on intelligent vision and a server, and the method comprises the steps: obtaining a multi-mode video stream containing a visible light and infrared thermal imaging image sequence through a vision collection device in a monitoring region, and carrying out the spatial-temporal feature extraction of the multi-mode video stream; the method comprises the steps of generating dynamic behavior sequence characteristics including a target object moving track, posture change and thermodynamic distribution characteristics, analyzing the dynamic behavior sequence characteristics through a preset abnormal behavior judgment model in a multi-dimensional matching mode, judging whether the target object triggers abnormal behavior characteristics or not, and if yes, generating a graded early warning signal in combination with an association weight. In addition, according to the spatial distribution and the time length of the abnormal behavior characteristics, the parameters of the acquisition equipment and the coverage range of the monitoring area are dynamically adjusted, and the subsequent video stream anomaly detection precision is optimized, so that intelligent and accurate early warning analysis is realized, and the security efficiency is improved.

The invention discloses an abnormal short message behavior detection method and system based on multi-dimensional feature fusion, and relates to the related technical field of short message security detection.The method comprises the steps that spatial and temporal distribution features, semantic association maps and equipment behavior fingerprints of short message interaction are collected, a dynamic feature pool is configured, and a cross-modal feature sequence is extracted; cascade identification is carried out; the feature fusion weight matrix is dynamically adjusted, an abnormal probability score is generated, and when the abnormal probability score exceeds a dynamic abnormal probability threshold, a multi-stage verification mechanism is triggered; and matching a time sequence mode at an edge computing node, dynamically generating a verification code triggering threshold value, performing interactive risk verification, and determining an abnormal short message behavior mark. The technical problems of insufficient detection timeliness and adaptability and high false alarm and missing report rate caused by single detection dimension and difficulty in identifying novel complex abnormal short message behaviors in the prior art are solved, and the technical effects of reducing the false alarm and missing report rate of short message anomaly detection and improving the detection timeliness and adaptability are achieved.

The invention provides an environment monitoring data anomaly detection method, a medium and a system, and belongs to the technical field of electrical digital data process.The method comprises the steps that firstly, an environment monitoring data original matrix is constructed, time sequence segmentation is conducted, and a rule matrix and an anomaly matrix are obtained through matrix decomposition; calculating a historical mean value, a historical standard deviation, a spatial feature and a time feature of the environmental parameters, designing an anomaly detection optimization equation set based on the features, including an environmental parameter weight equation, an anomaly threshold equation and a comprehensive anomaly degree equation, and calculating an anomaly detection result by calculating an environmental parameter weight vector, a dynamic anomaly threshold matrix and a comprehensive anomaly degree index. Finally, dynamic optimization of anomaly detection is achieved through the gain vector and the feedback vector, anomaly detection of real-time environment monitoring data is completed, and the technical problem that in the prior art, an environment monitoring data anomaly detection method is difficult to give consideration to dynamic relevance of multi-dimensional environment parameters and the accuracy of anomaly detection at the same time is solved.