9713 results about "False alarm" patented technology

The invention discloses a battery pack thermal runaway risk identification and early warning system and method, and relates to the related field of battery risk early warning, and the method comprises the steps: collecting an operation parameter data set of a battery pack in real time through a multi-mode sensor network, carrying out the hybrid anomaly detection of the battery pack, obtaining a real-time anomaly detection result, and carrying out the thermal state prediction of the battery pack, and constructing a thermal state evolution trend chart to perform thermal runaway risk assessment, determining a thermal runaway risk level, triggering a multi-level early warning signal according to the thermal runaway risk level, and generating a thermal runaway suppression strategy. The technical problems that in existing battery thermal runaway early warning, due to monitoring limitation of a single sensor, the false alarm rate is high, response lags behind, and battery aging cannot be dynamically adapted are solved, and the technical effects that the thermal runaway early symptom detection rate is increased through multi-mode sensing, and the early warning time is shortened are achieved.

The invention provides an operation and maintenance alarm processing method and system based on a knowledge graph enhanced large model, and relates to the technical field of knowledge graphs, and the method comprises the steps: collecting multi-source operation and maintenance alarm historical data, carrying out the adaptive hierarchical structured processing of the data, and constructing a space-time semantic multi-dimensional feature matrix; constructing a multi-dimensional alarm association network based on a dynamic weight fusion algorithm and forming a hierarchical knowledge graph; constructing a root cause inference rule base in combination with time sequence evolution, topology propagation and business dependence characteristics of the alarm; and when a real-time alarm is received, a diagnostic reasoning system is started to analyze a propagation link, a root cause is identified, and a processing suggestion is generated. The method can accurately identify the alarm root cause, improves the operation and maintenance efficiency, and reduces the false alarm rate.

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 relates to the technical field of camera linkage alarm, and discloses a camera linkage alarm method and system for intelligent environment monitoring, and the method comprises the steps: synchronously collecting the video stream data of a plurality of cameras and the multi-parameter monitoring data of an environment sensor; executing multi-path feature analysis and cross-modal feature fusion to obtain environment-vision joint features; environment scene self-adaptive classification is executed, and environment-vision joint features are analyzed through an anomaly judgment model to obtain an abnormal event judgment result; executing multi-camera collaborative verification and vision-environment data association analysis to obtain multi-dimensional abnormal event description information; and selecting a multi-camera linkage response strategy and coordinating the plurality of cameras to carry out three-dimensional monitoring on the abnormal area to obtain a linkage response execution result, so that a false alarm mode can be identified and an abnormal judgment rule can be dynamically adjusted, continuous optimization of the performance of the plurality of cameras is realized, and the false alarm rate and the missing report rate of the plurality of cameras in long-term operation are reduced.

The invention relates to the technical field of computer programming languages, and particularly discloses a coal-fired power plant safety monitoring system and method. The system comprises a multi-modal data fusion platform, a federal learning agent network and a digital twinborn simulation engine. The edge computing node carries out unified acquisition and feature extraction on multi-source heterogeneous data through multi-protocol conversion, quantum noise suppression and a preprocessing chipset; the multi-modal data fusion platform realizes semantic mapping based on a knowledge graph, and fuses time-space correlation characteristics of data such as an infrared image and gas concentration by adopting a time-space encoder and a cross-modal attention mechanism. According to the method, the problems of early warning delay and high false alarm rate caused by low multi-source data decentralized processing efficiency and insufficient nonlinear correlation analysis in a traditional scheme are solved, efficient data fusion, complex risk accurate prediction and automatic safety response are realized, and the real-time performance and reliability of a coal-fired power plant monitoring system are remarkably improved.

The invention relates to the technical field of wind turbine generator fault monitoring. The invention provides a wind turbine generator fault monitoring method and system. The method comprises the following steps: synchronously acquiring gearbox and environment temperature and humidity data, and generating a time-frequency energy fusion matrix through adaptive wavelet packet transformation; adopting mutual information entropy weighted improved variational mode decomposition to screen out an intrinsic mode component set related to a fault mode; constructing a space-time double-flow residual network based on the intrinsic mode component set, and fusing two branch outputs of the space-time double-flow residual network through a dynamic feature gating mechanism to obtain a multi-dimensional feature vector; and inputting a multi-dimensional feature vector obtained by fusion into a lightweight fault classifier, and outputting a real-time fault probability and a component health degree evaluation index based on a sliding window mechanism. The problems of low efficiency, high false alarm rate, missing detection of early faults, reduction of prediction precision, incapability of mining multivariable coupling relations, need of massive annotation data, and high delay caused by insufficient edge side computing power existing in an existing wind turbine generator fault monitoring mode are solved.

The invention relates to an intelligent management system, in particular to a building construction quality safety risk management system which comprises a data acquisition module, a convolution analysis module, a risk assessment module, an early warning decision module and a feedback optimization module. The data acquisition module generates a sensing signal. And the convolution analysis module receives and adopts a multi-scale feature pyramid and a cross-modal attention mechanism to process the sensing signal to form a four-dimensional fusion signal. And the risk assessment module carries out comparative analysis on the four-dimensional fusion signal under a digital twinborn framework and forms a five-dimensional risk early warning signal. The early warning decision module receives the risk early warning signal and forms a multi-level risk early warning level signal. And the feedback optimization module receives the risk early warning level signal and optimizes the building construction quality safety risk management system through an incremental learning technology. The building construction quality safety risk management system can solve the problems that the dynamic characteristic adaptability is low in the construction process, and misinformation or missing information is likely to be generated.

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 discloses a data driving-based power battery safety risk assessment method and system, particularly relates to the technical field of battery safety monitoring, and is used for solving the problems of false alarm and missing detection caused by confusion of electrochemical relaxation effect and real fault signal characteristics in the existing standing stage fault detection method. Through multi-source data acquisition based on voltage, temperature and state of charge in a standing stage, a current slope extreme point is identified through charge and discharge mode weighted correction, a time-delay mapping relation with a voltage curve geometrical morphology abrupt change point is established, abnormal inflection points are screened, and fractal dimension attenuation characteristics are quantified; according to the method, abnormal distribution density and material damage indexes are fused to generate a dynamic threshold value, composite criterion analysis is carried out by combining frequency domain energy offset characteristics extracted by time-frequency transformation and heat conduction time-lag phase difference, relaxation interference signals are effectively stripped, hidden danger characteristics are locked, full-dimension safety state evaluation from microdefect evolution to macroscopic parameter abnormity is realized, and the safety state evaluation accuracy is improved. And the reliability of power battery fault detection under complex working conditions is improved.

The invention discloses a household Internet of Things integrated fire alarm system and a linkage control method thereof, and relates to the technical field of smart home security. A first sensing recognition module, a second deep recognition module, a linkage control module, a cloud management platform module and a system self-checking module are integrated; the problems that in an existing fire alarm system, the false alarm rate is high, linkage equipment is prone to false triggering, linkage control logic is complex, and errors are prone to occurring are effectively solved. According to the invention, multi-modal data fusion and a deep learning algorithm are adopted to carry out fire identification, a dynamic threshold adjustment algorithm is combined to optimize the sensitivity of a sensor, and intelligent linkage configuration and an anomaly detection function are provided through a cloud management platform; the accuracy of fire identification and the stability of the system are greatly improved, the credibility of a user to the system and the comprehensiveness of family safety protection are remarkably improved, and resource waste and unnecessary panic caused by false alarm are effectively reduced.

The invention discloses a multi-modal-based power equipment health state monitoring method, and relates to the technical field of power equipment detection.The power equipment monitoring method is based on multi-modal data and knowledge graph fusion, cross check, expert rule cleaning and label correction are implemented by collecting sensing data such as chromatography, temperature, current and vibration in oil, and the detection result is obtained. Outputting high-credibility data; a graph model is constructed based on equipment topology by adopting self-encoder dimension reduction fusion, early anomaly detection is realized by utilizing a graph neural network, and an anomaly alarm is generated; mechanism matching and consistency evaluation are carried out based on the fault mechanism knowledge graph, and interpretable diagnosis is output; and when the diagnosis result is significantly deviated from the actual operation and maintenance conclusion, triggering an online increment and transfer learning updating model and expanding the knowledge graph to form a closed-loop self-learning mechanism. According to the method, the fault detection accuracy is remarkably improved, false alarms and missing alarms are reduced, the operation and maintenance decision-making efficiency is improved, and meanwhile operation and maintenance intelligence and real-time alarm are enhanced.

The invention relates to the technical field of fan blade fatigue damage prediction. The invention provides a fan blade fatigue damage prediction method and system. The method comprises the following steps: constructing a coupling finite element model based on blade anisotropy parameters; blade surface three-dimensional strain field data, blade vibration acceleration signals, environment temperature and humidity and wind speed and direction data are obtained in real time, and a multi-dimensional monitoring data set is constructed; based on the multi-dimensional monitoring data set, nonlinear coupling features of all the load components are extracted, a multi-dimensional feature tensor is obtained, and a reference stress field matched with the current working condition is generated; inputting the multi-dimensional feature tensor and the reference stress field into a bidirectional long-short-term memory network, and establishing a data-physics combined driven damage evolution model; and positioning a damage area based on a damage probability distribution diagram output by the damage evolution model. The problems that in the prior art, prediction errors are obvious, sensitivity to early damage is insufficient, the false alarm rate is high, and accurate positioning of the damage position and quantitative prediction of the residual life are difficult to achieve are solved.

The invention provides an intelligent electric energy meter fault prediction method based on multi-modal sensor fusion. The method comprises the following steps: collecting original data of a multi-modal sensor and preprocessing the original data to obtain a standardized data stream; establishing a sensor health state model and monitoring the working state of the sensor; performing feature extraction and pattern recognition on the standardized data to generate an electric energy meter state feature vector; combining a sensor health assessment result and an electric energy meter state feature vector, and applying a double-layer causal reasoning framework to realize separation and identification of a sensor fault and an electric energy meter fault; multi-source uncertainty is quantified, diagnosis reliability is evaluated, and a fault evaluation report is generated. The method can effectively distinguish the sensor fault from the electric energy meter fault, improves the fault prediction accuracy, reduces the false alarm rate, and optimizes the maintenance decision.

The present invention discloses a method and system for signal detection and remote early warning of wireless base stations, comprising the following steps: collecting environmental factors and signal quality data of multiple base stations in real time through a dynamic environment perception system, combining historical signal data, comprehensively evaluating the signal health status, and generating a multi-dimensional analysis report on the signal health status, using a fuzzy logic algorithm to accurately predict signal failures and attenuation trends, and dynamically adjusting the signal early warning threshold to avoid false alarms and missed alarms caused by fixed thresholds, realizing signal sharing and collaborative analysis between base stations through an intelligent collaborative detection mechanism, optimizing signal health management through cross-base station linkage, identifying potential signal failure areas in advance and optimizing response strategies, using a remote monitoring and repair platform, creating an intelligent operation and maintenance system through virtualization technology and intelligent algorithms, simulating fault repair scenarios and optimizing repair paths, and the system can adaptively learn and continuously optimize signal quality prediction and risk control capabilities.

The invention relates to the technical field of multi-sensor fusion, in particular to a high-reliability multi-sensor fusion pump station monitoring system and an intelligent early warning control method, and the method comprises the steps: 1, carrying out the synchronous fusion processing of data based on the sampling frequency difference of multi-source heterogeneous sensors, so as to guarantee the consistency of time axes; 2, calculating the consistency difference of the data of each sensor according to the synchronized data, and dynamically shielding an abnormal data source to avoid false alarm interference; 3, real-time operation state modeling is carried out based on the data change trend after dynamic shielding; and 4, accurate early warning and decision optimization are realized. According to the high-reliability multi-sensor fusion pump station monitoring system and the intelligent early warning control method, data synchronous fusion processing is performed based on the sampling frequency difference of the multi-source heterogeneous sensors, so that the consistency of a time axis is ensured, and the problem of influence of asynchronous data on the state judgment accuracy is effectively solved.

The invention discloses a submarine cable risk dynamic assessment method and system based on multi-modal deep learning, and belongs to the field of marine infrastructure operation and maintenance. Aiming at the problems of incomplete data coverage, unreal generated scene, low evaluation reliability and the like in the prior art, the method comprises the following steps of: 1) constructing a multi-source heterogeneous data set containing six types of data including geology, ocean, ships, biology and the like, and realizing data alignment by adopting space-time grid coding; 2) designing a physical constraint generative adversarial network, and generating risk scene data conforming to a fluid mechanics law through a Navier-Stokes equation constraint; 3) creating a hierarchical space-time fusion network (HST-Transform), and combining CNN spatial feature extraction, a time sequence attention mechanism and a dynamic memory module to realize multi-modal fusion; according to the method, the detection rate of rare risk events is increased by 62%, the evaluation accuracy rate reaches 91.7%, the false alarm rate is reduced by 34% compared with a traditional method, and submarine cable breakage accidents can be effectively prevented.

The invention discloses an intelligent monitoring system for a municipal drainage pipe network, and particularly relates to the technical field of drainage pipe network monitoring. The node operation mode identification module carries out real-time classification and confidence evaluation on the operation state of the pipe network, constructs a multi-attribute pipe network weighted graph based on pipe diameter difference, gradient and confluence density, and extracts multi-scale features through graph Fourier transform. A hybrid anomaly detection link is constructed in combination with an LSTM self-encoder, an isolated forest model and chemical oxygen demand and turbidity water quality verification, and the problems that traditional single-index monitoring is prone to false alarm and missing alarm and inaccurate in positioning are solved; sensor data compensation is realized through cooperation with digital twinning, a rapid detection mode is started during rainstorm early warning, key nodes are processed preferentially, and drainage scheduling is controlled in a closed-loop mode; and target nodes which are easy to accumulate grease are screened based on pipe network topology connectivity, accumulation risks are predicted by fusing multi-sensor data, and a preventive clearing instruction is triggered.

The invention relates to the technical field of state monitoring of wind generating sets, and discloses a fault early warning and service life prediction method and system for a wind generating set, and the method comprises the steps: obtaining first state data, second state data and image data of a target wind generating set, and forming multi-dimensional data; fusing the multi-dimensional data by using a multi-modal fusion model to obtain multi-modal data fusion features of the target wind generating set; and performing fault early warning and / or life prediction on the target wind generating set based on the multi-modal data fusion features. By integrating the multi-modal data, the problem that fault features are difficult to comprehensively capture by a single data source is solved, fault early warning and service life prediction are performed by utilizing the multi-modal data fusion features, the false report and missing report rate of faults is reduced, accurate quantitative prediction of the remaining service life of the wind generating set is realized in combination with the data driving model, and the prediction efficiency is improved. By improving the accuracy of fault early warning and life prediction, the wind generating set is effectively operated and maintained in advance.

The invention discloses a box-type substation state monitoring and early warning method based on artificial intelligence, relates to the technical field of intelligent power grids, and aims to solve the problems of missing report, false report and response lag caused by the fact that an existing static threshold ignores multi-physical coupling and a depth model highly depends on scarce fault samples. According to the scheme, sliding window kernel density estimation is carried out on a multi-channel time sequence signal, a dynamic coupling matrix is constructed through recursion Copula decomposition, a three-level threshold surface is generated through time-varying quantile regression, abnormal samples and graph attention network extraction state representation are generated in combination with a conditional variation auto-encoder, lightweight recursion pruning is carried out, and the dynamic coupling matrix is obtained. An abnormal score is generated through a multilayer Bayesian network and particle filtering, a multi-step risk trend is discriminated through a Gaussian kernel derivative slope, and finally unscented Kalman filtering is used for smoothing and online threshold correction; according to the method, the detection sensitivity and the early warning recall rate of the box-type substation to the transient coupling fault are remarkably improved, the response speed is improved, and the false alarm frequency is effectively reduced.

The invention relates to the technical field of risk management and early warning, and discloses a risk early warning method comprising the following steps: collecting multi-source data including structured data, unstructured data and time series data; the invention provides a risk early warning system, which is used for performing cleaning, standardization processing and fusion on multi-source data to generate a unified data set for analysis, and comprises a data acquisition module used for acquiring the multi-source data including structured data, unstructured data and time sequence data; the data processing module is used for carrying out cleaning, standardization processing and fusion on the multi-source data and generating a unified data set, the electronic equipment comprises a memory, a processor and a communication module, and the processor implements the risk early warning method during execution. Through multi-source data fusion, a dynamic threshold model and deep learning analysis, the risk is accurately identified, the method adapts to the dynamic environment in real time, false alarms and missing alarms are reduced, and the long-term monitoring adaptability is improved.

The invention discloses a heterogeneous sensing early warning system based on decoupling perception and adversarial robust learning, and the system comprises a feature extraction module which processes heterogeneous sensor original data collected in real time through a multi-layer decoupling encoder, separates target related features and environment interference features, and suppresses noise pollution from the source; the multi-dimensional collaborative fusion module adopts a cross-domain adversarial robustness learning framework to carry out space-time sequence alignment and deep fusion on decoupling features to generate high-robustness joint representation, and a data missing problem is processed through a cross-modal generative feature completion mechanism; and the cognitive enhancement closed-loop decision module constructs a cognitive heuristic confidence evaluation model based on joint representation, realizes graded early warning by combining real-time quality scoring and behavior prediction, and dynamically optimizes system parameters through a feedback mechanism. According to the method, the problems of poor target detection robustness, high delay and low accuracy in a complex dynamic environment are solved, the detection precision is remarkably improved, the false alarm rate is reduced, and the all-weather adaptive capacity is enhanced.

The invention relates to the technical field of abnormal monitoring of water-turbine generator sets, in particular to an abnormal monitoring system and method for a water-turbine generator set, and the system comprises a sensor module, an edge computing node, a cloud analysis platform and a scheduling decision center. The edge computing node performs data preprocessing, the cloud analysis platform realizes accurate early warning by applying technologies such as multi-source data fusion, a dynamic early warning model library and adaptive threshold adjustment, the scheduling decision center optimizes scheduling decisions through a resource matching engine and an emergency plan generation module, and the industrial communication network guarantees reliable transmission of data. In addition, the system is also provided with a visual interaction interface and a state evaluation report generation module. According to the invention, comprehensive and accurate monitoring and intelligent early warning of the water-turbine generator set are realized, the fault processing efficiency is effectively improved, the probability of false alarm and missing alarm is reduced, resource allocation is optimized, the operation safety is enhanced, and a powerful guarantee is provided for stable operation of the water-turbine generator set.

The invention relates to the technical field of battery detection, and particularly provides a battery fault early warning and diagnosis method, which comprises the following steps: acquiring operation parameters of a battery pack in real time, processing the operation parameters based on a preset judgment condition, generating a corresponding preprocessing signal according to a noise environment state, and extracting spatial-temporal characteristics to perform weak signal enhancement processing, so as to obtain a battery fault early warning and diagnosis result. Generating an enhanced feature set; based on the enhanced feature set, performing space-time fusion calculation according to a preset weight relation to obtain a fault energy accumulation value; dynamically correcting a fault judgment threshold according to the health state of the battery and the real-time environment parameters; and when the fault energy accumulation value exceeds the fault judgment threshold after dynamic correction, outputting a graded early warning signal. By capturing weak fault features of the battery pack and combining spatial domain feature extraction of temperature difference and voltage distribution and a signal enhancement technology, the detection sensitivity of early hidden faults is improved, and in a lithium battery safety early warning scene, the fault detection time is shortened, the false alarm rate is reduced and the like.

The invention provides an intelligent data production method and system based on a graph neural network and adaptive learning, and the method comprises the steps: collecting a text, a time sequence, an image and sensor data in real time, and converting unstructured data into a structured JSON format through a format analysis tool; extracting text entity features based on a large language model, modeling a cross-modal entity association relationship in combination with a graph neural network, and fusing multi-source heterogeneous data through a dynamic knowledge graph; the detection threshold is dynamically optimized by adopting a reinforcement learning algorithm, and the false alarm rate is reduced in combination with a double-track verification mechanism; and predicting a data trend by using the space-time diagram convolutional network, and outputting an interpretability analysis report through the generative large model. According to the invention, a multi-protocol adaptation engine and a distributed stream processing architecture are adopted, and a protocol analysis layer automatically identifies a plurality of industrial protocols, so that the manual adaptation time is reduced; and real-time synchronization of production line-level data is realized through edge node parallel acquisition and compression transmission.

The invention discloses a water conservancy project construction monitoring data supervision system and method based on multi-source data fusion, and relates to the technical field of engineering construction. The multi-source data fused hydraulic engineering construction monitoring data supervision system and method comprises the following steps: S1, acquiring hydraulic engineering construction monitoring data, and preprocessing the hydraulic engineering construction monitoring data; s2, constructing a sliding window, extracting key structure and environment variables, comprehensively analyzing a structure disturbance degree, and dividing different disturbance state sections according to the structure disturbance degree; s3, an unstable state time period is extracted, a disturbance feature sequence is constructed, highly-suspected disturbance fragments are identified, response deviation between the structure and the environment is analyzed, highly-credible abnormal fragments are screened, and an abnormal data structure is generated; and S4, the coupling relation between the structural response and the environmental disturbance is analyzed in a combined mode, and a dynamic unbalance index used for measuring the unsteady state intensity is constructed. The problem that the abnormal recognition false alarm rate is high due to the fact that monitoring data are disturbed frequently in a complex construction scene is solved.

The invention discloses a data anomaly detection method based on multi-scale feature extraction, relates to the technical field of data anomaly detection, and sequentially performs multi-source data acquisition and environment calibration, noise robustness multi-scale feature extraction, context awareness anomaly detection and sensor redundancy check, and adaptive feedback and collaborative update decision. Intelligent threshold learning and scene self-adaption are realized; the method comprises the following steps: firstly, acquiring and marking acceleration, current, electromagnetic and other multi-path signals, and constructing an environment label; then, utilizing a TCN and LSTM fusion model to extract short-term impact and long-term trend; according to the elevator working condition and the noise index, the threshold value is dynamically adjusted, and the fault is subjected to redundancy check; the model, the sensor weight and the threshold value are corrected through false alarm and missing alarm backflow; and finally, a monitoring-detection-feedback-updating-self-adaptive closed loop is constructed by means of reinforcement learning or meta learning cross-seasonal adaptation, high precision, low false alarm and high robustness are achieved, and elevator operation safety and maintenance efficiency are remarkably improved.

The invention discloses an industrial network risk perception and collaborative early warning method based on a dynamic risk map, and relates to the technical field of industrial internet security, and the method comprises the steps: S1, multi-source perception deployment; s2, heterogeneous data fusion acquisition; s3, constructing a knowledge graph engine; s4, analyzing depth data; s5, performing dynamic risk assessment; and S6, intelligent early warning decision making. According to the industrial network risk perception and collaborative early warning method based on the dynamic risk map, through fusion perception of OT layer data such as equipment states and process parameters, the problems of single perception dimension, evaluation lagging and disjunction in the prior art are solved, the false alarm rate is extremely low, and the method is suitable for popularization and application. Particularly, a dynamic adjustment mechanism of a time-varying risk weight matrix is improved, novel attacks can be dynamically responded, meanwhile, cross-domain risk conduction analysis is achieved, the accuracy and response speed of industrial network security early warning are improved, and meanwhile a closed-loop mechanism of attack path prediction and disposal suggestions is constructed.

The invention relates to the technical field of fault analysis, in particular to a power grid fault intelligent diagnosis and analysis system which comprises a fault signal detection module, a propagation path analysis module, a fault positioning calculation module, a fault type analysis module and a fault influence analysis module. According to the invention, by monitoring the voltage transient data of the plurality of nodes of the power grid in real time, the instantaneous offset and the time sequence difference of the voltage can be accurately calculated, so that the voltage mutation exceeding the preset threshold value can be quickly identified in the continuous time window, the early identification of the power grid fault is quicker, the risk of false alarm is reduced, and the accuracy of the early identification is improved. By analyzing the propagation path and speed of the sudden change quantity along the power grid, the fault positioning precision is improved, rapid and accurate fault source tracing is realized, and by careful analysis of the current waveform of a fault point and combination of real-time tripping current data, the fault type can be identified more accurately, the fault handling strategy can be optimized, and the fault handling efficiency is improved. And the power grid fault processing efficiency and safety are improved.

The invention provides a fire early warning and intelligent fire extinguishing method based on image recognition. The fire early warning and intelligent fire extinguishing method comprises the steps that a camera network is used for covering a target monitoring area, flame and smoke characteristic parameters are input, noise is removed through image preprocessing, and image data are standardized. A heat source point is selected as a camera installation position in combination with a fire propagation mode, and layout is optimized. And calling image data, performing real-time analysis based on a dynamic flame sequence, extracting abnormal response, calculating a fire risk value by using a mode recognition algorithm, and generating peak fire probability data. And collecting real-time fire characteristic data, comparing the data with a risk value after filtering and noise reduction, and setting multi-level threshold values to generate an alarm result. And analyzing a false alarm reason, optimizing the position of the camera and the starting condition of the fire extinguishing device, and generating a fire risk and fire extinguishing efficiency report. According to the invention, the accuracy and timeliness of fire early warning can be improved, the false alarm rate is reduced, and the fire extinguishing efficiency is enhanced.

The invention provides an optical fiber communication equipment monitoring method and system based on big data and a medium, and belongs to the technical field of optical fiber communication and big data. The method comprises the following steps: synchronously acquiring operating parameters and environmental parameters of optical fiber equipment through a multi-source sensor network, and generating a standard parameter information matrix through space-time alignment and noise suppression; further extracting characteristics such as an optical signal stability index and vibration energy anomaly through time-frequency domain conjoint analysis, and constructing an equipment health state matrix; and predicting a degradation process based on a bidirectional LSTM model of an attention mechanism, and outputting a visual diagnosis result in combination with a dynamic threshold grading alarm mechanism. According to the scheme, the problems of high false alarm rate and insufficient prediction capability of a traditional monitoring method are solved, the false alarm rate is reduced, and the fault prediction accuracy is improved.