810 results about "Situation awareness" patented technology

The embodiment of the invention relates to the technical field of ship scheduling, in particular to a multi-source data-based intelligent auxiliary decision-making method for ship entering and leaving a port and berthing and leaving the port, and the real-time performance and accuracy of port scheduling decision-making can be improved by fusing multi-source heterogeneous data to construct a ship navigation situation awareness system. The space-time alignment technology effectively eliminates space-time dislocation of environment interference factors and enhances the characterization capability of the ship motion state and environment coupling relation. Quantitative evaluation of multi-dimensional navigation risks is realized based on a ship behavior prediction model, and potential risks such as channel deviation and berthing conflicts can be identified. A collaborative decision-making scheme considering safety and efficiency is generated through a collaborative optimization mechanism of the dynamic scheduling rule base and the risk indexes, and ship trajectory control precision is optimized through dynamic planning of navigational speed adjustment and steering time sequence. Closed-loop linkage of port resource allocation and ship navigation control is achieved through automatic instruction generation and execution, and the reliability and the response speed of the port operation process can be ensured.

The invention relates to the technical field of intelligent data fusion deduction, in particular to a task planning method and system based on intelligent deduction, and the method comprises the steps: constructing a rule knowledge graph with weight annotation through integrating a real-time battlefield situation and a preset rule library, and carrying out the task planning through the real-time battlefield situation; a large-model intention understanding engine is used for analyzing a commander instruction to generate a five-dimensional intention vector, an initial scheme is generated by combining a double-engine mechanism of a rule hard constraint engine and a large-model soft constraint engine, and graph weights are dynamically adjusted through reinforcement learning to optimize a rule path. The system monitors node conflict frequency in real time, adopts a near-end strategy optimization algorithm to re-distribute weights for high-frequency conflicts, activates a new rule injection mechanism when the intention conformity is insufficient, and finally outputs a three-level structured scheme including a main scheme, an alternative scheme set and a risk early warning report, thereby realizing an intelligent closed loop from situation awareness to scheme generation. And the command decision-making efficiency and the combat collaboration are obviously improved.

The invention relates to the technical field of emergency command, and discloses an emergency command system and method based on a BIM digital base, and the method comprises the steps: building a unified data model through expanding an IFC standard, carrying out the semantic mapping and entity alignment of BIM building information, GIS geographic space data, monitoring data and an emergency plan document, and carrying out the construction of a unified data model. The method comprises the steps of generating an emergency knowledge graph with BIM building information as nodes, coupling physical simulation and an LSTM prediction algorithm, injecting monitoring data into a BIM model to deduce a disaster process, outputting a risk thermodynamic diagram and an evacuation forbidden zone, and calculating space-time accessibility of emergency resources through a reinforcement learning model according to a deduction result. An optimization scheme including an unmanned aerial vehicle path, a personnel evacuation route and an equipment scheduling instruction is generated, the instruction is synchronized to an augmented reality individual soldier terminal and a mobile command shelter through an edge computing node, a resource deployment state is labeled in real time in a BIM model, multi-terminal operation is synchronized, the timeliness and accuracy of disaster situation sensing are remarkably improved, and the disaster situation sensing efficiency is improved. And disaster loss is effectively reduced.

The invention discloses an intelligent maritime affair security situation awareness system and method, and relates to the technical field of path planning, and the method comprises the steps: obtaining multi-source ship data, carrying out the space-time dimension alignment and feature weight distribution, generating a fusion feature tensor, extracting a spatial topological relation through a graph convolution operation, and capturing a time dependence feature through an LSTM model. Constructing a ship interaction potential energy field model in combination with the spatial topological relation and the time dependence features, calculating a collision risk index in combination with a conflict cone algorithm, generating a dynamically updated sea area risk thermodynamic diagram, and performing situation map construction on the sea area risk thermodynamic diagram and the fusion feature tensor to obtain a dynamic situation map; a ship-environment-event triple relationship is extracted by adopting a hierarchical graph attention network, an interpretable semantic situation map is constructed, a threat matrix is generated through node-level risk assessment in sequence, and an air route planning scheme is obtained through regional-level path optimization; high-risk areas can be avoided, and navigation safety and efficiency are improved.

The invention relates to the technical field of data processing, and discloses a multi-data fusion method and system in airport station safety supervision and a medium. The method comprises the following steps: based on a target feature matrix, judging potential conflicts through a set safety distance threshold and a speed threshold, and constructing a risk assessment index in combination with historical risk event data to obtain a safety risk score; according to the security risk score, establishing a spatial association relationship and a risk propagation relationship between the apron targets, calculating regional risk density through a risk accumulation effect, and generating real-time security situation data; and for the real-time security situation data, calculating a target function value of security control, and setting a security constraint condition for optimization solution to form a control strategy parameter. According to the invention, effective fusion of multi-source heterogeneous data is realized, accurate awareness of the security situation of the station level is constructed, and an optimized management and control strategy is generated based on situation information, so that the accuracy and real-time performance of security supervision of the station level are improved.

The invention relates to a ship safety monitoring method, system and device based on data fusion, and relates to the technical field of ship navigation safety, and the ship safety monitoring method comprises the steps: collecting multi-source heterogeneous data; preprocessing the multi-source heterogeneous data; carrying out fusion processing on the multi-source heterogeneous data based on an information entropy theory; a ship safety domain model is constructed, trajectory prediction is executed, a collision risk index is calculated, and the ship collision risk is evaluated; and performing anomaly detection on the ship equipment operation data based on manifold learning and a thermodynamic model. According to the invention, a multi-source heterogeneous data fusion technology is adopted, multi-dimensional data sources are integrated, and omnibearing ship safety situation awareness is constructed. The early warning time is prolonged to 8-10 minutes, which is far better than the early warning time of 3-5 minutes in the prior art. The system thoroughly solves the problem that subsystems in a traditional ship monitoring system operate independently and do not communicate with one another, the crew information acquisition cost is reduced, and the number of times of interactive operation is reduced by 42%.

The invention relates to the technical field of water conservancy project risk early warning, in particular to a multi-modal time-space fusion water conservancy risk real-time early warning system, which comprises a multi-modal time-space data fusion module for performing time-space alignment and feature extraction calculation based on reservoir group sensor data flow, remote sensing images and weather forecast to obtain a fusion time-space situation value. According to the invention, by fusing the reservoir group sensor data flow, the remote sensing image and the weather forecast data, executing the space-time alignment and feature extraction calculation, and constructing the fused space-time situation value, the collaborative analysis precision and situation awareness of the multi-source heterogeneous data are improved, and the data utilization rate and the risk analysis accuracy are improved; when a potential scheduling operation set is established, gate opening degrees and flood discharge flow adjustment options under different time windows are defined, and the flexibility and the strain capacity of a scheduling scheme are enhanced, so that the scheduling operation better fits actual hydrological condition changes.

The invention relates to the technical field of low-altitude aircraft navigation, and discloses a low-altitude aircraft track real-time planning method and system. The system comprises a flight situation awareness module, a track constraint calculation module, a real-time track planning module, a conflict prediction module and a track dynamic correction module. The flight situation sensing module generates a flight situation matrix through multi-source data fusion; a track constraint calculation module extracts static obstacle contours and dynamic obstacle tracks according to the static obstacle contours and the dynamic obstacle tracks, and generates a multi-dimensional track constraint set in combination with aircraft performance parameters; the real-time flight path planning module builds a three-dimensional flight path search domain by using an adaptive space division technology, and iteratively solves an optimal flight path sequence by using an intelligent search algorithm; the conflict prediction module combines the real-time dynamic obstacle trajectory to calculate the space-time proximity, and generates a conflict early warning map; and the track dynamic correction module re-draws an obstacle avoidance constraint area according to the map, and triggers local track correction. The system improves the comprehensiveness, real-time performance and safety of flight path planning, and guarantees the stable operation of the low-altitude aircraft.

The invention discloses an intelligent management system for nuclear power plant personnel situation prediction and risk assessment, and relates to the field of intelligent safety management systems, and the system comprises a data collection unit, a multi-dimensional situation awareness unit, a risk prediction and assessment unit, an intelligent decision intervention unit and a visual interaction unit. And multi-source data acquisition, real-time situation construction, dynamic risk prediction and evaluation, intelligent early warning intervention and information visualization are realized. According to the invention, real-time monitoring, dynamic risk prediction and intelligent management of the safety state of the operating personnel can be realized, and the defects of real-time monitoring, dynamic prediction and intelligent management of the operating personnel in a high-risk area in the prior art are overcome, so that the safety management level is improved, the life safety is guaranteed, and the accident occurrence probability is reduced.

The invention provides a medical material distribution method and system based on an offshore medical rescue unmanned aerial vehicle. Wherein a marine environment model is generated; according to the marine environment model, an AI scheduling algorithm is applied to analyze the specific demand, priority and emergency degree of a marine receiving point, and an optimized task sequence and a material loading scheme are obtained; establishing a cooperative communication mechanism between offshore medical rescue unmanned aerial vehicle formations through an AI-driven distributed consensus algorithm, and dynamically adjusting a flight path and generating a distribution path by using an AI path planning technology in combination with real-time situation awareness and obstacle detection analysis; and after arriving at a specified receiving point, automatically calibrating the landing coordinates, and synchronizing the material state, handover information and environmental data to a medical team and a central scheduling system through a low-delay wireless transmission mode according to the distribution path. According to the technical scheme provided by the invention, the efficiency and reliability of offshore material distribution are greatly improved.

The invention discloses an intelligent emergency and management and control method for a stadium, and belongs to the technical field of stadium safety management and control. According to the method, video stream data are collected through a monitoring camera array, and coverage areas of all cameras in the array have 15%-20% of overlapping areas; inputting the data into a pre-trained people density identification model to generate a real-time people flow distribution thermodynamic diagram; the retention index of each partition is calculated, and an emergency evacuation mode is activated in combination with a preset safety threshold value; in the emergency mode, the infrared sensor group monitors the instantaneous pedestrian flow speed, and an optimal evacuation path is generated by a dynamic path planning algorithm; the path is converted into a control instruction to drive an electromagnetic access control gate, emergency lighting and an electronic guide identifier, feedback data triggers a path re-calculation mechanism, and a two-dimensional code scanning device counts the number of evacuated people so as to start manual re-checking. Real-time situation awareness, dynamic risk assessment and intelligent evacuation guidance of the stadium are achieved, emergency response and evacuation efficiency is effectively improved, and crowd safety is guaranteed.

The invention discloses an intelligent ship situation awareness prediction method and system, and relates to the technical field of intelligent ship situation awareness and trajectory prediction, and the method comprises the steps: carrying out the time-space synchronization of collected operation environment data of a ship through a timestamp alignment method, and generating a ship situation data set; based on the ship situation data set, the course angle change rate and the Euclidean distance of the obstacle relative to the ship are extracted, and weighted fusion is carried out on the dynamic collision probability and the approaching time of the obstacle to obtain an obstacle threat degree index; inputting the obstacle threat degree index into a ship trajectory prediction model, and outputting the minimum meeting distance between the ship and each obstacle; and carrying out risk grade division on the minimum encounter distance according to an international maritime collision avoidance rule, outputting a graded early warning signal, and formulating a navigation strategy to execute obstacle avoidance and route adjustment. According to the method, the technical problems of risk perception lag, single and rough avoidance action and insufficient trajectory prediction accuracy in the prior art are effectively solved.

The invention discloses a space-time spectrum combined super-resolution reconstruction method based on a giant remote sensing satellite group. The method comprises the following steps: acquiring time series, multi-view and multi-spectral data of the same area from a plurality of heterogeneous satellites, and performing radiometric calibration and atmospheric correction; sub-pixel-level alignment of the multi-source data is realized by adopting a joint registration model; extracting time change features by using three-dimensional convolution, extracting space structure and texture features by using two-dimensional convolution, and extracting and reducing the dimension of spectral features by using one-dimensional convolution; performing adaptive weighted fusion on time, space and spectral features through an attention mechanism to generate a joint feature tensor; and carrying out super-resolution reconstruction to obtain a target image with high spatial resolution, high time resolution and high spectral fidelity. The method gives consideration to both resolution improvement and spectrum authenticity, and is suitable for high-precision remote sensing application scenes such as fine urban mapping, agricultural monitoring, ecological environment assessment, disaster emergency and battlefield situation awareness, remote reconnaissance, target change detection and damage assessment.

The invention provides an urban water affair situation awareness management method and system, and relates to the technical field of urban water affair management, and the method comprises the steps: obtaining multi-source heterogeneous data; performing preprocessing, feature extraction and fusion on the multi-source heterogeneous data to obtain a comprehensive analysis feature set; inputting the comprehensive analysis feature set into a situation assessment model to determine the situation level of the urban water affair; inputting the comprehensive analysis feature set into a time sequence prediction model, and predicting to obtain a situation change trend of a future time period; based on the comprehensive analysis feature set, the situation level and the situation change trend, performing comprehensive assessment by using a preset risk assessment index system, and generating a risk assessment result; based on the situation level, the situation change trend and the risk assessment result, a preset expert knowledge base is called for matching and reasoning, and a candidate disposal strategy set is generated; and evaluating strategies in the candidate disposal strategy set, and outputting an optimal disposal strategy. According to the invention, the management level of urban water affairs is improved.

The invention relates to the field of environment monitoring, discloses an ecological protection red line holographic dynamic monitoring and early warning method and system, and aims to solve the defects of an existing monitoring and early warning mechanism in the aspects of timeliness, data integration and intelligent analysis. According to the method and the system, multi-source heterogeneous data are acquired and fused, a space bottom line digital model is constructed, space-time situation awareness analysis and change abnormity identification are performed, risk assessment early warning triggering is performed, and early warning issuing and processing feedback are managed. According to the invention, high-precision holographic acquisition and fusion of multi-source data can be realized, the accuracy and timeliness of change abnormity identification are improved, passive early warning is changed into active early warning, a monitoring disposal closed loop is formed, supervision intelligence and treatment efficiency are improved, and ecological safety is guaranteed.

The invention discloses a gas pipe network maintenance method and system based on a large language model, and electronic equipment, and aims to solve the ubiquitous problems that data fusion is difficult, fault analysis depends on manpower, the operation and maintenance automation degree is low and the like in a gas pipe network. The method comprises the following steps: firstly, acquiring and processing multi-source heterogeneous data of the gas pipe network, then vectorizing the multi-source heterogeneous data to construct a unified multi-modal database, when monitoring that the data is abnormal, adopting retrieval enhancement to generate a large language model, retrieving associated information from the database, performing fault root cause intelligent reasoning, and generating an analysis report, and finally, automatically generating a structured maintenance instruction according to the report, and docking with an operation and maintenance system to form a whole-process automatic closed loop from monitoring to disposal. The system and the electronic equipment are used for executing the method. The method has the advantages that a data island is broken through, accurate situation awareness is achieved, the efficiency and accuracy of fault diagnosis are remarkably improved, and the effect of reducing the operation cost and the safety risk is remarkable.

The invention relates to an emergency management disaster dynamic early-warning scheduling system based on multi-source data fusion, which comprises a multi-source data acquisition module, a physical sensor-containing data acquisition sub-module, a network data acquisition sub-module, a multi-source data integration module, a multi-source data management module and a multi-source data management module, the system comprises a preprocessing sub-module, an annotation sub-module, a fusion processing sub-module, a dynamic early warning generation module and a precise resource scheduling module. Data are collected through a meteorological satellite, a ground monitoring station, an Internet of Things sensor and a network channel, and disaster situation sensing information is formed through cleaning, labeling and fusion processing. Based on a disaster evolution model of a physical and machine learning algorithm, multi-level time period early warning information is generated in real time in combination with historical data. According to the GIS and the emergency resource database, an improved algorithm is used for planning a resource allocation scheme, efficient early warning of disasters and accurate resource scheduling can be achieved, and the emergency management level is improved.

According to the method, multi-modal data such as voice, images, texts, GIS and Internet of Things sensing are fused, and deep neural network prediction, reinforcement learning scheduling optimization and rule engine compliance check are combined; the intelligent vehicle and material dispatching method and system are applied to multiple scenes such as emergency material storage depots, fire-fighting emergency command, urban disaster response, traffic accidents and medical first aid. The system is interconnected and intercommunicated with an intelligent emergency material storage cloud platform, a city brain, Beidou navigation, intelligent fire fighting and other external platforms, and supports one-key issuing, path optimization, traffic signal linkage and whole-course return closed loop. Compared with the prior art, the method has the advantages that unification of multi-modal situation awareness, data-driven optimal scheduling and expert knowledge constraints is realized, the response time is remarkably shortened, the resource utilization rate is improved, and compliance safety is ensured.

The invention discloses a method for checking network asset information of a power situation awareness system based on a knowledge graph. The method comprises the following steps: collecting power secondary equipment data from multiple sources and carrying out data preprocessing; based on a plurality of preset field statistics key indexes, communication messages are selected from the communication data, a communication group is constructed, and typical samples are extracted; by taking the equipment information as a center, constructing nodes and relationships of the power secondary equipment knowledge graph on the ontology layer, and importing the power secondary equipment data after data preprocessing into an instance layer of the knowledge graph according to the structure of the ontology layer; checking nodes and relationships in the knowledge graph according to rules, and checking and complementing nodes, edges and attributes in the knowledge graph based on deep learning; and feeding back results of the rule verification and the deep learning verification to the knowledge graph, and updating the knowledge graph. According to the method, data checking can be effectively carried out, the information accuracy of the data is improved, and the asset data quality is improved.

The invention discloses an electric power production data private network security situation awareness system and method. The method comprises the following steps: 1) acquiring security data of an electric power production data private network in real time; 2) standardizing the acquired data; 3) constructing a power grid entity relation graph, and fusing the standardized data obtained in the step 2) into the graph; 4) constructing an attack graph, and associating the attack graph with the power grid equipment entity relationship graph; 5) combining the power grid entity relation graph with the attack graph, performing deep correlation analysis, and identifying potential attacks in the power grid; 6) generating a grade risk alarm according to an identification result in the step 5), and triggering corresponding countermeasures; and 7) simulating the defense measures in the step 6), and optimizing and adjusting the defense countermeasures according to a feedback result. According to the method, the multi-source data is combined with the dynamic knowledge graph technology, real-time correlation analysis of the power grid entity relationship and the attack behavior is supported, and the recognition capability of novel attacks is remarkably improved.

The invention relates to the technical field of computer vision and artificial intelligence, and discloses a computer vision-based hundred-million-pixel panoramic situation awareness method and system, and the method comprises the steps: synchronously collecting original local field-of-view images through multiple cameras; independently executing target detection and semantic segmentation in an original image domain; constructing local-to-global coordinate mapping based on the calibration parameters; projecting the target features to a global coordinate system and detecting a cross-view-field target; extracting multi-view-field texture, edge and color features, and performing weighted fusion according to a visible area; according to the method, through identification preposition and semantic level feature fusion, geometric splicing distortion is avoided, the cross-boundary identification precision is improved, the calculation load is reduced, target continuous tracking and situation real-time output in a high-density scene are realized, and the method has high engineering suitability and industrial popularization value.

The invention relates to the technical field of load monitoring, in particular to a station electricity load feature recognition system based on operation situation awareness, which comprises a situation extraction module, a power focusing module, a sudden change judgment module, an anomaly recognition module and a state output module. According to the method, the behavior boundary definition between the loads is enhanced through a difference clustering mode based on the power linkage relation, the anomaly identification independence under the complex load coupling condition is improved, the high-consistency feature reference is constructed by using the operation stability index, the identification interference caused by short-time fluctuation is effectively eliminated, the misjudgment and missed judgment conditions are avoided, and the reliability of the system is improved. The reliability and adaptability of sudden load behavior recognition are guaranteed by combining tolerance period judgment of a sudden change sample interval and peeling off accidental disturbance factors, structured filing of an abnormal recognition result can be achieved by establishing a mapping list, a corresponding relation between a control path and an abnormal behavior is constructed in an auxiliary mode, and the reliability and adaptability of sudden load behavior recognition are improved. And the interpretability and traceability of station electricity load identification are enhanced.

The invention provides a multi-target track tracking method based on dynamic anti-noise clustering and bidirectional verification matching. The method comprises the following steps: firstly, collecting heterogeneous data of different sensors in real time; secondly, performing dynamic space-time registration processing on the heterogeneous data to obtain space-time aligned multi-sensor measurement data; then, noise filtering and target preliminary screening are carried out on the measurement data, multi-modal data fusion is realized in combination with sensor confidence coefficient weights, and multi-target position information after multi-sensor fusion is obtained; constructing and solving a forward space-time enhancement cost matrix and a reverse space-time enhancement cost matrix, carrying out bidirectional verification on target points and a track, and carrying out track matching on obtained multi-target position information and an existing track; and finally, predicting a target state through extended Kalman filtering, and dynamically optimizing the process through a closed-loop feedback mechanism to obtain a stable track and complete multi-target track tracking. According to the method, the multi-target continuous stable tracking capability can be obviously improved, and reliable technical support is provided for sea area situation awareness.

The invention discloses an aeronautical medical rescue assessment method and system based on an MR technology, and relates to the technical field of aeronautical medical rescue. Comprising a multi-dimensional data acquisition module, a fusion data display module, a task real-time evaluation module, a task automatic adjustment module, a remote cooperation adjustment module and a task record evaluation module. According to the scheme, data such as vital signs of a patient, the flight state of an aircraft, rescue worker operation and medical resource configuration are comprehensively integrated through multi-dimensional data acquisition, a comprehensive data source ensures that a decision maker can obtain comprehensive, real-time and accurate situation awareness, and information one-sidedness caused by a single data source in the prior art is avoided; the data are cleaned and fused through a Kalman filter, and the precision and timeliness of the data are remarkably improved.

The invention provides a situation awareness traffic control method and system based on a time sequence large model, and a medium, and belongs to the technical field of traffic control. The method comprises the following steps: processing a road monitoring video stream through a time sequence large model to determine a situation awareness data set corresponding to a first preset time period; and analyzing a sensing data change result of the situation awareness data set based on the historical situation awareness time sequence data, and matching each associated road section corresponding to the monitored road section of the first monitoring equipment so as to construct a road network space of a corresponding associated type. And according to the similar road correlation characteristics corresponding to the road network space, matching a correlation weight set corresponding to the multi-source data type so as to perform weighted fusion on the multi-source data of the monitoring equipment group in the road network space, and obtaining a road control grade judgment index corresponding to the road network space. And determining a corresponding traffic control strategy according to each road control grade judgment index and a preset situation awareness control data set, and sending each traffic control strategy to a traffic control management terminal.

The invention relates to the technical field of data visualization, and discloses a visual analysis method and system for security and protection monitoring data, and the method comprises the steps: carrying out the asynchronous collection of a video monitoring data stream, an infrared sensor data stream, an access control system data stream and an audio collection data stream in a security and protection monitoring system, and obtaining a multi-source asynchronous data stream set; generating a potential causal event pair set based on the multi-source asynchronous data stream set, and creating a time window allocation table according to the potential causal event pair set; performing causal probability calculation on the multi-source asynchronous data stream set to obtain a multi-dimensional causal probability propagation matrix; and asynchronous time delay compensation and causal chain visualization are performed on the multi-dimensional causal probability propagation matrix based on the time window allocation table to obtain a dynamic causal relationship graph, so that visual dynamic display of the causal chain of the asynchronous data stream is realized, and brand new monitoring situation awareness experience is provided for security personnel.

The invention provides a major accident-oriented rescue resource demand quantity prediction and dynamic supply method, which relates to the technical field of emergency rescue, and comprises the following steps: acquiring accident site multi-source data information for situation awareness, performing long-term prediction on rescue demand quantity based on a symplectic geometric structure and Hamiltonian dynamic mapping, and determining the rescue demand quantity; and the supply network is dynamically optimized by using the Riemannian curvature flow to generate the rescue resource supply scheduling scheme, so that the resource demand can be accurately predicted, the supply path can be optimized, the rescue efficiency can be improved, the resource waste can be reduced, and the accurate delivery of the rescue resources can be realized.

The invention discloses an asynchronous motor energy-saving monitoring and dynamic early warning method based on situation awareness, and relates to the technical field of asynchronous motor energy-saving monitoring. The method comprises the steps that S1, multi-source heterogeneous data are synchronously collected, power supply parameters and operation parameters are obtained in real time, and a frequency conversion working condition self-adaptive deviation value and a current stability coefficient are obtained; s2, generating four-dimensional situation factors including an overtemperature danger value, a current stability coefficient, a voltage stability coefficient and a frequency deviation value; s3, constructing a fourth-order transmission network, and generating a primary early warning signal based on the path mark combination; s4, calculating the motor efficiency in real time, triggering an efficiency abnormity mark, fusing the efficiency abnormity mark with the primary early warning signal, and reconstructing a final early warning instruction; and S5, executing hierarchical control according to the final early warning instruction. Through multi-parameter coupling and dynamic early warning transmission, the early-stage capture capability of the composite fault is improved, the early warning response delay is shortened, energy-saving optimization and fault protection of the motor are realized, and the method has a remarkable practical value.

The invention provides an unmanned aerial vehicle cooperative confrontation control method, device and equipment and a storage medium, and the method comprises the steps: obtaining the situation information of a multi-unmanned aerial vehicle system, and carrying out the preprocessing of the situation information through a situation awareness module, and generating a standardized state feature; the hierarchical control strategy comprises upper-layer option decision and bottom-layer multi-agent cooperative control, guiding an unmanned aerial vehicle group to execute a task according to the hierarchical control strategy, executing an approaching maneuvering and surrounding strategy by adopting a priori knowledge rule in an initial stage of the task, and realizing upper-layer option selection through an Option-Critic algorithm after surrounding is completed, bottom-layer multi-agent cooperative control is realized through a QMix + DQN algorithm; the feedback data of task execution of the unmanned aerial vehicle is obtained, the feedback data is processed through a hierarchical reward mechanism to optimize a decision strategy and update network parameters, and option switching conditions are dynamically adjusted to form closed-loop optimization, so that the problem that the unmanned aerial vehicle cannot effectively process complex decisions of a high-dimensional state space and a large-scale action space is solved.

The invention discloses an integrated circuit heat distribution optimization method driven by fusion situation awareness, and particularly relates to the technical field of integrated circuit heat distribution optimization, which comprises the following steps: acquiring a behavior fragment set in an integrated circuit, performing regulation and control trigger sequence adjustment and regulation and control chain atlas construction operation, constructing a regulation and control chain atlas, and establishing a regulation and control chain atlas; performing identification and extraction operation of path traversal on the regulation and control chain atlas, and performing mapping to generate a disturbance input variable set; extracting data in the disturbance input variable set, performing normalization processing and time sequence reconfiguration operation, and constructing a disturbance response track; a regulation and control path structure caused by a temperature control behavior in an integrated circuit is extracted, and a stability identification model is constructed based on path disturbance propagation characteristics and behavior feedback offset, so that dynamic judgment and regulation and control structure optimization of a potential thermal feedback chain in a multi-core environment are realized, and task scheduling imbalance caused by path evolution accumulation is avoided.