294 results about "Threat level" patented technology

The invention relates to a wireless communication test method and system based on a portable frequency spectrograph and a medium, and belongs to the technical field of radio frequency spectrum analysis, and the wireless communication test method comprises the steps: collecting electromagnetic signal original data of a target area; constructing a dynamic electromagnetic environment baseline model; performing filtering and background subtraction processing on the original data of the electromagnetic signal; carrying out modulation mode identification, protocol analysis and terminal identification extraction on the preprocessed signal matrix, generating a multi-dimensional feature vector, carrying out convolution similarity matching on the multi-dimensional feature vector and a legal signal feature template, and distributing a threat level label; dynamically reconstructing scanning parameters of the portable spectrum analyzer according to the threat level label, generating an equipment parameter adjustment record and updating the electromagnetic fingerprint database; and generating a spectrum optimization control instruction based on the threat level label and the equipment parameter adjustment record, and issuing the spectrum optimization control instruction to the target base station or the terminal equipment. According to the application, the identification precision of the threat signal and the control closed-loop linkage capability in a complex electromagnetic environment are improved.

The present invention relates to adaptive multi-state encoding and processing in virtualized computing environments. The system includes a virtualized state selection module that dynamically transitions between binary, ternary, quaternary, and higher-order encoding states based on workload, bandwidth, security posture, and compliance requirements. A virtual encoding engine utilizes hardware-accelerated components, such as vFPGAs, vGPUs, and cTPUs, to enhance encoding throughput. A compliance-driven feedback controller continuously monitors encoding efficiency, threat levels, and adherence to mandates such as GDPR, HIPAA, and FIPS 140-3. Additional features include AI-based anomaly detection, federated model refinement, distributed ledger-backed audit trails, and quantum-resistant encoding techniques. By integrating intelligent encoding decisions with scalable compliance enforcement, the system enables high-performance, secure, and regulation-ready data processing across distributed cloud and edge environments, delivering measurable improvements in system responsiveness, data integrity, and operational trust.

The invention belongs to the technical field of electromagnetic confrontation and intelligent decision making, and particularly relates to an electromagnetic situation intelligent confrontation decision making method and system based on deep learning. Threat level evaluation and resource adaptation degree analysis are carried out based on a multi-head self-attention mechanism, and a dynamic electromagnetic confrontation strategy knowledge graph is generated; a game confrontation engine driven by deep reinforcement learning is used for deducing an enemy countering path and a situation evolution result under different interference strategies of our party, interference parameters are optimized through a multi-agent collaborative learning model, weight parameters of a strategy network are updated in real time through online confrontation element learning, and historical confrontation efficiency data and current task constraints are combined to obtain a confrontation result. And dynamically generating a target interference decision sequence and an electromagnetic confrontation efficiency prediction report, and synchronously updating the electromagnetic confrontation strategy library. Therefore, the problems of serious disjunction of deduction results, low real-time antagonism and the like in the prior art are solved.

The invention discloses an unmanned aerial vehicle intrusion real-time alarm and countering guidance system based on RID information. The system comprises a multi-source information acquisition and fusion module, an intrusion detection and threat evaluation module, a real-time alarm and countering strategy generation module, a feedback evaluation and strategy iteration module, a signal loss prediction and compensation module, a comprehensive processing terminal module and the like. The system establishes a three-dimensional geofence model by analyzing remote identification information of the unmanned aerial vehicle and fusing radar, photoelectric, electromagnetic and other multi-source data, and performs dynamic judgment and multi-factor threat grading on a target intrusion state. Multi-mode alarm is automatically triggered according to the threat level, and a hierarchical countering scheme from broadcast alarm and electromagnetic interference to physical capture is generated; and when the RID signal is lost, the system realizes continuous tracking and state recovery of the target by using a motion prediction and probability association algorithm.

The invention relates to the technical field of network security, and particularly discloses an edge device network threat detection method and system based on an electric power large model, and the method comprises the steps: capturing a network message sequence in real time, extracting a time sequence randomness feature and a semantic deviation feature from a time dimension and a protocol dimension, and carrying out the fusion to form a comprehensive threat feature vector; performing multi-dimensional feature analysis and time sequence modeling by adopting a lightweight electric power large model to realize millisecond-level threat assessment; establishing a multi-level response mechanism, dynamically triggering a differential protection strategy according to the threat level, and ensuring the reliability and consistency of response actions through digital signature and collaborative verification; according to the method, the complex network attack in the power edge equipment can be effectively identified, the threat detection accuracy and the system defense capability are improved, and the strict requirements of a power system on real-time performance and reliability are met.

The invention relates to the technical field of robot obstacle avoidance methods, in particular to an obstacle avoidance method of a robot. According to the method, a dynamic environment sensing system is constructed through multi-sensor fusion, and a dynamic feature description package and a static environment sketch are generated. The system generates a global navigation route by adopting an improved path planning algorithm, and constructs an obstacle space-time probability distribution map in combination with a multi-hypothesis prediction technology. According to the method, a two-channel intention analysis mechanism is introduced, the motion intention of a target is predicted by analyzing the kinematics characteristics and the biological behavior characteristics of the target, and a four-dimensional space-time threat model is established by fusing a space-time probability graph. And according to the threat level, a hierarchical obstacle avoidance strategy is dynamically generated, wherein the hierarchical obstacle avoidance strategy comprises various response modes such as path fine adjustment, speed cooperative adjustment and emergency channel avoidance. The environment cognition and behavior model is continuously updated through a closed-loop optimization mechanism, and finally safe and smooth autonomous navigation in the high-dynamic man-machine coexistence environment is achieved.

The invention relates to the field of abnormal flyer identification, and provides an airspace abnormal flyer monitoring method combining an FS-AFD model and joint agent analysis, and the method comprises the steps: collecting a to-be-detected video of a target airspace, splitting the to-be-detected video into image frame sequences which are continuous in a time sequence, performing structured division on each image frame in the image frame sequence to obtain a spatial semantic feature sequence, using a foreground region observation module to identify a dynamic change region or an abnormal object existence region in each image frame in the image frame sequence to obtain an abnormal foreground feature sequence, screening out candidate regions of suspected abnormal flyers, and performing feature extraction on the candidate regions of the suspected abnormal flyers. The candidate area is input into the joint agent, abnormal flyer existence judgment, attribute recognition, threat index calculation and threat level judgment are executed, and a corresponding judgment result is output, the detection capacity of unknown or unconventional flyers is expanded, and the method is suitable for actual deployment scenes with scarce samples or unpredictable target categories.

The invention discloses an unattended substation autonomous operation and maintenance management platform based on multi-modal data fusion. Multi-source data such as equipment state, intrusion behavior, flood prevention safety and environmental parameters are acquired through a multi-modal data sensing module; the cross-modal space-time fusion module performs timestamp alignment, space coordinate mapping and feature level fusion on the data to generate a unified representation vector; the intelligent analysis module outputs analysis results such as equipment fault probability and intrusion threat level by using a multi-task parallel deep learning model; the autonomous decision-making module dynamically generates an operation and maintenance strategy set based on the rule base and the knowledge graph; the execution driving module converts the strategy into an executable instruction and issues the executable instruction to the edge computing node; the visual interaction module realizes strategy verification through a three-dimensional digital twinborn body; and the edge computing node drives an execution mechanism to complete specific operation and return a result. According to the invention, multi-source data fusion, intelligent analysis and autonomous decision making of the unattended substation are realized, and the operation and maintenance efficiency and reliability are improved.

The invention discloses an unmanned aerial vehicle detection countering method, system and equipment and a medium, and the method comprises the steps: obtaining a task instruction and sensing signals of a plurality of sensing devices in a monitoring region in real time, carrying out the preprocessing of the task instruction and the sensing signals, and obtaining the standardized modal data; performing feature extraction and high-level semantic extraction on the task instruction and the standardized modal data corresponding to each sensing signal in parallel to obtain a task context parameter and a high-dimensional feature vector of each sensing signal; fusing the high-dimensional feature vectors based on the task context parameters by using a space-time alignment multi-modal fusion algorithm to obtain multi-modal fusion features; using a neural network model to predict and generate a flight trajectory of the unmanned aerial vehicle in a future time period according to the historical trajectory data and the multi-modal fusion features; and performing threat level judgment and behavior anomaly detection based on the task context parameters and the flight trajectory, generating alarm information, and executing a security countering strategy generated based on the alarm information.

The invention belongs to the technical field of power network inspection, and particularly relates to a power network security inspection system which comprises a multi-source sensing module, an edge calculation module, a zero-trust security verification module, a core analysis module, an execution module and a feedback optimization module. Through an edge-cloud collaborative architecture, sensors, network flow and video data are fused in real time, the detection efficiency is improved, a physical equipment state and zero-trust dynamic authority control are introduced, 'physical-digital 'dual verification is realized, real threats and false alarms are marked, algorithm parameter adjustment is guided, and a feedback optimization module continuously reduces the false alarm rate through a closed loop mechanism; full-dimension safety protection of the power network is realized through modular design, dynamic branch processing and a closed-loop optimization mechanism; the threat level and the risk assessment result are dynamically adjusted through the environmental risk correction factor, the risk misjudgment rate is reduced, and the fault prediction accuracy in extreme weather is improved.

The invention provides an electric power metering system network security situation analysis method and system based on big data, and relates to the technical field of electric power system information security. According to the method, network traffic, system logs, security alarms, asset information, vulnerability records and external threat intelligence are collected, a security data lake is constructed, and security situation factors are extracted; outputting an anomaly detection result and a threat classification result by using the unsupervised anomaly detection model and the supervised threat classification model; calculating an asset security risk value and an overall security risk value by combining the vulnerability severity and the asset importance, and generating an overall security index, an attack threat level and a vulnerability level; and a time sequence prediction model is further constructed based on the network security situation indexes, and future situation prediction and security early warning are realized. According to the invention, comprehensive perception, accurate analysis and active defense of the network security situation can be realized.

The invention discloses a vehicle-mounted communication terminal protection strategy generation method, apparatus and device, and a storage medium. The method comprises the steps of obtaining multi-source heterogeneous data of a vehicle-mounted communication terminal; performing feature extraction on the multi-source heterogeneous data to obtain a feature vector; inputting the feature vector into a target deep learning network, identifying the current attack mode of the vehicle-mounted communication terminal through the target deep learning network, and outputting an attack detection result; inputting the attack detection result and the state information of the vehicle-mounted communication terminal into a target reinforcement learning network, evaluating the security threat level of the attack mode through the target reinforcement learning network, and outputting the security threat level; and generating a target protection strategy of the vehicle-mounted communication terminal according to the security threat level. According to the invention, the adaptability of the vehicle-mounted communication terminal protection strategy is improved.

The invention discloses a low-altitude economic flight data management method based on a block chain technology, and relates to the technical field of low-altitude traffic management, and the method comprises the steps: verifying an encrypted positioning proof, generating a verification state set, decrypting flight coordinates according to the verification state set, and generating a credible coordinate data stream; performing sensitive airspace intrusion detection according to the credible coordinate data stream and the high-security protection node, performing collision risk analysis by the traffic management node to obtain an encrypted aggregated threat vector, and identifying a threat level according to the encrypted aggregated threat vector; and dynamically adjusting the working parameters of the airborne equipment according to the threat level, generating an airborne equipment regulation and control instruction set, scanning the signal-to-noise ratio and the threat intensity in real time, dynamically allocating a communication frequency band, and generating a new positioning strategy parameter. According to the method, the real-time signal-to-noise ratio and the threat intensity scanning result are combined, communication frequency band dynamic allocation and encryption strategy dynamic optimization are driven, the airspace situation awareness capacity is formed, the resource utilization rate is increased, and the anti-interference capacity is enhanced.

The invention discloses a multi-level network threat dynamic identification method based on a graph neural network, and the method comprises the following steps: collecting multi-source heterogeneous network security data, and carrying out the preprocessing; constructing a multi-level network threat graph; inputting the multi-level network threat graph into an improved GraphSAGE network to carry out graph embedding modeling; identifying an attack propagation path, and extracting a risk sub-graph region serving as a candidate attack chain; performing threat level evaluation on the risk sub-graph region, and calculating an overall threat score of the risk sub-graph region; and comparing the overall threat score with a preset threshold value, if the overall threat score exceeds the threshold value, determining that the threat is a high-risk threat, and outputting an early warning result. The multi-level threat graph is modeled through the graph neural network, attack chain recognition and threat evaluation are achieved, and the method has the advantages of being high in expressive power, accurate in recognition and fast in response.

The invention relates to the technical field of communication, and discloses a wireless signal coverage enhancement system and method based on reinforcement learning, and the method comprises the following steps: fusing multi-source perception data of a laser radar, a camera and the like, extracting a terminal motion state and environment obstacle information in real time, calculating a relative motion parameter and collision time, and carrying out the real-time detection of the collision time; the method comprises the following steps: estimating a threat level according to the obstacle size and distance, outputting a quantitative threat index and a potential high-risk target, identifying an effective reflecting surface from point cloud data, constructing a time-varying diffraction topological graph according to the dynamic obstacle position, and predicting signal loss of each path by using a graph neural network. According to the method, the optimal diffraction path is predicted by sensing the motion trail of the high-speed dynamic obstacle in real time in combination with environmental electromagnetic characteristics, the beam forming and power strategy is dynamically optimized, the influence of multipath interference and penetration loss on the signal quality is suppressed, and the interruption recovery time can be shortened and the positioning precision and the communication reliability can be improved in a shielding scene.

The invention discloses an unmanned aerial vehicle countering method and countering device for radio detection, and relates to the field of signal processing, and the method comprises the steps: scanning and capturing a transient radio frequency pulse signal through a radio frequency receiving array, carrying out the time-frequency domain conjoint analysis, constructing a signal intensity space distribution matrix, and extracting a parameter set; matching with a protocol feature library, identifying an unmanned aerial vehicle remote control link protocol type, and calculating an unmanned aerial vehicle position and a threat level in combination with a signal time difference of arrival and a frequency difference; generating adaptive interference signal parameters according to the protocol type and the threat level; the method comprises the following steps: generating a directional beam through a two-dimensional phased-array antenna, focusing the position of an unmanned aerial vehicle to transmit an interference signal, monitoring signal intensity change and protocol response delay in real time, calculating a communication suppression ratio, and iteratively optimizing an interference strategy until a suppression threshold is reached. The method has the advantages that efficient and accurate signal capture and interference are realized through multi-channel radio frequency detection, accurate positioning and adaptive interference technologies, and the countering efficiency and adaptability are remarkably improved.

The invention relates to the technical field of unmanned aerial vehicle radio detection, in particular to an unmanned aerial vehicle detection and judgment method based on cooperation of an air-ground system and radio detection, and the method comprises the steps: finding a suspected target through scanning of a multi-frequency-band radar, triggering an infrared thermal imager carried by an aerial unmanned aerial vehicle to approach an ultra-wide-frequency-band spectrum sensor for verification; accurate positioning is realized by combining ground photoelectric equipment multi-view triangulation positioning and a signal arrival time difference algorithm, a command and control center calculates threat levels through a multi-source data fusion engine, and navigation decoy of dynamically adjusting intensity is adopted for targets smaller than or equal to two levels. Hard killing of ground laser locking rotor center and air electromagnetic pulse synchronous coverage is executed on a target of more than or equal to three levels, the equipment state is synchronized in real time through an optical fiber link, high-energy-consumption equipment is intelligently closed after threat is relieved, a platform starting strategy is optimized, a detection closed loop of'three-dimensional perception-graded treatment-intelligent energy saving 'is constructed, and the intelligent energy saving effect is achieved. And an efficient and accurate low-altitude safety protection scheme is provided for a sensitive area.

The invention discloses a marine fuel leakage assessment method, device and equipment, a medium and a product. The method comprises the following steps: acquiring leakage scene information related to marine fuel leakage, wherein the leakage scene information at least comprises personnel position information, ship environment information, leakage state information and fuel type information; based on the leakage scene information, determining first distribution data of leakage concentration and leakage related positions by calling a preset simulation model realized based on computational fluid dynamics or calling a historical leakage simulation result in a database, and second distribution data of leakage concentration and leakage time at a position indicated by the personnel position information; and according to the first distribution data and the second distribution data, visually displaying a leakage concentration contour map marked with the personnel position information, a leakage concentration time change map and personnel escape suggestion information. According to the scheme, the actual threat degree of leakage to personnel on the ship can be reflected, and suggestions for dealing with leakage are provided.

The invention provides a vehicle collision detection and intervention method and system, and relates to the technical field of data processing, and the method comprises the steps: carrying out the spatial modeling analysis of a dynamically selected vehicle motion track curvature center point, a vehicle safety envelope boundary point and a historical track weighted center point based on a minimum safety distance, and obtaining a geometric feature model; converting the geometric feature model into a corresponding virtual feature point set, and constructing a dynamic evaluation structure based on the virtual feature point set; based on the dynamic evaluation structure, performing security threat level calculation to obtain a vehicle security threat index; according to the vehicle security threat index, performing early warning grade determination to obtain an early warning grade; and controlling the roadside equipment to execute corresponding early warning and intervention operation according to the early warning level. The trend that the vehicle deviates from the normal driving path can be recognized in advance through track prediction, and the time window of risk response is shortened.

The invention relates to the technical field of unmanned aerial vehicle autonomous reconnaissance, in particular to an unmanned aerial vehicle reconnaissance decision-making method, device and equipment based on edge calculation and a medium, and the method comprises the steps: obtaining the multi-dimensional data of an environment and a target through an optical camera, an infrared thermal imager and a radar carried by an unmanned aerial vehicle; target identification and strategy reasoning are carried out on the multi-dimensional data, a target type and attributes are identified from the multi-dimensional data through a lightweight convolutional neural network, and a battlefield situation is obtained in advance to generate a strike strategy; obtaining a threat level and a task priority, and dynamically adjusting the flight path of the unmanned aerial vehicle and the strike strategy according to the threat level and the task priority; and obtaining a strike result corresponding to the strike strategy, analyzing the strike result in real time, judging a target destroy state through an airborne sensor, and updating a subsequent task strategy. According to the invention, the autonomy of the unmanned aerial vehicle system is improved.

The invention discloses an identity authentication method and system based on a national secret algorithm, and the method comprises the steps: building a hierarchical key management system based on a national secret IBE framework, generating a system master key pair through employing an SM2 algorithm, and achieving a decentralized key distribution mechanism; constructing a domain perception differential privacy protection module, defining a privacy budget allocation strategy according to the security level, and adding calibrated Laplace noise to the user identity feature vector; designing a distributed batch matrix multiplication protocol, and decomposing the distributed batch matrix multiplication protocol to a plurality of computing nodes for parallel processing through a secret sharing technology; a zero-knowledge proof verification mechanism is implemented, and identity verification is completed through a commitment scheme based on an SM3 hash algorithm; deploying a self-adaptive key updating strategy, and analyzing threat level change through a threat situation evaluation function; and establishing a secure communication channel based on SM4 symmetric encryption, and performing encryption processing by using the temporary session key. The security and expandability of the system are improved, the privacy of the user is effectively protected, and the system adapts to a dynamically changing network threat environment.

The invention relates to the technical field of computers, in particular to a network security operation method, device and equipment and a storage medium, and is used for providing a network security operation scheme which can give consideration to efficiency and precision and is adaptive to a current dynamically evolved network threat environment. The method comprises the steps of obtaining to-be-processed operation data, wherein the operation data is obtained by preprocessing network flow data and log data; processing the operation data by using a lightweight model to generate initial alarm data; based on the initial alarm data, carrying out alarm authenticity discrimination and threat level determination through a heavy model, and outputting confirmed threat event information; the threat event information indicates alarm data and threat levels corresponding to threat events confirmed by the heavy model; based on the threat event information, risk disposal is executed, affected asset information is obtained, a traceability evidence obtaining tool is matched, and a standardized evidence obtaining report is generated; and based on the standardized evidence obtaining report, executing vulnerability repair.

The invention discloses a campus intelligent alarm linkage method and system based on face recognition, and the method comprises the steps: collecting a video stream in real time, and extracting a face image in a video frame through a face detection algorithm; comparing the face image with a white list database pre-stored in a campus in real time, and generating an identity recognition result and a confidence score of the face; calculating the threat level of the current behavior scene through a dynamic early warning judgment model and generating a graded early warning signal based on the identity recognition result and the confidence score in combination with the real-time analysis of the personnel behavior scene; automatically triggering a corresponding equipment linkage instruction according to the graded early warning signal; and on the basis of the execution state of the equipment linkage instruction and a subsequent video analysis result, generating a complete security event disposal report containing an incident position, an incident-related personnel image and a disposal suggestion. According to the embodiment of the invention, the active early warning capability and the emergency response efficiency of the campus security system can be improved.

The invention relates to the technical field of unmanned aerial vehicle detection, in particular to a civil small unmanned aerial vehicle cat eye detection and countering system which comprises the steps that a cat eye detection module collects cat eye reflection characteristic signals, environment sensing data and unmanned aerial vehicle flight state associated information in an airspace; the threat level evaluation center identifies the type of the unmanned aerial vehicle and the intrusion risk level through an improved random forest model, and measures the real-time airspace position; the dynamic trajectory modeling module constructs a digital twin flight model of the civil small unmanned aerial vehicle, and simulates a flight trajectory and a potential intrusion path by using an intention pre-judgment algorithm; the grading countering unit generates a non-destructive countering strategy according to the intrusion risk grade, the flight path and the potential intrusion path; the man-machine interaction unit constructs a visual airspace monitoring scene, renders a detection result, an intrusion risk level and a countering effect in real time, and carries out man-machine interaction operation. Therefore, the problems of insufficient perception, poor decision collaboration and the like in the prior art are solved.

The invention belongs to the technical field of network security management and control, and particularly relates to a network security threat perception and adaptive defense system based on artificial intelligence. Comprising a data acquisition preprocessing module, a threat feature intelligent mining module, a threat situation comprehensive evaluation module, a self-adaptive defense strategy generation module, a defense effect dynamic feedback module and a background supervision terminal. The data acquisition and preprocessing module acquires various data from a network environment and performs related preprocessing operation, the threat feature intelligent mining module mines potential network security threat features, and the threat situation comprehensive evaluation module comprehensively evaluates the network security threat situation. The self-adaptive defense strategy generation module generates a self-adaptive defense strategy based on the threat level and the influence range, and the defense effect dynamic feedback module monitors and evaluates the implementation effect of the self-adaptive defense strategy in real time, thereby providing an omnibearing, intelligent and self-adaptive guarantee for network security.

The invention discloses a method for measuring hydrological flow through amphibious equipment, and relates to the field of hydrological flow monitoring, the method comprises the steps that an amphibious platform with a ship body at the bottom and multiple rotor wings additionally arranged at the top is utilized, an ADCP, a GNSS / IMU, an environment sensor and the like are integrated, and the interference trend and the threat level are pre-judged by means of a prediction model by sensing water flow, waves, wind power and the posture of the ship body in real time. An active stability control algorithm is adopted, power output of an underwater propeller and an air rotor wing is dynamically coordinated and distributed, a ship body is restrained from moving to a measurement allowable error range, and the core of the method is that an operation mode is intelligently switched, and underwater power contact measurement is relied on in medium and low water; the system lifts off in high water or flood, GNSS positioning is adopted for non-contact hovering measurement, the industrial problems that traditional cableway flow measurement is likely to be broken, and sailing ADCP is likely to be out of control and lose efficacy in torrent are thoroughly solved, full-scale, high-precision and full-automatic hydrological flow online monitoring is achieved, and a reliable technical means is provided for flood control, disaster reduction and water resource management.

The invention discloses a network security operation method and system, electronic equipment and a storage medium, and relates to the field of network security. The method comprises the following steps: collecting multi-source heterogeneous data in real time through a distributed sensor network, and processing the multi-source heterogeneous data to generate a standardized data set; performing feature extraction on the standardized data set to generate a feature vector fusing space-time relevance and behavior semantics; matching the feature vector with features of known threats in a threat intelligence library, and if matching succeeds, generating a preliminary alarm according to a target known threat corresponding to the matched target feature; performing false alarm filtering on the preliminary alarm, and generating a first optimization alarm list of the known threat of the target; and triggering a predefined first response strategy according to the threat level of the first optimization alarm list. By implementing the technical scheme provided by the invention, an intelligent operation scheme capable of fusing multi-source data, reducing the false alarm rate and improving the unknown threat detection capability can be obtained.

The invention relates to a low-altitude protection management method and system, and the method comprises the steps: collecting the real-time flight information and identity information of a low-altitude unmanned plane through a multi-source detection device, and carrying out the identity verification based on the identity information, and obtaining an identity verification result; if the identity verification result is not passed, determining an abnormal target, and countering the abnormal target; if the identity verification result is passed, preprocessing the real-time flight information to generate preprocessed data; performing threat analysis based on the preprocessed data and historical flight data through a machine learning framework to generate a threat level; and determining a new abnormal target according to the threat level, and countering the new abnormal target. The problems of inaccurate identity recognition, lagging threat assessment and extensive countering measures in the prior art are solved, and the low-altitude unmanned aerial vehicle management and control accuracy and real-time performance are improved.

The invention discloses a low-altitude aircraft situation awareness method and system based on multi-source heterogeneous data, and relates to the technical field of low-altitude safety monitoring. The method comprises the following steps: collecting multi-source heterogeneous data around a low-altitude aircraft; extracting heterogeneous feature data from the time sequence type data stream and the space type data stream by adopting different deep learning models; performing quantitative analysis on the signal quality of each data stream in real time to generate a dynamic reliability score; inputting the heterogeneous feature data into a deep learning fusion model, and performing real-time modulation on a fusion weight by using a dynamic reliability score to generate a fusion feature vector; and processing the fusion feature vector through a deep learning fusion model to generate comprehensive situation awareness output including target classification, trajectory prediction and threat level evaluation.

The invention relates to the technical field of network security, in particular to an artificial intelligence-based security big data intelligent analysis and detection system, which comprises a data acquisition and processing module, an abnormal mode recognition module, a context association analysis module, a threat level evaluation module and a report generation module. The system performs unified standardization processing on multi-source heterogeneous security data to form an event data stream. Deep time sequence association and context dependence features in a data stream are automatically extracted through a deep neural network to identify potential threats which are difficult to find by a traditional method. And performing dynamic causal reasoning on the identified threat events based on association rules of the knowledge graph, tracing attack steps, aggregating to form a complete security threat event chain, and performing comprehensive threat level evaluation and report generation according to the complete security threat event chain. According to the method, accurate perception of novel complex attacks and automatic attack chain reconstruction are realized, and the coverage range, the analysis depth and the response research and judgment efficiency of security threat detection are improved.