452 results about "Threat level" patented technology

The invention discloses a network security threat research and judgment method, system and device and a storage medium, and the method comprises the following steps: S1, obtaining network traffic, terminal logs, application program interface calling records and threat intelligence data in real time, carrying out the standardized cleaning and format conversion of the data, and building a unified data lake; s2, matching, identifying and determining threats through a preset known threat feature library, constructing a normal behavior baseline by using an unsupervised learning algorithm, and marking suspicious events deviating from the baseline; s3, for the suspicious event marked in the step S2, mining a potential attack path and an attack intention by combining knowledge graph technology associated asset information, a historical attack chain and a homologous IP address; and S4, based on the attack success probability, the influence asset importance and the diffusion speed, calculating a threat level by adopting a fuzzy comprehensive evaluation model, and generating a research and judgment report containing disposal suggestions.

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 invention discloses a portable unmanned aerial vehicle sound wave countering system and method, and relates to the field of radio countermeasure and sound wave interference, and the system comprises a multi-source sensing module which is used for obtaining a multi-modal feature data set of a target unmanned aerial vehicle; the signal processing module is used for carrying out space-time correlation analysis on the multi-modal characteristic data set and generating a threat evaluation parameter set containing an unmanned aerial vehicle type identification result, communication protocol analysis characteristics and navigation mode discrimination indexes; the countering strategy generation module is used for generating a sound wave modulation parameter set, a radio frequency interference waveform sequence and a positioning deception signal template; the cooperative interference module is used for generating a multiband joint countering instruction set according to the improved phase cooperative control strategy; and the execution monitoring module is used for executing the multiband joint countering instruction set in the preset airspace range. According to the portable unmanned aerial vehicle sound wave countering system and method provided by the invention, the multi-level countering capability for different threat levels is improved.

The invention discloses a multi-dimensional security data threat detection method and device based on a large model and a storage medium, and belongs to the technical field of network security, and the method comprises the steps: obtaining multi-dimensional heterogeneous security data, and generating a plurality of basic event descriptions; performing semantic processing on the basic event description to obtain a standardized event description, inputting a pre-trained large language model to perform single-point threat research and judgment, and outputting an event threat evaluation result; splicing the standardized event descriptions of the same target entity into a behavior sequence text, inputting the behavior sequence text into a large language model for multi-stage attack feature analysis, and outputting an attack chain integrity evaluation result; determining a comprehensive threat level based on the event threat assessment result, the attack chain integrity assessment result and the affected asset attribute; and generating a security event analysis report, and extracting the security entities and the association relationships thereof from the report to update the security knowledge graph. The technical breakthrough from traditional'event seeing 'to real'event understanding' is realized.

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 relates to the technical field of traffic data processing, and discloses a traffic edge computing data security sharing method and system, and the method comprises the steps: collecting traffic edge computing network multi-source heterogeneous data, and carrying out the classification standardization processing to generate a structured data set; designing a dynamic data sharing security protocol based on a multi-party security computing protocol and a homomorphic encryption algorithm; verifying the authority of a requester in a multi-level manner by using an attribute-based access control model and a zero-knowledge proof mechanism, and generating a dynamic access token; storing data by adopting a fragmentation storage and redundancy encryption strategy, and recording storage information through a hash chain; and dynamically adjusting the encryption strength and the sharing strategy according to the network threat level and the data sensitivity. The method effectively guarantees safe sharing of traffic data, accurately controls access authority, improves storage and sharing efficiency, adapts to complex network environment changes, and provides powerful support for development of an intelligent traffic system.

The invention relates to the technical field of intelligent control and unmanned aerial vehicle application, in particular to a bird repelling and unmanned aerial vehicle dispatching integrated platform based on big data analysis. Comprising a multi-source data acquisition module; a dynamic scheduling decision module; and a federal learning optimization module. According to the invention, the millimeter-wave radar, the infrared camera, the voiceprint recognition unit and the meteorological data interface are integrated through the multi-source data acquisition module, so that the defects of one-sided data and extensive evaluation of a traditional scheme are overcome; a bird threat level, unmanned aerial vehicle endurance time, an electric quantity attenuation model and an airspace control rule are fused through a dynamic scheduling decision module, dynamic balance of priority response of a high-risk area and unmanned aerial vehicle endurance and energy consumption is realized, and the problems of response lag and insufficient endurance caused by unreasonable task allocation in a traditional scheme are avoided; the federal learning optimization module adapts to a specific geographical environment through transfer learning, and the problem of strategy stiffness of a traditional scheme in a complex environment is solved.

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 technical field of perimeter intrusion monitoring, in particular to a perimeter intrusion monitoring system based on cooperation of infrared thermal imaging and an intelligent algorithm. The perimeter intrusion monitoring system based on infrared thermal imaging and intelligent algorithm cooperation comprises a multi-source sensing layer, an edge computing node, a cloud analysis platform and a response execution layer. According to the perimeter intrusion monitoring system based on cooperation of infrared thermal imaging and an intelligent algorithm, environmental noise interference is effectively suppressed through a dynamic threshold compensation mechanism, and accurate classification of personnel, vehicles, unmanned aerial vehicles and animals is realized by combining multi-modal feature fusion of infrared thermal imaging, visible light vision and millimeter wave radar; according to the method, intention modes of passing, observation, invasion and the like can be identified, threat level dynamic adaptation is realized in combination with a hierarchical response system, the technical bottlenecks of poor environmental adaptability, high target misjudgment rate, behavior analysis deficiency and the like of a traditional system are solved, and the intelligent level and all-weather protection capability of perimeter security and protection are remarkably improved.

The invention relates to the technical field of unmanned ship navigation control, and discloses an unmanned ship navigation state control method and system based on intelligent perception. Navigation environment information of multi-dimensional data is obtained, a multi-dimensional data fusion technology is adopted for construction of an environment state matrix, and discrete environment elements are converted into structured matrix expression. In the initial path generation stage, data acquisition value evaluation is fused, obstacle avoidance requirements are considered, and a high-value detection area is actively covered. In the path correction stage, a multi-objective cost function including energy consumption, threat degree and path length is constructed, and real-time path optimization in a dynamic environment is realized. In the propulsion control link, propulsion force and rudder angle parameters are calculated by integrating the attitude, the ship speed and the ocean current speed of the unmanned ship. According to the method, the contradiction among the real-time performance of dynamic obstacle avoidance, the data acquisition efficiency and the energy consumption optimization is solved through the cooperation of global-local double-layer path planning and the control of the propulsive force and the rudder angle of the unmanned ship.

The invention discloses a security threat-oriented unmanned aerial vehicle active protection method and system for a power system, and the method comprises the following steps: S1, collecting data through active patrol and a plurality of sensors by a defense unmanned aerial vehicle, and carrying out the detection of a surrounding environment through AI recognition; and S2, after the target is detected, the defense unmanned aerial vehicle performs intelligent behavior analysis on the detected unmanned aerial vehicle, and judges whether the unmanned aerial vehicle is a real security threat or not. According to the method, collaborative detection of multiple sensors is combined with AI recognition, the threat flyer can be effectively recognized, the false alarm rate is reduced, the detection accuracy is improved, in addition, intelligent behavior analysis is combined, the threat level is effectively evaluated, the risk of false alarm and misjudgment is reduced, automatic interception is conducted according to the evaluation result, manual intervention is not needed, and the risk of risk of threat detection is reduced. The safety protection effect is improved, and the power system safety is effectively guaranteed.

The invention provides a tunnel anomaly detection and response method and device based on an orbital robot, and the method comprises the steps: obtaining scoring and grading data from a quantitative scoring model through a preset model optimization module, carrying out the dynamic adjustment of the parameters of the scoring model through a deep Q learning network in combination with historical response records, optimizing a grading threshold value to match an actual threat level, and carrying out the detection of an actual threat level. Transmitting the adjusted hierarchical data to a response scheduling system; and according to the adjusted hierarchical data, automatically matching a preset response strategy combination, if at least one selectable strategy combination exists, determining an optimal strategy combination through a weighted optimization algorithm, a comprehensive response speed, a resource utilization rate and a disposal cost, and issuing an execution instruction to a related execution unit.

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 relates to the technical field of the Internet of Things, and discloses a security assessment method and system based on the Internet of Vehicles, and the method comprises the steps: obtaining multi-dimensional original data, context data and a traffic data flow; attack event features are generated through data classification, cleaning, standardization and PCA feature extraction; constructing spatial and temporal distribution characteristics in combination with context data, determining a damaged object and a damage radius through a K-means clustering and morphological expansion algorithm, and outputting a damage area; analyzing abnormal behavior distribution based on the area and the traffic data flow, and generating a distribution diagram; analyzing the threat level according to the distribution map, the damaged object and the hazard radius, distributing resources by combining a greedy algorithm, and planning a time sequence to construct a dynamic defense strategy; optimizing a protection scheme according to the real-time feedback data; finally, behavior changes are continuously monitored, and a risk thermodynamic diagram and a safety gap list are generated. According to the method, the Internet of Vehicles safety assessment capability is improved.

The invention relates to the technical field of unmanned aerial vehicle countering, in particular to a vehicle-mounted unmanned aerial vehicle detection and countering integrated system and a working method thereof. The method comprises the following steps: continuously scanning a preset airspace and acquiring radio frequency fingerprint characteristics of an unmanned aerial vehicle in real time through a radio detection unit carried by a vehicle-mounted platform; generating a target recognition result based on the radio frequency fingerprint features; when the target identification result determines that illegal unmanned aerial vehicle intrusion exists, generating a corresponding adaptive countering instruction based on the threat level; driving a directional interference unit to transmit a blocking signal to the target unmanned aerial vehicle according to the self-adaptive countering instruction; and synchronously monitoring the state response of the target unmanned aerial vehicle in the countering process, and feeding back the state response to the control unit in real time to dynamically adjust the countering strategy until the threat is relieved. According to the invention, full-process intelligent disposal of the illegal unmanned aerial vehicle is realized, and target detection, accurate identification and dynamic countering can be completed in real time in a vehicle moving state.

The invention relates to the technical field of unmanned aerial vehicle signal processing, and discloses an unmanned aerial vehicle countering multiband radar early warning system, which comprises a multiband radar detection module, an echo data processing module, a target identification and positioning module and an early warning information generation module. The multi-band radar detection module emits radar waves of different frequency bands to obtain echo data, the echo data processing module calculates the difference degree and the comprehensive difference coefficient for target identification, the target identification and positioning module determines the position of the unmanned aerial vehicle, and the early warning information generation module issues early warning. The method has the advantages that accuracy and reliability are improved through multi-frequency-band detection, targets are accurately recognized and positioned, threat levels can be evaluated, interference resistance, clutter suppression and environment self-adaption capacity are achieved, multiple targets can be distinguished and tracked, the defects of an existing unmanned aerial vehicle monitoring technology are effectively overcome, powerful support is provided for unmanned aerial vehicle countering, and the method is worthy of popularization and application. And public safety and key area stability are ensured.

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 discloses a multi-dimensional safety fusion risk assessment method and related equipment, and relates to the technical field of automatic driving, and the method comprises the steps: obtaining a first data set and a second data set of a target system; performing functional security risk analysis, expected functional security risk analysis and information security risk analysis on the first data set to identify a hazardous event of the target system; based on the second data set, establishing a dynamic scene model, and obtaining a threat level of the hazard event through the dynamic scene model; constructing a multi-dimensional risk matrix according to the hazard event, the dynamic scene model and the threat level; and obtaining a risk value of the hazard event based on the multi-dimensional risk matrix, and performing risk grade division according to the risk value to obtain a multi-dimensional security risk assessment result of the target system.

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 target threat assessment method based on a large language model, and the method comprises the following steps: carrying out the modeling of a confrontation environment, integrating multi-dimensional information, and determining a plurality of threat assessment targets; generating an evaluation index through the large language model; evaluating the generated evaluation indexes in combination with expert knowledge, if the evaluation indexes do not meet the requirements, further generating the evaluation indexes by the large language model, and if the evaluation indexes meet the requirements, generating an evaluation matrix by the large language model; performing evaluation by combining an evaluation matrix symmetrically formed by expert knowledge, if the evaluation matrix does not meet requirements, further generating the evaluation matrix by the large language model, and if the evaluation matrix meets the requirements, calculating the distance between each target and the positive and negative ideal solutions; and performing threat level sorting on the target according to the distance. By optimizing the decision process, more efficient threat assessment in a complex confrontation environment is realized, and decision support with higher reference value is provided for decision makers.

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 provides an unmanned aerial vehicle countering method, which comprises the following steps of: acquiring a radar signal containing an unmanned aerial vehicle reflection signal in a target airspace through a radar detection module, obtaining an unmanned aerial vehicle motion feature through feature extraction, and inputting the unmanned aerial vehicle motion feature into a threat judgment model to determine a threat level. And if the active interception condition is met, controlling the radio interference module to transmit a directional interference beam and activate the physical interception module, and analyzing an interference efficiency parameter to generate a dynamic interception instruction. And finally, combining the target airspace coordinate, the threat level and the interception instruction into a countering control instruction, and sending the countering control instruction to a physical interception module to complete interception. According to the invention, automatic and accurate countering of the urban low-altitude unmanned aerial vehicle can be realized, and the response speed and interception reliability of a security and protection system are improved.

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.