72 results about "Response strategy" patented technology

The invention relates to the technical field of data analysis, provides an urban meteorological disaster data identification method and system based on deep reinforcement learning, and is used for effectively improving the model performance so as to enhance the accuracy and real-time performance of meteorological disaster monitoring. The method comprises the steps of obtaining a meteorological monitoring data set of a target city area, executing meteorological data preprocessing operation on the meteorological monitoring data set to obtain a preprocessed meteorological spatial-temporal feature set, calling a trained deep reinforcement learning recognition model, and performing dynamic disaster mode matching processing on the meteorological spatial-temporal feature set to obtain a dynamic disaster mode recognition model. And generating a meteorological disaster recognition result set of the target city region, generating a disaster coping strategy set according to the meteorological disaster recognition result set, and performing dynamic strategy optimization processing on the deep reinforcement learning recognition model based on the disaster coping strategy set to obtain an optimized deep reinforcement learning recognition model. And deploying the optimized deep reinforcement learning recognition model to a meteorological disaster monitoring system.

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 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 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 discloses a multi-agent traffic event real-time response system and method, and the system comprises an event perception fusion module which is disposed on vehicle-mounted and roadside equipment, fuses the vehicle motion state, environment perception data and meteorological data, and generates an unstructured traffic behavior set in real time through a lightweight recognition model; the edge decision-making module is used for defining decision-making responsibilities by adopting a dynamic role binding mechanism and decomposing tasks based on a hierarchical memory storage architecture to generate response instructions; the central collaborative decision-making module is used for realizing strategy decoupling through role adapter layering and coordinating cross-domain response measures by adopting a three-level conflict resolution mechanism; and the strategy optimization module updates center parameters through federated learning and optimizes a response strategy in combination with a reinforcement learning reward model, so that second-level identification and dynamic response of traffic events can be realized, the congestion dissipation time is shortened, the false report and missing report rate is reduced, the secondary accident rate is reduced, and the disposal efficiency and safety of complex traffic scenes are comprehensively improved.

The invention discloses an intelligent identification and grading early warning method based on mine production abnormity, and a system and equipment thereof. The method comprises the following steps: obtaining space-time evolution data, earth surface deformation data and environment monitoring data of rock mass stress in a target area; performing abnormal mode detection on the data by using a space-time convolutional network to obtain accident risk probability distribution data; using an ultra wide band positioning system to track the movement track of the person entering the target area in real time to obtain the movement track data of the person; and constructing a three-dimensional early warning index system in combination with the accident risk probability distribution data in the target area and a dynamic early warning mechanism, performing dynamic early warning grading on different positions in the target area by using the three-dimensional early warning index system, and generating a grading response strategy matched with the personnel activity trajectory data. According to the invention, the problems of incapability of tracking a personnel movement track in real time and low timeliness of mine production abnormity early warning are solved.

The invention provides an artificial intelligence early warning and management method for a smart ocean, and is applied to the field of data processing application. Aiming at the problems that intelligent ocean data is large in scale and complex in multiple sources, threat identification is single in the prior art, an early warning model lacks self-adaptive adjustment and is prone to false alarm and missing alarm, and data security is difficult to guarantee, the method is based on intelligent ocean multi-source data and a preset data set containing data types, security levels and other labels; after preprocessing, a deep learning framework is used to train a threat identification and early warning model. The model detects four types of features such as sensitive information in real-time data, extracts parameters, matches threat features to determine risk levels, and establishes mapping relationships between data types and threat and abnormal modes. Through abnormal threshold evaluation, false alarms are removed to generate initial early warning parameters, a machine learning iterative optimization model is combined, risk data are finally sorted, a response strategy is constructed and the like, a safety management scheme is formulated to generate a result, and intelligent upgrading from passive response to active defense is realized.

The invention belongs to the technical field of disaster alarm analysis, and particularly relates to a trend analysis system and method based on multi-dimensional disaster alarm data. By optimizing the matching rule and the threshold value in real time, the accuracy and timeliness of disaster prediction are remarkably improved, deep fusion and efficient utilization of multi-source data are achieved, a scientific basis and technical support are provided for disaster early warning and emergency response, meanwhile, the system has the self-adaptive learning ability, and the system can be widely applied to disaster early warning and emergency response. According to the method, the model can be continuously optimized in continuously updated data, the dynamic accuracy of a prediction result is ensured, the disaster prevention and reduction capability is effectively improved, in addition, through comprehensive analysis of multi-dimensional data, the comprehensiveness and reliability of disaster early warning are enhanced, a visual disaster evolution situation map is provided for decision makers, rapid formulation of coping strategies is assisted, and the method is suitable for popularization and application. The disaster loss is reduced to the maximum extent; and the emergency response efficiency is improved.

The invention discloses a meteorological disaster early warning response method and system based on a meteorological early warning aircraft, and the system comprises a data collection module which is used for constructing a database containing the real-time meteorological data, the terrain data, the historical disaster data and the early warning aircraft operation data; the multi-disaster terrain adaptation analysis module is used for analyzing waterlogging and gale comprehensive risks by using a synchronous superposition model in plains and analyzing landslide and thunder-gale chain risks by using a time sequence chain model in mountainous areas through a double-branch fusion neural network; the early warning response strategy generation module is used for generating differentiation strategies according to regional risks and terrains; and the terminal interaction module pushes information and receives feedback. The system dynamically generates protection guidance for different geographic features such as a plain high-risk area and a mountainous area high-risk area through multi-source meteorological data integration and analysis, and improves the timeliness and coverage of early warning information.

The invention provides an intelligent early warning and response method for a slope instability risk, and belongs to the technical field of intelligent early warning based on data analysis. The method comprises the following steps: firstly, collecting multi-dimensional environmental geological data, and introducing an instability type and spatio-temporal evolution chain double-label mechanism to form a dynamic data set; then, through slope disturbance sensitivity mapping modeling, key influence factors and weights thereof are determined, and input feature selection and adaptive optimization of a model structure are realized; a deep learning model fusing a multi-scale convolutional neural network and an attention mechanism is adopted to extract multi-layer spatial-temporal features, and model parameters are dynamically adjusted, so that the slope instability probability and evolution path are accurately predicted; and finally, combining an evolutionary game theory to bring the risk level, the response time efficiency and the treatment cost into multi-objective optimization, and generating an optimal intelligent response strategy. According to the method, dynamic perception, sensitive modeling, intelligent prediction and scientific response of the slope instability risk are realized, and the prediction precision and the emergency response efficiency are effectively improved.

The invention relates to an unstable slope monitoring and early warning method, system and device and a medium, and belongs to the technical field of geological disaster monitoring, and the monitoring and early warning method comprises the steps: collecting original monitoring data of a target slope region, carrying out the edge preprocessing, and generating a standardized data package; transmitting the standardized data packet to a cloud server for integrity verification and logic verification, and generating a verified data set; performing multi-modal data fusion on the verified data set, constructing a displacement-rainfall-microseismic time sequence incidence matrix, extracting displacement acceleration, rainfall cumulative intensity and microseismic event frequency features, and generating a fusion feature vector set; inputting the fusion feature vector set into a pre-trained risk prediction model for real-time risk prediction, and outputting a landslide risk index and a displacement trend prediction result; and determining a corresponding early warning level according to the landslide risk index, and triggering an automatic response strategy corresponding to the early warning level. According to the invention, the real-time performance and accuracy of landslide early warning can be improved.

The invention discloses a meteorological water conservancy disaster intelligent grading early warning linkage call routing method and a meteorological water conservancy disaster intelligent grading early warning linkage call routing system. A meteorological water conservancy two-factor early warning matrix is constructed, a cross-department data fusion channel is established, an early warning coupling model is developed, and multi-dimensional early warning fusion is achieved. And formulating a water conservancy special response strategy library, wherein the water conservancy special response strategy library comprises river water level grading routing and mountain torrent risk area customization strategies. And developing an intelligent water conservancy enhancement module, such as a flood control project topology analysis engine and a water and rain work condition semantic analyzer. The method is characterized in that a three-element risk rating model is constructed by an initiative dynamic coupling algorithm, and a water conservancy emergency response topology network based on double dimensions and a water conservancy project BIM data and meteorological early warning space superposition analysis module are adopted. The accuracy and comprehensiveness of disaster early warning can be improved, the timeliness and effectiveness of emergency response are ensured, and the intelligent level and disaster response capacity of the system are improved.

The invention discloses an intelligent water conservancy monitoring system and method based on air and underwater cooperation, and relates to the technical field of water conservancy projects, and functional modules of the system comprise an air platform, an underwater platform and a command center; comprising the following steps of: modeling a dynamic task demand based on multi-dimensional environment collaborative awareness; an air-horizontal platform task allocation mechanism based on collaborative path-load optimization; a multi-source data space-time coding and platform embedded sensing guide sampling mechanism; edge node multi-source observation collaborative fusion and local anomaly recognition; carrying out risk trend prediction and regional priority reconstruction based on a dynamic evolution network; and generating an intelligent response strategy based on risk dynamic reconstruction. According to the method, a cooperative working mechanism of an air platform and an underwater platform is constructed, and technologies of multi-source data fusion, a multi-platform linkage response strategy and the like are combined, so that systematic enhancement of a water conservancy project area in multiple dimensions of full space coverage, high time frequency, strong risk identification, response schedulability and the like is realized.

The invention belongs to the technical field of intelligent manufacturing and production scheduling, discloses a flexible job shop dynamic scheduling method and system considering machine aging, and designs a hierarchical environmental response strategy which can firstly evaluate the severity of environmental change. When the change is not violent, only a lightweight local optimization strategy is adopted for fine adjustment; and when the change is relatively violent, a global reconstruction strategy combining knowledge migration, reinitialization and directional repair is started. Therefore, the algorithm can intelligently allocate computing resources according to the intensity of environment change, blind global search is avoided, and the response speed and the operation efficiency of the algorithm are greatly improved. The design effectively balances the exploration and utilization capabilities of the algorithm, and maintains the diversity of the population while ensuring rapid convergence, thereby obtaining a group of Pareto optimal solution sets with good convergence and wider distribution.

The invention belongs to the technical field of fire-fighting monitoring, and particularly relates to a multi-source intelligent fire-fighting monitoring and response method, system and terminal based on hierarchical linkage. The method comprises the following steps: acquiring environmental parameters such as temperature and smoke collected by monitoring equipment in a plurality of target areas, and constructing first data information; a judgment model is generated based on the temperature and smoke change trend, and the regional state is judged to be safe or alarm; if the state is an alarm state, determining a disaster category in combination with a threshold comparison result; executing alarm or fire extinguishing response according to the disaster category; meanwhile, identifying an associated region related to the target region, determining a linkage level according to a preset rule, and executing a hierarchical response; and finally, generating a checking result according to a response effect, and dynamically optimizing the judgment model and the response strategy. According to the system, multi-source information fusion, intelligent fire identification and multi-region collaborative response are realized, and the timeliness and accuracy of intelligent fire fighting are improved.

The invention discloses a self-adaptive adjustment method and system based on an energy management system, and relates to the technical field of energy management, and the method comprises the following steps: collecting multi-dimensional heterogeneous data including weather conditions, economic situations, social activities, user behavior patterns and energy price fluctuations; according to the method, the multi-dimensional heterogeneous data is fused, the space-time attention mechanism and the graph neural network are utilized to construct the high-precision prediction model, ultra-short-term to medium-and-long-term energy demand dynamic prediction is realized, the problem of insufficient multi-factor fusion is solved, the hierarchical response strategy is triggered based on the prediction result, and the prediction efficiency is improved. Distributed energy storage, a flexible load and a production end are linked to perform dynamic power adjustment, strategy autonomous optimization is realized through reinforcement learning, the adaptive adjustment ability to a complex scene is improved, an adaptive adjustment closed loop is formed through real-time error calculation and model parameter correction, and adjustment parameters and model weights are corrected in real time, so that the real-time performance of the system is improved. And the stability and accuracy of long-term operation of the system are ensured.

The invention provides an on-line monitoring and early warning method for whole-process beneficiation wastewater, and belongs to the field of on-line monitoring and early warning, and the method comprises the steps: deploying an on-line monitoring group for a corresponding link according to the wastewater treatment attribute of each link in the whole-process beneficiation wastewater process of a factory, and according to the mapping relation between the historical abnormal event and the treatment experience of each link, constructing an on-line monitoring group for the corresponding link; determining the participation effect of each historical abnormal water quality parameter in the historical abnormal event in the corresponding link and an adjustment scheme surrounding the historical abnormal water quality parameters, and establishing a response strategy library; establishing an input and output parameter model associated with the corresponding link according to the connection relationship of each link, and determining a current water quality abnormal parameter of the corresponding link according to a water quality analysis result to generate a water quality abnormal event; and comprehensively matching all the water quality abnormal events with the response strategy library, and determining and adjusting an early warning response strategy of each link. Problems in the wastewater treatment process can be quickly found and solved, and the risk that wastewater discharge does not reach the standard is effectively reduced.

The invention discloses a self-adaptive perimeter protection linkage disposal method and system, particularly relates to the technical field of security linkage alarm control, and is used for solving the problem of poor multi-target self-adaptive response linkage. According to the method, multi-device equipment is deployed to collect multi-source sensing data in real time, time format standardization and space mapping are carried out, and a fused multi-target sensing frame is generated; extracting dynamic features such as speed, direction, residence time and path of the target object, constructing a feature vector, matching the feature vector with a historical behavior template, and generating a threat level trend; a response resource scheduling graph is constructed in combination with response equipment states, a response cost value is calculated, an optimal strategy sequence is generated, and actions such as voice warning and illumination intervention are executed in a linkage mode; equipment feedback is collected in the execution process, whether the strategy fails or is in an abnormal closed loop or not is judged, if yes, the threat level is evaluated again, the response strategy is dynamically reconstructed, the strategy chain length is compressed, and the accuracy and linkage efficiency of perimeter protection are improved.

The invention provides a multi-disaster-type linkage-oriented grain and cotton crop meteorological risk grading early warning method and system, and relates to the technical field of risk grading early warning. The first information comprises soil salinity change data, frost meteorological data, drought meteorological data, wind disaster meteorological data and crop monitoring data in a preset time period; performing disaster type space-time relation analysis on the first information to generate a multi-disaster type linkage space-time model; performing fusion processing on the multi-disaster linkage spatial-temporal model through spectral clustering and a preset gradient boosting tree to obtain a disaster influence prediction model; carrying out adaptive filtering and grading judgment processing on the disaster influence prediction model to obtain a disaster influence grade; and generating an adversarial network based on the disaster influence levels and historical early warning coping strategies, and determining coping strategies corresponding to all disaster influence levels of each crop. According to the invention, targeted disaster grading can be carried out according to soil types and crop planting characteristics in different areas.

The invention relates to the technical field of data processing, and particularly provides a disaster response decision-making method and device based on multiple modes, a medium and equipment, and the method can comprise the steps: obtaining structural data corresponding to original data of a disaster site in real time; wherein the original data comprises video data, image data, voice data or disaster monitoring data; the structured data comprises disaster event types, spatial positions, confidence coefficients and timestamps; taking the structured data as a retrieval element, positioning a semantic position of the disaster site from a pre-constructed disaster chain knowledge base, and reasoning a chain evolution path of the disaster site; obtaining a response measure set corresponding to the semantic position; and acquiring a disaster response strategy according to the original data, the structured data, the chained evolution path and the response measure set. According to the embodiment of the invention, the decision-making precision of disaster treatment can be improved.

The invention discloses a power transmission line fault prediction and strategy generation method and system in severe convective weather, and the method comprises the steps: obtaining the grid weather forecast data of a target region, extracting severe convective weather parameters, calculating the meteorological threat index of a power transmission line based on the data, dividing the power transmission line into a plurality of line units, and carrying out the fault prediction of the power transmission line. Extracting anti-disaster toughness attributes of the line units, fusing meteorological threat indexes of meteorological grid points of the positions where the line units are located, predicting failure rates of the line units through a pre-trained machine learning model, determining risk levels of the line units according to the failure rates, and combining a preset risk level-disposal rule mapping library to determine the risk levels of the line units. And generating and pushing a corresponding coping strategy for each line unit. According to the method, the fault rate of the individual line unit is accurately and quantitatively predicted by using the pre-trained machine learning model, the early warning accuracy, the strategy performability and the operation and maintenance resource use efficiency are greatly improved, and an effective technical support is provided for ensuring safe and stable operation of a power grid in extreme weather.

The invention relates to a coal mine underground safety comprehensive management method, system and device and a medium. The method comprises the following steps: establishing a coal mine underground risk factor set and a corresponding risk assessment index set; acquiring monitoring data of each element in the risk assessment index set to form a risk assessment index monitoring data set; based on a hierarchy analysis method and a fuzzy comprehensive evaluation method, first and second overall risk evaluation results are obtained according to the monitoring data set, and then the first and second overall risk evaluation results are synthesized to obtain a comprehensive risk evaluation result; and finally generating a response strategy according to the comprehensive risk evaluation result. Therefore, comprehensive and scientific assessment of the safety condition of the underground coal mine is realized, targeted prevention measure guidance is provided for managers, the safety management level of the underground coal mine is effectively improved, and the possibility of safety accidents is reduced.

The present application belongs to the technical field of intelligent regulation, and discloses an intelligent control cabinet regulation method and system based on environmental regulation, comprising: obtaining environmental parameter regions and corresponding three-dimensional environmental data; obtaining environmental regulation sensitivity and environmental stability through parameter coupling analysis and air flow state distribution analysis; evaluating air flow interaction based on environmental stability to obtain air flow balance strength and regulation prediction index; constructing an adaptive control framework to form a dynamic fusion control strategy; constructing an anomaly detection model to analyze environmental parameter abnormal fluctuations and generate an abnormal response strategy; combining the dynamic fusion control strategy and the abnormal response strategy to perform parameter optimization and energy efficiency adjustment, and generating an optimal environmental regulation scheme; finally, executing the scheme and performing real-time monitoring and feedback adjustment; the present application realizes precise environmental regulation of an intelligent control cabinet.

This invention relates to the field of highway inspection technology, specifically to an adaptive low-altitude highway inspection method and system for extreme environments. The method includes: collecting heterogeneous data from UAV airborne sensors, roadside weather stations, and geographic information systems; constructing an environmental feature vector including a sensor reliability index; retrieving an extreme scenario database based on this vector; running a hybrid planning model to generate a flexible task package including a main execution strategy and a scenario-response strategy; scheduling a UAV swarm to execute the flexible task package; calculating an environmental comprehensive score and dynamic link weights to dynamically reconstruct the network topology of the UAV swarm; adaptively enhancing the collected data to remove interference; and using a nonlinear confidence model to fuse the judgment results of multiple algorithms to generate a highway inspection judgment report. This invention ensures flight safety and communication stability in extreme environments through environment-driven flexible task planning and dynamic topology reconstruction.

The application provides a robot control method, computer equipment and a computer readable storage medium. The method comprises: acquiring multi-modal data of a user, the multi-modal data comprising posture data, sound data and force control data; performing feature extraction and fusion processing on the multi-modal data to obtain multi-modal fusion features; determining context information of human-computer interaction according to environmental information of an area where a robot is located and historical interaction data of the robot; generating a response strategy according to the multi-modal fusion features and the context information, and controlling movement of the robot according to the response strategy, so as to improve naturalness and accuracy of human-computer interaction.

The invention discloses a multi-scene-oriented volatile organic compound real-time monitoring and early warning method, and relates to the technical field of intelligent sensor monitoring. The method comprises the following steps: dividing microenvironment types in a target scene, defining the health tolerance of a benchmarking object, selecting whether to send a marking signal or not, synchronously constructing a microenvironment health tolerance model, and outputting microenvironment health tolerance indexes corresponding to different microenvironment types; the method is technically characterized in that a dual-condition trigger logic of a mark signal and an anomaly residual error is utilized, so that a hierarchical response strategy is associated with spatial semantics and reflects pollution causes; and on the other hand, fine control of grading and enhanced response measures is also realized, balance between excessive intervention and insufficient response is sought, and the extensive problem existing in a switching mode in the traditional early warning is solved, so that the intervention measures are accurately matched with the risk grades, and the effects of efficiency and user experience are considered to a certain extent.

The present invention relates to an excess power consumption prediction system that collects power consumption, power generation, and weather information of a factory, predicts a power excess state in which the consumption exceeds the generation, analysis causes using operation information and correction information, and generates a response strategy.

The invention discloses a recognition and driving system for preventing biological invasion and a control method, and the system comprises a dynamic risk field modeling module which is used for carrying out the real-time scanning of the spatial information of a monitoring site; the YOLO-GAN collaborative recognition module is used for recognizing and positioning a target, extracting target features and generating a dynamic track; the environment coupling control engine is used for collecting environment parameters of the monitoring site in real time; the driving device is used for driving according to the control instruction; and an edge intelligent decision-making unit. The laser radar is arranged for space partitioning and real-time scanning and monitoring, suspicious organisms can be recognized more accurately through YOLO-GAN collaborative biological recognition, meteorological self-adaption can be achieved through the environment coupling electromagnetic field, normal operation can be achieved even in an extreme environment, a multi-stage response strategy is formed in cooperation with the upper driving device, and the driving device can be used for driving a driving device to drive the driving device to drive the driving device to drive the driving device to drive the driving device to drive the driving device to drive the driving device to drive the driving device. And the transformer substation protection system which is low in cost, low in complexity and eco-friendly is formed.

The invention relates to the technical field of geophysical detection and intelligent signal processing, in particular to a geomagnetic anomaly feature recognition method and system based on big data. The method comprises the following steps: carrying out integrated deep preprocessing consisting of high-precision time synchronization, double-loop drift correction, environment association annotation and noise fingerprinting on multi-source heterogeneous geomagnetic observation data; the point type record is converted into a gridding multi-scale behavior vector fusing a background and an abnormal residual error through partition focusing, sequence sparse warping and local disturbance layer construction; performing node manifold embedding and abnormal mode propagation based on a space-time graph structure, and performing coding to form a low-dimensional abnormal mode vector; abnormal types are distinguished by utilizing clustering classification, an abnormal trend is predicted through machine learning in combination with a historical mode, and a response strategy is automatically generated. According to the method, full-link intelligent processing from data acquisition, feature extraction, pattern recognition to prediction response is realized, and the accuracy and early discovery capability of geomagnetic anomaly recognition are remarkably improved.

The application provides an artificial intelligence early warning and management method for smart ocean, and is applied to the field of data processing.The present application is aimed at the problems that the smart ocean data is large in scale, multi-source and complex, the threat identification of the prior art is single, the early warning model lacks self-adaptive adjustment and is prone to false alarm and missed alarm, and it is difficult to guarantee data security, and is based on multi-source data of smart ocean and preset data sets containing data types, security levels and the like, and after preprocessing, a threat identification and early warning model is trained by using a deep learning framework.The model detects four types of features such as sensitive information in real-time data, extracts parameters, matches threat features to determine the risk level, and establishes the mapping relationship between data types and threats and abnormal patterns.The false alarm is removed through abnormal threshold evaluation to generate initial early warning parameters, the model is iteratively optimized in combination with machine learning, the risk data is finally sorted, the response strategy is constructed, the safety management scheme is formulated, and the result is generated, so that the intelligent upgrading from passive response to active defense is realized.