6687 results about "Risk level" patented technology

The invention discloses a method, a system and a device for monitoring multifunctional parameters of a direct-current drilling machine, and relates to the technical field of manufacturing of industrial automatic control system devices. The method, system and device for monitoring the multifunctional parameters of the direct-current drilling machine comprises the steps that S1, various data are collected and subjected to standardization and normalization processing, and a standardized working condition data set is constructed; s2, multi-dimensional disturbance characteristics are analyzed, and the stability level of a drilling system is quantified; s3, evaluating a dynamic evolution trend of a working condition, and updating a risk level, a response strategy and a monitoring priority; and S4, identifying an abnormal state based on the key disturbance value and the trend evolution value, and generating a monitoring report. The problems that an existing direct current drilling machine display device is insufficient in key working condition feature extraction capacity, deep understanding and trend analysis of the equipment operation state are difficult to support, and then the early warning timeliness and judgment accuracy of the abnormal state are limited are solved.

The invention discloses a wind power plant booster station multi-source data fusion anti-misoperation locking intelligent decision-making and early warning method, and relates to the technical field of intelligent misoperation prevention of a power system, and the method comprises the following steps: collecting multi-source heterogeneous data, obtaining the data through a distributed sensor network, and carrying out the edge calculation preprocessing; performing data space-time alignment and fusion, performing equipment state evaluation, and constructing a deep belief network and Bayesian network hybrid model to calculate a health index; anti-misoperation rule modeling is carried out, and operation logic verification is carried out based on a Petri network and an expert knowledge base; risk early warning decision making: fusing multi-source early warning information to divide risk levels; intelligent locking control is carried out, and a locking strategy is optimized through reinforcement learning; and performing decision support and visualization, constructing a three-dimensional digital twinborn model, and displaying operation guidance and risk early warning in combination with an AR technology. Through multi-source data fusion and intelligent decision making, the anti-misoperation locking accuracy and efficiency are improved, and the safety and the operation and maintenance level of the booster station are remarkably enhanced by equipment fault early warning three months ahead of time.

The invention discloses a geological disaster networking monitoring and early warning method, and relates to the technical field of geological disaster monitoring and early warning, and the method comprises the following steps: S1, collecting the original data of multiple types of monitoring equipment in a monitoring region, extracting a high-amplitude sudden change region and a frequency drift factor according to the time and frequency distribution, constructing a high-frequency disturbance sensing matrix, and carrying out the recognition of the high-frequency disturbance sensing matrix; and generating a disturbance characteristic index map for representing the spatial distribution of the unnatural disturbance source. According to the method, active identification and modeling of non-natural interference are realized by constructing a high-frequency disturbance perception matrix and a disturbance index map, disturbance propagation analysis and residual difference are combined to strengthen precursor signal features, the risk level is accurately judged through trend identification and causal analysis, and finally, early warning model parameters are dynamically optimized based on response regulation factors, so that the early warning accuracy is improved. A closed-loop mechanism of interference identification, signal purification, trend extraction, risk judgment and strategy adjustment is formed, and the early warning stability, accuracy and practicability of the system in a high-interference environment are remarkably improved.

The invention relates to the field of fault early warning, in particular to a wind power fault dynamic early warning method and system based on multi-source heterogeneous data fusion. According to the method, multi-source data such as SCADA operation data, CMS vibration monitoring data and meteorological environment data of a wind turbine generator are collected in real time, standardization processing is carried out, and a multi-dimensional feature vector is constructed. And generating a fusion data set by using an adaptive weighted fusion algorithm, constructing a fault prediction model based on a deep convolutional neural network, and outputting a health state assessment value and a fault risk level in real time after historical fault sample supervised training. And when the risk level exceeds a threshold value, generating an early warning signal containing a fault type and a positioning and repairing suggestion, dynamically adjusting a monitoring parameter weight, iteratively updating a model, and realizing adaptive optimization of an early warning strategy. The problem that an existing method depends on single data source and multi-source data fusion is solved, and accurate dynamic early warning is achieved.

The invention belongs to the technical field of intelligent machining path control, and discloses an intelligent numerical control machine tool automatic programming path optimization method based on workpiece modeling, which comprises the following steps: acquiring a CAD model, machine tool sensor data, tool wear data and historical machining logs, generating a workpiece characteristic parameter set, and fusing a three-level compensation mechanism to generate a dynamic error parameter set; then, dividing a preliminary risk level of the processing area, and performing secondary risk assessment to generate a comprehensive risk level; extracting a risk level conflict area, and determining a final risk level; constructing a static / dynamic cost matrix to obtain a path priority map; thirdly, generating an initial path, smoothing an optimized path trajectory, and performing multi-objective optimization to generate an optimized path planning table; cutting parameters are adjusted in real time, the path feasibility is verified, and a real-time control instruction set is generated; and finally, constructing a quality-process correlation model, generating a global strategy packet, forming closed-loop iteration, and completing system self-evolution.

The invention relates to a risk management and control method and system based on real-time behavior analysis, and the method comprises the steps: carrying out the structural processing of multi-source behavior data through lightweight protocol decoding and behavior label embedding, and constructing an original behavior data set of a user and an entity; extracting multi-dimensional behavior characteristics by using a sliding window analysis and sparse representation mechanism, and constructing a user behavior graph by combining graph embedding learning; constructing a time-sensitive behavior trend model through streaming modeling and an incremental learning strategy, identifying an abnormal evolution trajectory in real time, and introducing a dynamic risk threshold regulation and control mechanism; adopting a high-throughput flow data processing and fast similarity matching algorithm to construct a fusion discrimination model, giving risk levels to abnormal behaviors and classifying the abnormal behaviors; and finally, performing closed-loop optimization in combination with a historical treatment effect. The system has the advantages of high real-time performance, high calculation efficiency, adaptability to complex network environments and the like, and the network security protection capability can be effectively improved.

The invention discloses a distribution cable branch box state monitoring method based on state recognition, and particularly relates to the technical field of power monitoring, and the method comprises the steps: collecting multi-dimensional operation data of a plurality of branch boxes in a continuous time period, and constructing a state evolution sequence; calculating a state offset score and an adjacent equipment state consistency score to judge whether an evaluation process is triggered or not; after triggering, constructing a state influence probability map, identifying an abnormal influence path, extracting a state disturbance diffusion index and a cooperative behavior deviation index, inputting into a pre-trained risk identification model, generating a risk level interval and a cause probability distribution vector, executing an influence regulation and control measure, and updating a state identification logic; according to the method, dynamic perception of the state of the branch box is realized by constructing a state evolution sequence, combined judgment of individual and group behaviors is realized by combining a state offset score and an adjacent equipment state consistency score, and a risk level interval and cause probability distribution vector are used for driving regulation and control strategies and map updating. And the monitoring precision and the self-adaptive capability of the system are improved.

The invention discloses a robot real-time potential safety hazard recognition system based on multi-modal sensor fusion, and particularly relates to the technical field of intelligent inspection and safety monitoring, the system comprises five parts of data acquisition, information fusion, behavior response, trajectory analysis and risk output, and the potential safety hazard recognition system is used for recognizing potential safety hazards through image acquisition, thermal imaging, gas concentration and temperature and humidity information. Carrying out numerical value normalization and feature extraction, identifying potential abnormity and generating early warning; triggering data enhanced acquisition and track recording in the target area, and constructing a space-time path model to analyze an abnormal evolution trend; and finally, outputting a potential safety hazard assessment result according to a risk level classification rule by combining the enhanced information and the trajectory features. According to the method, high-precision early warning is realized through multi-source data acquisition and normalization fusion, the local recognition capability is improved based on dynamic enhanced acquisition of a behavior response mechanism, an abnormal development trend is tracked by combining track evolution modeling, risk level assessment is output according to the abnormal development trend, and accurate recognition and dynamic management and control of hidden dangers are realized.

The invention relates to a high-altitude large steel structure corridor safety risk monitoring method and system and a medium, and belongs to the technical field of civil engineering structure health monitoring, and the monitoring method comprises the steps: collecting original monitoring data through a multi-source sensor group disposed at each node of a steel structure corridor; performing space-time alignment processing on the original monitoring data to generate a sensor data matrix; performing dynamic noise suppression on the sensor data matrix based on an environmental noise transfer function model, and outputting a pure signal matrix and a damage characteristic frequency band identifier; calculating a thermal stress sensitive factor, extracting a multi-physical field coupling feature associated with the damage feature frequency band identifier in the pure signal matrix, fusing to generate a high-dimensional damage feature tensor, inputting the high-dimensional damage feature tensor into a pre-constructed digital twinborn risk assessment model, and outputting an assessment result including a risk level label and a damage position coordinate; and matching the regulation and control strategy according to the risk level label, and generating a corresponding equipment control instruction. According to the invention, the scientificity and accuracy of risk decision can be improved.

The invention relates to the technical field of network security, in particular to a network security analysis method and system based on big data. Comprising the following steps: collecting related multi-source heterogeneous data of a network, and carrying out standardized processing such as cleaning and de-noising; network analysis is carried out based on the preprocessed data, network traffic is analyzed in real time by using machine learning and deep learning algorithms, and abnormal conditions are detected; constructing a risk prediction model according to a network analysis result and related information, and predicting a future network security risk level; if the risk level exceeds the threshold value, determining a security event source and a responsibility subject through data tracing; and finally, generating a safety response strategy according to risk prediction and data traceability results, and performing disposal. The corresponding system covers the modules of data acquisition, preprocessing, network analysis, risk prediction, data tracing, security response and disposal and the like, and all the modules work cooperatively to form a complete network security analysis and guarantee system, so that the stable operation of the network system is guaranteed.

The invention relates to the technical field of financial data security, and discloses an intelligent ERP financial system data security management and authentication method. The method comprises the following steps: acquiring an original transaction data stream in an ERP system, extracting key financial fields, and dividing the key financial fields into a sensitive data set and a common data set according to a preset rule; a dynamic encryption strategy framework is constructed based on sensitive data set attributes, the framework comprises multiple levels of encryption strength parameters, and the corresponding encryption strength can be automatically matched according to the authentication level of an access request. And monitoring a system data access behavior in real time, collecting feature data, inputting the feature data into the anomaly detection model, and triggering access blocking when an output anomaly access probability exceeds a threshold value. And generating a periodic integrity verification instruction according to the sensitive data updating frequency, performing integrity verification by using a hash chain technology, recording a result and marking a tampering risk level. And based on the association relationship between the tampering risk level and the abnormal access probability, generating an updated security policy and synchronizing the updated security policy to each data access node.

The invention discloses a slope deformation monitoring and dynamic early warning method and system based on multi-sensor data, and relates to the technical field of slope monitoring, and the method comprises the steps: collecting multi-source sensor data by using pre-deployed multi-class sensors, constructing graph structure data according to the sensor distribution and the pre-processed multi-source sensor data, and carrying out the graph structure data; a graph convolutional network is used for modeling, and a slope deformation monitoring model is constructed; introducing a clustering federation learning strategy to carry out joint training on the slope deformation monitoring models of the plurality of sites, and carrying out risk grade division by using the trained slope deformation monitoring models; key influence factors of landslide disasters are extracted, an improved firefly algorithm is introduced to dynamically optimize an early warning threshold value, the optimized early warning threshold value and the current risk level are used for judgment, and early warning information is generated. According to the invention, the reliability of monitoring and the timeliness of early warning are improved through multi-source data fusion and intelligent analysis, and the crossing of slope deformation monitoring from single-point static state to networked intelligence is realized.

The invention provides a deep learning-based avalanche early warning model construction method and system, and the method comprises the steps: firstly obtaining multi-source environment monitoring data, including meteorological time sequence, topographic space and accumulated snow layer physical data, of a target region, carrying out the time dimension alignment of the meteorological time sequence data to generate a feature sequence, carrying out the meshing of the topographic space data to generate a feature set, and carrying out the construction of an avalanche early warning model; the method comprises the following steps: extracting parameters from accumulated snow layer physical data to generate a state vector, inputting a deep learning network model containing time sequence attention, spatial convolution and cross-modal interaction units, generating a fusion feature vector, constructing a training set based on historical avalanche event annotation data, performing dynamic weight optimization on the fusion feature vector, and generating an avalanche risk prediction model. And finally, receiving current monitoring data in real time, outputting a risk level and an early warning trigger threshold value by the avalanche risk prediction model, and generating a multi-level early warning signal when a real-time risk value exceeds the threshold value, thereby realizing accurate avalanche early warning.

The invention relates to a power construction monitoring method, system and device and a medium, and the method comprises the steps: carrying out the space-time calibration and anti-interference processing through collected construction images, temperature and dust data, and carrying out the fusion to generate a multi-source fusion data set containing environment information; performing feature extraction on the multi-source fusion data set based on a neural network model, synchronously identifying an action track of a constructor and equipment operation parameters, and constructing a feature map set associated with behaviors and equipment states; performing action and equipment anomaly conjoint analysis on the feature map set, and generating a construction risk event set by analyzing human body joints and temperature feature detection; performing spatial distance calculation and time monitoring on the risk event, and quantifying the coupling risk level; and based on the dynamic risk threshold, a grading early warning instruction is triggered, and precise response of field personnel warning, equipment power failure and evacuation guidance is realized. According to the method, the technical problems of low construction risk identification precision, equipment anomaly detection lagging and risk assessment deficiency in a complex environment are solved.

The invention provides a road bridge crack detection method and system, and the method comprises the steps: collecting a bridge surface multi-view image, and constructing a training data set containing crack feature labeling through quality screening and standardized labeling; preprocessing the image by using a multi-scale feature fused deep convolutional neural network and carrying out semantic segmentation, initially identifying a suspected crack region and generating a segmentation mask; and constructing a BeNNS proxy model based on the mask, and establishing a mapping relationship between the detection result and the bridge structure topology, the stress flow field and the service function chain so as to evaluate the result reliability. And inputting an evaluation result into a hybrid evaluation mechanism, performing online real-time detection and offline batch verification to optimize precision, and outputting a verified crack region. Finally, morphological analysis is conducted on the area, geometric parameters and danger levels of cracks are extracted and integrated to a bridge health monitoring system, a crack evolution tracking algorithm and an early warning mechanism are established, and dynamic tracking early warning is achieved. The problem of low detection precision in a complex environment can be solved.

The invention discloses a fault early warning method and system based on an AI large model, and the method comprises the steps: obtaining multi-source heterogeneous data, and carrying out the denoising and standardization processing, and obtaining fusion data; inputting into a feature extraction model, and outputting a feature vector set; identifying the dynamic operation mode based on a K-means algorithm to obtain an operation mode baseline; inputting a feature sequence model, and outputting a precursor feature sequence; calculating an abnormal score according to the precursor feature sequence, marking as abnormal if the score is greater than or equal to a threshold value, otherwise, marking as normal, and obtaining an abnormal detection result; evaluating a risk level according to a detection result; inputting the risk level into a fault analysis model to obtain fault cause distribution; determining optimized operation mode parameters according to the fault cause distribution; and performing deviation analysis on the data and the optimized parameters, and if a deviation value is greater than a threshold value, triggering an early warning signal. The method can solve the problem of insufficient recognition capability in a scene with variable fault types.

The invention relates to the technical field of anomaly detection, and discloses a context-aware-driven multi-dimensional anomaly detection early warning method. The method comprises the following steps: collecting real-time context data in a target monitoring scene, and generating an initial feature set containing an environment parameter sequence and a behavior pattern map; a first detection model and a second detection model matched with the scene type are constructed according to the scene types, the first model comprises a dynamic correlation function of environment indexes and abnormal probabilities, and the second model comprises a nonlinear mapping rule of behavior characteristics and risk levels; and based on the real-time context deviation degree and the characteristic fluctuation coefficient, a target model is triggered to generate a dynamic early warning instruction, and the dynamic early warning instruction is pushed to an execution module to adjust a trigger threshold of an abnormal response strategy or a priority of a risk disposal process. According to the method, multi-dimensional data is combined, the adaptability and accuracy of anomaly detection are improved through dynamic model triggering and response strategy adjustment, and the method is suitable for various monitoring scenes.

The invention discloses an expressway emergency management and control method and system, and the method comprises the steps: building a multi-dimensional heterogeneous data fusion model through a time-space diagram neural network according to the traffic flow, meteorological data and accident reports collected by roadside sensing equipment, and carrying out the dynamic prediction of the risk level of a road segment, and obtaining a real-time risk level map of a whole road network; based on the real-time risk level map, outputting a dynamic shunting scheme containing variable lane marks and a speed limiting strategy; according to the dynamic shunting scheme, generating a vehicle trajectory optimization strategy; and outputting an anti-interference optimization control strategy based on a vehicle trajectory optimization strategy in combination with sudden obstacle data polled by the unmanned aerial vehicle in real time. According to the embodiment of the invention, rapid and accurate path adjustment and cooperative control can be carried out, and the emergency management level of the expressway is improved.

The invention specifically relates to the technical field of big data analysis, and discloses a water conservancy gate multi-parameter cooperative intelligent monitoring system, which comprises a multi-parameter acquisition module, a multi-parameter processing module, a comprehensive analysis module, an intelligent decision module, an operation and maintenance early warning module and a man-machine interaction module, the multi-parameter processing module is used for calculating flood control and discharge indexes, structure safety indexes and equipment health indexes; the comprehensive analysis module is used for judging gate risk levels; the intelligent decision-making module is used for generating an optimal gate scheduling scheme; the operation and maintenance early warning module is used for constructing a multi-level early warning mechanism; according to the method, parameter coverage is comprehensive, a gate digital twinborn model and a gate opening comprehensive evaluation model are constructed, the gate risk level is evaluated, an optimization strategy is dynamically adjusted through an intelligent decision module, the accuracy of gate risk judgment is improved, and the self-adaptive capacity of the system is improved.

The invention provides a flood peak evolution path and dam break risk early warning method and system. According to the method, a time-space coupling water conservancy data set is constructed by fusing multi-dimensional water conservancy monitoring data and regional rainfall prediction information, and a basin topology perception model is established. And further performing mode matching on the parameters and a historical dam break event characteristic spectrum, and generating a dynamic response strategy set including a flood storage and detention area capacity allocation scheme by correcting a space weight of a matching result. And finally, based on a watershed topology perception model, matching the strategy set with historical dam break features, realizing accurate identification of a flood peak evolution path and graded early warning of dam break risks, outputting a water conservancy analysis report containing risk grades, and realizing closed-loop management from data fusion and dynamic prediction to risk decision. According to the technical scheme provided by the invention, the efficiency and accuracy of intelligent analysis of the water conservancy data can be improved.

The invention relates to the technical field of monitoring and early warning, in particular to a fire hydrant monitoring intelligent early warning system based on an anomaly analysis technology, which comprises a flow velocity anomaly identification module, a node collaborative pressure difference detection module, a pressure difference trend independence judgment module, a node degradation feature extraction module and a risk level generation module. According to the method, through correlation judgment of flow velocity deviation and control signals, no-signal recognition of abnormal water taking behaviors and a cooperative analysis mechanism of pressure change of adjacent nodes, a hydraulic disturbance area under non-manual control can be accurately recognized, and through analysis of spatial independence of a pressure response trend, a water flow disturbance area under non-manual control can be accurately recognized. The function degradation level of the device is extracted according to historical data of on-off time delay and response performance, the quantitative evaluation capability of the node function state is enhanced, the capability of finely dividing the risk level is achieved when risk early warning is given out, the risk identification accuracy is improved, and the active discovery capability of early fault hidden dangers is enhanced.

The invention discloses a health monitoring method and system for an intelligent building, and belongs to the field of building construction health monitoring, and the method comprises the steps: obtaining internal and external multi-dimensional data of the intelligent building in real time through a plurality of sensors, the multi-dimensional data comprising environment data, structure data of the intelligent building, equipment state data and personnel activity data; preprocessing the multi-dimensional data to obtain preprocessed multi-dimensional data; according to the preprocessed multi-dimensional data, a health condition analysis result of the intelligent building is output by utilizing a pre-trained health monitoring model, and the health condition analysis result comprises the suitability degree of environmental conditions, the stability degree of a building structure, the equipment health degree and the risk degree of personnel activities; when the health condition analysis result indicates that the intelligent building is abnormal in health, an early warning signal is sent out; and determining a repair scheme of the intelligent building according to the type of the health abnormality. According to the method, the safety, comfort and management efficiency of the building can be improved.

The invention relates to a coal mine safety risk intelligent management and control method, device and equipment and a medium. The method comprises the following steps: constructing a multi-source heterogeneous coal mine safety data system according to received data of a target coal mine park; the multi-source heterogeneous coal mine safety data system comprises static structure data, dynamic environment data, personnel behavior data and management data; constructing a coal mine three-dimensional space model according to the static structure data and the dynamic environment data; risk indexes in the dynamic environment data are extracted based on multi-algorithm fusion for evaluation, and a risk level is obtained; and if the risk level reaches a preset threshold value, triggering a corresponding linkage response mechanism, and forming a visual result in the coal mine three-dimensional space model. By the adoption of the method, closed-loop logic from sensing, evaluation to linkage treatment can be achieved, and the real-time performance, predictability and controllability of coal mine safety management are effectively improved through algorithm support of each stage and fine design of implementation details.

The invention discloses a data security monitoring method based on risk early warning, particularly relates to the field of data security, and comprises the steps of multi-source data acquisition, index system calculation, comprehensive threat scoring, dynamic risk judgment and response strategy execution. According to the method, a three-dimensional monitoring system is constructed through a multi-source heterogeneous data fusion analysis framework, a traditional single-dimensional monitoring blind area is eliminated, a quantitative score is generated by combining a three-layer nonlinear evaluation model with double-track baseline analysis and dynamic aggregation of basic parameters, double verification of historical rules and real-time fluctuation is achieved, the threat judgment accuracy is remarkably improved, and the threat judgment efficiency is improved. An intelligent mapping system of risk levels and disposal strategies is established, differential response plans are matched through three-level risk division, and a closed-loop feedback channel is synchronously constructed, so that high-risk events are quickly isolated for evidence obtaining, low-risk abnormities are accurately controlled, and a complete iterative loop of assessment disposal optimization is formed. And the active adaptive capacity and the response timeliness of the security defense system are enhanced.

The invention relates to the technical field of agricultural meteorological prediction, in particular to an agricultural meteorological disaster time sequence prediction system and method based on multi-modal data fusion, and the method comprises the steps: collecting and preprocessing agricultural meteorological disaster related data, constructing a dynamic semantic association graph, carrying out the multi-layer feature abstraction processing, and generating a semantic enhancement feature vector; the dual-branch prediction network processes time sequence dependence and local mode features, multi-granularity attention processing identifies key feature information, multi-scale feature fusion extracts different time scale feature information, and cascade fusion is carried out; the multi-target optimization module carries out model training based on the comprehensive feature representation and optimizes a plurality of targets; the multi-time scale prediction output module generates short-term accurate prediction, medium-term trend prediction and long-term risk assessment results, and provides prediction confidence, error range and risk level information; a dynamic semantic association graph and a multi-layer feature mapping mechanism are constructed, and deep fusion of multi-modal data on the semantic level is achieved.

The invention, which relates to the technical field of disaster monitoring and early warning, discloses a landslide and debris flow disaster monitoring and early warning system comprising a multi-source data acquisition module, an intelligent processing module, an analysis decision module and an early warning response module. The space-time reference unified framework is constructed, the high-precision time synchronization and space projection conversion technology is adopted, the space-time matching problem of the satellite-ground heterogeneous data in the prior art is solved, the multi-source parameter fusion proportion is adaptively adjusted according to indexes such as signal quality and environment interference degree through a dynamic weight distribution mechanism based on data reliability, and the space-time matching precision of the satellite-ground heterogeneous data is improved. In addition, a multi-model collaborative decision-making framework is established, the advantages of three models of mechanism driving, probability statistics and intelligent learning are fused, a comprehensive research and judgment result is generated through a confidence quantitative evaluation and conflict resolution algorithm, an early warning response mechanism is matched, a pre-arranged plan library is automatically matched according to a risk level, and an external control system is linked. And a closed-loop management chain of monitoring early warning, analysis decision and emergency disposal is formed.

The invention provides a charger fault diagnosis method based on a knowledge graph, which realizes efficient and self-adaptive fault diagnosis and maintenance through multi-technology fusion, constructs a structured knowledge base, integrates charger parts, fault modes and detection methods, stores entities and causal relationships and detection association by adopting a graph database, extracts information through a BERT model, and realizes fault diagnosis and maintenance based on the knowledge graph. Marking causal strength and dynamically adjusting the causal strength, converting spoken description of a user into structured data, matching a fault path in a knowledge graph, dynamically adjusting a weight, analyzing time sequence data, matching a time sequence mode in the knowledge graph in combination with dynamic time warping, generating a candidate path, optimizing a diagnosis path weight, and designing a multi-target reward function; a visual report is generated, the fault probability in the future seven days is predicted, maintenance suggestions are generated in combination with risk levels, knowledge maps and models are continuously optimized through user feedback, diagnosis accuracy and maintenance efficiency are improved, and manual intervention requirements are reduced.

The invention relates to the technical field of power Internet of Things fault diagnosis, and discloses a power Internet of Things abnormal behavior diagnosis system based on large model dynamic optimization. The system comprises a data acquisition module, a feature extraction module, an anomaly detection module, a dynamic optimization module and an early warning response module. The data acquisition module acquires operating parameters of power equipment in an area; the feature extraction module extracts state feature vectors through operation parameters, obtains a deviation coefficient in combination with an anomaly analysis area and the like, fuses risk assessment values to generate an anomaly index, and judges whether deep diagnosis is started or not according to the anomaly index; the anomaly detection module utilizes an attention mechanism model to mine depth features and generate a report, and judges whether to trigger early warning or not in combination with real-time adjustment parameters; the dynamic optimization module guarantees data interaction through an edge computing node, and a standby node is started when a main link is abnormal; and the early warning response module matches an emergency scheme according to the risk level and issues an instruction. According to the system, accurate diagnosis and efficient response of abnormal behaviors of the power Internet of Things can be realized.

The invention discloses an information security adaptive protection method and system based on artificial intelligence, and relates to the field of security protection, and the method comprises the steps: dynamically collecting multi-dimensional asset data through distributed nodes, carrying out the edge calculation preprocessing, and extracting features through a deep learning model; carrying out threat identification by fusing LSTM time sequence analysis, an isolated forest and a multi-modal AI detection engine of a knowledge graph; outputting a risk level based on an improved analytic hierarchy process and a fuzzy evaluation model; the AI strategy engine combines the risk level and the business scene to generate an optimal protection strategy, and continuous optimization is carried out through reinforcement learning; a standardized instruction is linked with safety equipment to execute protection, and interception effect closed-loop optimization is fed back in real time; a whole process log is stored through a block chain, and an attack evidence chain is generated through an AI traceability model. The method has the advantages that the information security protection capability is comprehensively improved through hierarchical data acquisition, multi-modal threat detection, scientific situation evaluation, dynamic generation of an optimization protection strategy and combination of block chain evidence storage and AI traceability.

The invention provides an intelligent customer risk assessment system and method based on a large language model, and relates to the technical field of risk assessment, and the method comprises the steps: obtaining multi-modal data of a customer, carrying out the preprocessing, and extracting structured and unstructured features; constructing a hierarchical risk knowledge system, and realizing adaptive evolution of the knowledge system through a graph neural network and a generative model; constructing an initial negative sample library, and constructing a negative sample database in combination with a non-risk mode labeled by an expert and derivative layer analysis; optimizing the large language model by adopting a strong supervision, weak supervision and reinforcement learning cooperative training mechanism under each classification according to the customer type; mining risk features in a text by using the optimized large language model, processing multi-modal data through a multi-level attention network, and generating a positioning report including contradiction type coding, service influence dimension evaluation and risk level quantification; the accuracy, efficiency and flexibility of customer risk assessment are improved, and the risk management strategy is optimized.