469 results about "Situation awareness" patented technology

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.

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 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 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 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 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.

The invention provides a method for systematically monitoring aircraft pilot and air traffic control (ATC) radio communications to detect predefined aviation-specific keywords indicative of operational conditions affecting flight safety or efficiency, such as turbulence, icing, or visibility constraints. Detected keywords and semantic equivalents from voice transmissions are transformed into structured data formats. These structured data are correlated with Automatic Dependent Surveillance-Broadcast (ADS-B) data, including precise aircraft position, altitude, heading, and speed. Leveraging this integrated data, the system generates automated, quantitative visualizations, such as condition-specific heatmaps or other graphical representations, illustrating spatial distributions of reported in-flight phenomena like turbulence, icing, or hazardous conditions. These visualizations enable air traffic controllers, pilots, and aviation stakeholders to rapidly assess and disseminate real-time observations and historical patterns of flight hazards, thereby significantly enhancing situational awareness, operational safety, and decision-making effectiveness without manual input or subjective interpretation, improving overall aviation operational efficiency and safety.

The invention discloses an intelligent project implementation management system and method based on multi-source perception fusion, and aims to solve the problems of information isolated island, response lag, experience dependence, extensive management and control and the like in an engineering project. According to the system, multi-source data acquisition is carried out through an Internet of Things sensor, a video monitor, a mobile terminal, a BIM / GIS, a project management platform and the like, and after space-time alignment and standardization processing are carried out, a unified situation awareness view is constructed by adopting a hierarchical fusion strategy; performing real-time intelligent evaluation and prediction on progress, quality, safety, cost and resources based on the fused data; a rectification, scheduling or early warning instruction is automatically generated, and a sensing-analysis-decision-execution closed loop is realized by pushing and linking a field execution system through multiple channels; according to the method, the safety prevention and control, quality control and progress performance capabilities are remarkably improved, the accident rate and cost are reduced, and project management is promoted to be transformed and upgraded to a refined, active and self-evolution direction.

The invention discloses a heterogeneous feature fusion-based multi-task traffic situation cognitive calculation method, and belongs to the field of intelligent traffic. The method specifically comprises the following steps: firstly, preprocessing multi-source traffic observation data to obtain normalized traffic flow, OD raster data and traffic state data; then, for a current frame, carrying out dynamic space-time convolution and diffusion diagram convolution on traffic flow and OD raster data to obtain node-level space-time features; meanwhile, carrying out feature extraction on the traffic state data by utilizing 3D convolution to obtain grid-level spatial-temporal features; and then, inputting the features and the common features into a heterogeneous cross attention fusion module to obtain a unified fusion feature h of the current frame, performing multi-task prediction, and respectively obtaining flow, OD and traffic state prediction results of future K steps. And finally, inputting the prediction result of each task and a true value to carry out multi-task loss calculation, carrying out parameter updating and completing model training. According to the invention, multi-task prediction can be realized by using multiple prediction heads.

The invention relates to the technical field of airspace control, and discloses a low-altitude airspace precise control system and method for a low-altitude aircraft, and the system comprises an airspace digital twinning construction module, a multi-dimensional airspace distribution engine and a carbon constraint flight control module. When a low-altitude digital twin airspace is constructed, the system can solve the problem of signal distortion in airspace situation awareness by deploying a multi-source environmental interference filtering algorithm, analyzing waveform characteristics of sensor data in real time and dynamically identifying and eliminating the influence of electromagnetic interference and meteorological distortion on a monitoring signal; the authenticity and reliability of environment dynamic data acquisition are guaranteed, the construction precision of a digital twin model is improved, the accuracy of an airspace management and control decision is further guaranteed, and when dynamic monitoring of the flight path of the aircraft is implemented, a three-dimensional attitude real-time feedback mechanism is established, the space vector included angle between the actual course of the aircraft and a planned path is continuously calculated, and the real-time dynamic monitoring of the flight path of the aircraft is realized. Therefore, the system can sense the pose offset state in time, and the robust control capability of the low-altitude dense traffic flow is enhanced.

The invention belongs to the technical field of artificial intelligence and computer vision, particularly relates to a visual large model emergency management and control system for panoramic situation awareness, and aims to solve the problems of weak multi-source visual data fusion, low risk deduction precision, lagging command decision and the like in urban public safety. The system comprises a global visual perception layer, a space-time alignment engine, a visual large model reasoning center, a dynamic situation map generator, a multi-granularity emergency decision module and an instruction distribution feedback unit. By constructing a high-precision space-time grid and a visual large model for joint reasoning, conversion from pixels to semantics is realized, and a time sequence evolution knowledge graph is generated and dynamically updated; and in combination with a hierarchical response mechanism and a cloud edge collaborative architecture, automatic disposal and man-machine collaborative decision making are supported, and the emergency response efficiency and the system robustness are improved.

The invention discloses a signal reconnaissance, positioning and interference method based on deep learning, and belongs to the technical field of electronic countermeasures. The method comprises the following steps: deploying distributed reconnaissance nodes to obtain broadband electromagnetic signals; generating a spectrogram through time-frequency transformation, and identifying a fixed frequency / frequency hopping signal based on a deep learning model; fusing multi-node reconnaissance results to realize target situation awareness; the optimal positioning cluster is selected through dynamic networking, and the target position is calculated in combination with the arrival angle and time difference information; generating an optimal interference strategy based on the target situation and the position trajectory; networking cooperative interference is implemented; behavior parameters are collected in real time, the interference effect is evaluated through a machine learning model, and the strategy is dynamically adjusted. According to the method, the technical effects of high-efficiency and self-adaptive signal reconnaissance, positioning and interference in a complex electromagnetic environment are achieved.

The invention relates to the technical field of risk management and control, in particular to a distributed security risk management and control platform fusing situation awareness and an intelligent contract, and the platform comprises an acquisition module, a situation awareness module, a prediction module, an identification module, and an intelligent contract execution module. Multi-dimensional dynamic parameters related to a port cold chain are comprehensively collected and calculated, temperature difference fluctuation is used for reflecting environment stability, frequency jitter is used for reflecting equipment operation states, queue density and emergency brake behaviors are used for reflecting operation intensity and operation risks, trend judgment is carried out based on different situation index occurrence frequencies in a prediction module, and a prediction result is obtained. And in combination with the time continuity and the space aggregation of the abnormal nodes, the transportation speed is finally automatically adjusted through the intelligent contract, so that the problems of risk early warning lagging and difficulty in effective implementation of field control caused by response delay and insufficient prediction timeliness due to dependence on centralized processing are effectively solved.

The invention relates to the technical field of intelligent traffic and automatic driving, and discloses a driving takeover condition identification method and system based on multivariate situation awareness, which is used for solving the problem that a fixed safety threshold value is not representative any more during driving takeover condition identification, and comprises the following steps: obtaining a current human-vehicle-ring safety situation index; the method comprises the following steps: acquiring safety influence parameters, evaluating to obtain a multi-dimensional situation threshold correction index, judging whether multi-stage safety threshold adjustment is needed or not, if so, adjusting an original multi-stage safety threshold to obtain an actual multi-stage safety threshold, and generating a takeover instruction. And the response behavior data of the driver to the takeover instruction is obtained, the actual multi-level safety threshold is adjusted again according to the response behavior data, the multi-level safety threshold after feedback adjustment is obtained, and the safety of intelligent traffic in a complex environment is effectively improved.

The invention belongs to the technical field of bulk cargo wharf operation and maintenance situation awareness, and relates to a bulk cargo wharf operation and maintenance situation awareness method and system. The residual life of the equipment is calculated through the residual life probability distribution of the equipment predicted by the constructed residual life probability distribution prediction model, and the failure rate of the equipment is predicted through the constructed situation awareness model according to the health degree index of the equipment and the residual life of the equipment, so that equipment alarm signals of different grades are obtained. An auxiliary management scheme is generated and executed according to the equipment alarm signal, the equipment user maintenance time is reasonably planned, and the equipment utilization rate and the field production efficiency are improved. Health state monitoring and operation and maintenance situation awareness of the bulk cargo wharf are achieved, the equipment utilization rate and the field production efficiency are improved, and the turnover rate and turnover efficiency of wharf cargos are improved.

The invention provides a vehicle remote control driving system and method integrating AR virtual path projection, and relates to the technical field of vehicle remote control. The system constructs a high-precision three-dimensional environment model through cooperative work of an environment sensing module and a data processing and decision module; the AR path generation unit generates a collision-free virtual path and a vehicle dynamic rehearsal picture, and the collision-free virtual path and the vehicle dynamic rehearsal picture are visually presented in an augmented reality mode through the remote control terminal outside the vehicle, so that a driver can obtain comprehensive situation awareness beyond a physical visual field outside the vehicle; and after the virtual path is confirmed or adjusted in combination with the interaction control, the vehicle control strategy unit automatically generates a cooperative control command adapting to the road condition, and an execution mechanism is driven to perform accurate execution. According to the system, the safety and operation confidence of remote control driving are improved, the blind area collision risk is effectively avoided, the trafficability of the vehicle in a complex environment is enhanced, meanwhile, the operation difficulty is greatly reduced through AR visual interaction, and intelligent and user-friendly vehicle exterior remote control driving experience is achieved.

The invention relates to the technical field of data processing, in particular to an intelligent community data processing system based on situation awareness, which comprises an information acquisition module used for acquiring external accident information and generating an external accident report; the video module is used for acquiring video data in the community and generating a scene report; the analysis module is used for calling the external accident report and the scene report for comparison and generating a risk report; the preset scheme module is used for creating and storing a processing scheme; the automatic auditing module is used for matching the risk report with a processing scheme in the preset scheme module, generating a risk processing scheme if matching succeeds, and directly sending the risk report to the manual auditing module if matching fails; and the manual auditing module is used for auditing the risk processing scheme, analyzing the risk report, creating a corresponding processing scheme in the preset scheme module, and generating the risk processing scheme. The system can recognize some potential risks in the community in advance, and reduces the accident rate.

The invention relates to the technical field of operation management, in particular to an airport intelligent global situation awareness and intelligence analysis management method and system. The method comprises the following steps: capturing corresponding global operation data in real time to generate an airport global original data set; performing airport operation map construction and pressure elasticity test analysis on the airport global original data set to generate an airport operation global pressure assessment result; performing airport operation pressure planning and global situation awareness comparative analysis on the basis of an airport operation global pressure assessment result to generate an airport actual operation global situation awareness model; carrying out risk early warning information analysis and airport omnibearing comparison based on the airport actual operation global situation awareness model, and generating an airport global operation process redisk report containing deviation analysis; and performing operation optimization and improvement analysis based on the airport global operation process recovery report to obtain an airport operation global optimization and improvement management scheme. The airport operation efficiency can be remarkably improved.

The invention discloses a deep reinforcement learning multi-unmanned aerial vehicle cooperative path planning method of an HCA-MAPPO, and relates to multi-agent reinforcement learning and unmanned aerial vehicle cluster control. From a multi-unmanned aerial vehicle cluster collaborative path planning method, a simulation environment comprising a three-degree-of-freedom unmanned aerial vehicle kinematic model and a dual-coordinate system management system is established, and accurate simulation of a complex geographic scene is realized; a deep reinforcement learning method based on hierarchical cross attention HCA and multi-agent near-end strategy optimization MAPPO is provided; the method comprises the following steps: introducing an HCA executor network for performing priority processing on multi-source heterogeneous information; an internal curiosity module based on collaborative consistency gating is designed, so that the effective exploration efficiency in a sparse reward environment is improved; the invention provides a tangent guidance mixed reward shaping method based on conflict perception, and solves the problem of local optimum in path planning. And the situation awareness capability, the training convergence efficiency and the path planning success rate of the unmanned aerial vehicle cluster are obviously improved.

The invention provides a civil aircraft safety perception method and system based on virtual flight, and relates to the technical field of aircraft safety perception. A dynamic and hierarchical three-dimensional wind shear field model is constructed through space-time alignment and fusion of multi-source meteorological data; deep coupling of wind disturbance and aircraft dynamic response is realized based on a small disturbance theory and a flight dynamics model; continuous and dynamic quantification of risks is realized by combining and analyzing flight parameters and a safety threshold value dynamically adjusted based on wind disturbance intensity and utilizing multi-parameter risk level weighted integration; according to the method, the risk coefficient is coupled with the three-dimensional terrain and the virtual flight trajectory, hierarchical color mapping is adopted to carry out visual space visualization, and a complete closed loop from environment modeling to risk early warning is formed, so that the identification accuracy and timeliness of the flight risk in the wind shear environment are greatly improved, and the scientificity and practicability of security situation awareness are enhanced.

The invention relates to the technical field of low-altitude prevention and control, in particular to a low-altitude target detection identification and countering prevention and control system which comprises an operation and maintenance management platform, a multi-source data fusion unit, an intelligent noise reduction preprocessing unit, a general command platform, a dynamic threat evaluation unit and a cross-domain collaborative interface module. The detection and recognition precision and efficiency are improved through intelligent noise reduction and multi-source data fusion, intelligent collaborative countering is achieved through an intelligent decision center and a full-link countering unit, global situation awareness is enhanced through cross-domain data collaboration, operation and maintenance management and system stability are optimized by means of a prediction maintenance module and a modular architecture, and the detection and recognition efficiency is improved. The system can make up for the shortages of domestic existing systems, guarantees the airspace safety of key scenes, and reduces the long-term operation cost.

The invention discloses a water conservancy project global situation awareness and cooperative early warning system, and relates to the technical field of water conservancy project management, and the system comprises a multi-source perception collection module which collects multiple types of original monitoring data generated in the operation process of a water conservancy project, carries out the preprocessing of the collected original monitoring data, and generates a global perception data set; and the global digital twinborn deduction module is used for receiving the global perception data set, extracting a historical output sequence of a target node, constructing an exogenous input matrix comprising upstream outflow, water level, discharge, tributary flow, pump station state, gate opening and a scheduling instruction according to a water network topology, performing multi-step prediction by adopting an NARX time sequence prediction algorithm, and outputting a virtual-real fusion situation sequence. According to the invention, the multi-source sensing acquisition module is used for preprocessing multiple types of original monitoring data, so that the problem of data islands is solved, comprehensive convergence and standardization of hydraulic engineering operation data are realized, and a foundation is laid for subsequent analysis.

The invention relates to the technical field of radio monitoring and electromagnetic situation awareness, in particular to a multi-sensor data fusion intelligent electromagnetic situation awareness system, which comprises an environment reconstruction module for calculating and generating a pure ideal state reference vector which is not interfered at the current moment; the disturbance deduction module is configured to call to-be-detected target characteristic parameters from a preset signal characteristic parameter library and generate a simulation state vector with disturbance; the difference calculation module is used for calculating a numerical value difference between the real-time observation vector and the pure ideal state reference vector to obtain a real difference residual error, and calculating a numerical value difference between the disturbance simulation state vector and the pure ideal state reference vector to obtain a theoretical difference residual error; the coupling judgment module is used for calculating the geometric direction consistency of the real difference residual error and the theoretical difference residual error in the feature space and outputting a spectrum situation sensing result; according to the method, the false alarm rate of the system is remarkably reduced, and the judgment stability in a low signal-to-noise ratio environment is ensured.

The invention relates to the technical field of fire safety, and discloses a heptafluoropropane fire detection tube fire extinguishing system with an internet of things intelligent monitoring function. The cross-modal physical field sensing module is used for collecting internal physical field data and external environment field data of a protected area in real time; the fire detection tube health assessment and adaptive scanning control module is used for receiving the internal physical field data to execute health state assessment; the cross-modal correlation interference discrimination module is used for receiving internal physical field data and external environment field data; the fire process evolution analysis and time-space association confirmation module is used for receiving data of potential fire events; a precise fire extinguishing strategy generation module; a precise targeted response execution module; and a remote situation awareness and monitoring interaction module. Through the combination of multi-source data verification, health state self-evaluation and accurate response, the problems that a traditional system is weak in anti-interference capacity and prone to false alarm and waste of a fire extinguishing agent are effectively solved, and the accuracy of fire detection and the high efficiency of fire extinguishing response are remarkably improved.