680 results about "Situation awareness" patented technology

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 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 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 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.

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 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 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 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.

The invention provides a highway situation awareness method and system, and the method comprises the following steps: collecting camera video stream data to extract traffic flow data and parking data, and inputting the traffic flow data into a graph neural network to construct a traffic flow propagation model; constructing an abnormal event influence evaluation model based on the recurrent neural network and the long-short-term memory network; and through an abnormal event influence evaluation model, outputting influence range data including an affected road segment set and predicted abnormal recovery time, integrating the data and outputting the data to a visual interface. According to the method, the traffic flow state of the expressway is accurately evaluated by collecting, processing and analyzing the video stream data of the roadside camera, and a reliable situation awareness model is constructed in combination with toll station entrance and exit data, portal snapshot data and the like, so that real-time and accurate monitoring and prediction of the traffic condition of the expressway are realized, powerful decision support is provided for traffic management, and the traffic flow state of the expressway is accurately evaluated. And the operation efficiency of the expressway is improved.

The invention provides a smart city emergency response method and system combined with artificial intelligence, and the method comprises the steps: firstly carrying out the situation awareness of a city emergency event, generating an event situation map containing spatial-temporal evolution characteristics and influence levels, calling a pre-trained strategy migration model to match historical cases, and outputting a preliminary response strategy set; and dynamically adapting and adjusting the preliminary response strategy set based on the situation map real-time updating characteristics, generating an adaptive response strategy set, carrying out cooperative scheduling analysis on the adaptive response strategy set and the urban emergency resource network, generating a target cooperative scheme matched with resource supply and strategy requirements, and outputting an emergency response instruction containing an execution priority according to the target cooperative scheme. And the information is sent to a corresponding node to trigger a multi-main-body linkage response, so that the scientificity and the high efficiency of the emergency response of the smart city are improved.

The invention provides a network situation awareness method based on a heterogeneous graph neural network, and the method comprises the steps: carrying out the cleaning and standardization of structured data, constructing a unified semantic space based on topic prior, and forming a robust sample representation through employing a cross-modal alignment and attention fusion method; time, entities and link relations of different platform data are aligned, a'trigger-argument-role 'triple is extracted, and explicit / implicit propagation evidences are fused to construct a directed propagation graph; candidate paths are obtained through causal consistency, key links are screened through multi-path reliability and significance test, and provenance positioning and cross-platform association are achieved. According to the invention, network situation awareness with a localizable origin, a traceable path and an explainable result can be realized in a multi-source heterogeneous and high-noise scene; robustness is improved through significance control and multi-path fusion, decision reliability is improved through calibration and uncertainty propagation, and the method can be applied to scenes such as public safety monitoring, public opinion research and judgment and compliance risk control.

The invention relates to the technical field of power optical cable monitoring, and discloses a power optical cable operation situation sensing method, which comprises the steps of collecting temperature, strain, vibration and scattering signal data along an optical cable in real time through a distributed optical fiber sensor, and constructing a multi-source heterogeneous data set in combination with power grid operation data and environment monitoring data; carrying out denoising, abnormal value elimination and missing value filling processing on the data by adopting a time sequence synchronization algorithm, and extracting a temperature rise rate and a strain fluctuation frequency to generate a multi-dimensional feature matrix; by dynamically fusing multi-source data, situation awareness indexes such as a thermal stability score and an overload risk index are generated; an LSTM algorithm and a dynamic threshold model are combined to predict the operation risk level of the optical cable in the next 24 hours, a power grid dispatching system is linked through visual means such as a situation map and three-dimensional simulation, and intelligent alarm and operation and maintenance suggestions are automatically issued. The fault identification precision can be improved, the operation and maintenance cost can be reduced, and preventive maintenance can be realized.

The invention relates to the technical field of flight ability evaluation, in particular to a pilot emergency processing ability evaluation method and system fused with physiological signals. Initial ability indexes are extracted based on literature research and physical examination standards, and physiological data of a pilot in a normal state and a simulated emergency state are collected; the method comprises the following steps: extracting potential ability indexes through dimensionality reduction and denoising of a component analysis algorithm and factor analysis (FA), and screening remarkably changing physiological indexes (plt; 0.05), and performing correlation combination with the initial index (a correlation coefficient gt; 0.8) constructing an emergency capability evaluation index system including situation awareness, decision making, communication, pressure management, team cooperation, reaction time, strain and flexibility; the pilot emergency ability assessment method and system are constructed through multi-source physiological signal fusion and dynamic modeling, accurate quantitative analysis and personalized training guidance are achieved, and civil aviation safety specifications are adapted.

The invention discloses a multi-agent intelligent way-finding and real-time decision-making method based on MADDPG, and the method achieves the autonomous collaborative decision-making of multi-target optimization through the technical links of environment perception, dynamic obstacle intention prediction, single-agent pre-training, strategy distillation and multi-agent collaborative training. The method comprises the following core steps: predicting a dynamic obstacle intention based on a long short-term memory network of an attention mechanism; constructing a threat field model to quantify the environmental risk; forming basic obstacle avoidance and path optimization experience through single agent pre-training; empirical migration is realized by utilizing strategy distillation so as to shorten a multi-agent training period; and multi-agent cooperative training is completed based on a centralized training-distributed execution architecture and an MADDPG algorithm. According to the method, the path length, the energy consumption, the safety and the task completion rate can be considered, the survivability, the cooperation efficiency and the task adaptability of the unmanned aerial vehicle formation in the complex dynamic environment are remarkably improved, and the method can be widely applied to different-scale formation and multiple task scenes such as reconnaissance, communication relay and situation awareness.

The invention relates to the technical field of low-altitude equipment, in particular to a low-altitude resource intelligent scheduling method and system based on deep learning, and the method comprises the steps: collecting the real-time state and network load data of a low-altitude flight equipment group, and constructing a dynamic operation data set; generating an operation mode feature set through multi-dimensional airspace situation awareness and analysis, and performing sparse clustering division based on the feature set to form a network resource demand priority mapping table; traversing the mapping table to dynamically calculate the resource demand, determining a multi-dimensional weight coefficient, and performing high-dimensional feature dimension reduction and optimization through a mixed integer nonlinear programming solver to obtain a resource demand feature vector; constructing a resource scheduling strategy optimization model by adopting a deep reinforcement learning algorithm based on the vector; inputting real-time data into the model to execute a resource scheduling decision, and outputting a dynamic allocation strategy; simulation deduction and compliance verification are carried out on the strategy in the digital twin simulation platform, and cooperative intelligent scheduling of communication, calculation and spectrum resources is achieved.

The invention discloses a sailing ship safety situation awareness system, and belongs to the technical field of data analysis. The method is used for solving the technical problem that in an existing scheme, multi-dimensional data analysis and timely and reliable early warning cannot be carried out on complex sea conditions, and consequently the coping capacity of the complex sea conditions is weak. Through the whole process of biological anchoring anomaly suppression, data fusion and dynamic feature extraction, the limitation that traditional ship perception depends on a physical sensor can be broken through, high-precision perception of environmental risks and ship states under complex sea conditions is achieved, and compared with a traditional method, the abnormal sea conditions can be recognized in advance, and the method has the advantages of being high in practicability. The robustness of the data and the detection rate of small probability and high influence events can be effectively improved; cross-scale modeling is realized through deep coupling of a microscopic physical mechanism and macroscopic data features; the constructed multi-dimensional risk assessment matrix not only can output a risk value, but also can provide a risk propagation path, and can effectively improve the risk assessment precision of the ship under a complex sea condition.

The invention provides a digital twin smart park security and protection management system based on artificial intelligence, and relates to the field of security and protection management, and the system comprises a multi-mode sensing network which is used for collecting park environment data through a multi-mode sensor network, and carrying out the preprocessing of the data, and obtaining an environment data set; the digital twin modeling module is used for constructing a park three-dimensional virtual model based on the environment data set; the semantic alignment module is used for defining a dynamic security semantic rule based on the three-dimensional virtual model and carrying out semantic alignment of multi-modal features on the environment data set; the threat assessment module is used for performing threat assessment on the aligned semantics based on a knowledge graph and a Bayesian network; and the resource scheduling execution end is used for generating a disposal plan according to the evaluation result and realizing security response through an execution terminal. The method is used for solving the problems of information isolated island, response lag, high false alarm rate, fuzzy situation awareness and the like of a park security system in the prior art.

The invention discloses a situation awareness and linkage disposal system based on dynamic threat modeling driving, and the system comprises a security data collection and standardization module which is used for collecting security data of a network, a host and an application layer, and carrying out the standardization processing; the dynamic threat modeling module is used for constructing a dynamic threat model; the threat agent modeling and simulation module is used for modeling each node in the threat attack graph into a threat agent; the parameter optimization module is used for globally optimizing parameters and strategy weights of the Monte Carlo tree search network by using a seagull algorithm; the linkage processing decision module is used for configuring a Monte Carlo tree search network based on the optimal parameter group; the safety equipment scheduling module is used for analyzing the optimal linkage disposal decision scheme; and the disposal effect evaluation and feedback module is used for performing multi-dimensional analysis on the disposal feedback data set. According to the invention, accurate situation awareness and linkage processing are provided for the user.

The invention relates to the technical field of environment perception, and discloses a fusion perception system and method based on a multispectral sensor. According to the method, asynchronous heterogeneous environment data streams collected by a visible light sensor, a thermal imaging sensor, a millimeter-wave radar and a laser radar are obtained, environment information of multispectral bands is covered, comprehensive capture of target appearance, temperature, motion and geometric characteristics is achieved, space-time registration operation is executed on asynchronous heterogeneous data, space-time deviation between the sensors is eliminated, and the accuracy of target tracking is improved. And a multi-source sensing data cube with time-space synchronization is generated, and a consistent data basis is provided for subsequent fusion. Based on the data cube, a hierarchical feature fusion architecture is adopted to extract cross-spectrum joint features, a multi-dimensional feature tensor is generated, and internal association and complementary information of multi-source data is deeply mined. The multi-dimensional feature tensor is processed through a dynamic weighting decision mechanism, a three-dimensional situation awareness map of an environment target is output, environment dynamic change and sensor data fluctuation are adapted, and the reliability of environment awareness in a complex scene is improved.

The invention discloses a multi-satellite layered cooperative task planning method for multi-target situation awareness, and belongs to the field of aerospace. The implementation method comprises the following steps: defining a multi-satellite collaborative planning problem of a multi-target sensing task as a quintuple, and determining satellite resources and time constraint conditions in an inter-satellite collaborative planning problem of the multi-target sensing task; constructing a multi-target sensing task multi-satellite collaborative planning model, describing elements, related constraints and simplified conditions of a multi-target sensing task multi-satellite collaborative planning problem, analyzing and decoupling multiple coupling complex constraints, and designing a layered bidding and tendering collaborative mechanism; on the basis, a multi-target situation awareness hierarchical collaborative task planning method is constructed, a two-dimensional coding structure related to task types is constructed according to multi-dynamic target orbit determination requirements, a mixed profit value function related to a double-satellite orbit determination observation configuration, attitude maneuver cost, double-satellite collaborative rewards and the like is designed, and multi-satellite collaboration is guided. And a multi-satellite collaborative observation sequence is obtained, and multi-satellite layered collaborative task planning is realized.