274 results about "Threat assessment" patented technology

The invention relates to the technical field of intelligent security operation and maintenance, in particular to an artificial intelligence network security system based on multi-modal large model training, which comprises a server fault diagnosis module, a network attack detection module, an endpoint security monitoring module, a key management optimization module and a threat analysis feedback module. According to the method, the fault prediction accuracy is improved through multi-dimensional data analysis, service interruption caused by sudden hardware faults is reduced, the network access frequency, source and instruction features are evaluated based on the server abnormality, the attack detection accuracy is improved, the misjudgment risk is reduced, the endpoint equipment execution behavior, resource calling and behavior sequence are extracted, and the service performance of the terminal equipment is improved. Fine-grained security monitoring is realized, attack traceability is enhanced, a key strategy is dynamically adjusted, security adaptability is improved, strategy lag risk is reduced, multi-level data is integrated to calculate threat behavior and attack fitting degree, threat assessment fineness and response speed are enhanced, and global security situation awareness is improved.

The invention discloses an unmanned aerial vehicle monitoring and countering integrated system, and relates to the technical field of unmanned aerial vehicle monitoring, and the system comprises a sensing module which is used for carrying out the monitoring of an environment through a plurality of sensors, and obtaining original data; the fusion module is used for processing the original data by adopting a data fusion algorithm to obtain target information; the identification module is used for outputting the type and behavior mode of the unmanned aerial vehicle; the analysis module is used for evaluating the type and behavior mode of the unmanned aerial vehicle by adopting a dynamic threat evaluation method and generating early warning information; the strategy module is used for formulating a dynamic countering strategy; the execution module is used for implementing corresponding countering measures and monitoring the countering effect in real time; and the recording module is used for recording all results in the whole process. Through the technical means of multi-sensor cooperative monitoring, data fusion processing, classification identification, dynamic threat assessment, game decision making, intelligent countering and the like, omnibearing perception, accurate identification, intelligent assessment and efficient disposal of the unmanned aerial vehicle are realized.

The invention relates to the technical field of network security, in particular to a network security situation awareness method and system, and the method comprises the following steps: extracting a source IP address and a target IP address based on a network behavior record, carrying out the statistics of the number of used ports, the transmission direction and the time interval value, analyzing the direction change times and the time interval difference, and screening abnormal communication pairs. And generating an abnormal communication pair set. According to the invention, through analyzing port usage, transmission direction and time interval value, deeply mining communication features, screening abnormal communication pairs and improving identification accuracy, dividing a time sequence window, analyzing rate fluctuation and frequency distribution, locking an unstable time window, combining with a multi-dimensional data classification behavior mode, and extracting a switching path and priority, a multi-dimensional data classification behavior mode is combined. Potential threat paths are identified independently, global threat situations are identified through node interaction relation statistics and correlation analysis and an expansion range, threat assessment precision and efficiency are enhanced, and comprehensive and reliable risk protection capability is provided for network managers.

The invention discloses a precise interference avoidance method and device in a radio system, and relates to the field of signal processing, and the method comprises the steps: constructing a sensing matrix through sensing node data, and capturing a transient interference signal through aperiodic scanning; performing tensor decomposition on the signal data to extract time domain, frequency domain, space domain and modulation domain features, constructing a dual-mode spectrum analysis model, reconstructing an instantaneous spectrogram by using compressed sensing, and predicting an interference mode through LSTM; after the instantaneous spectrogram and the predicted interference graph are fused, threat assessment is carried out through a multi-stage interference classification model; according to the interference category and the threat level, beam forming is optimized, adaptive null is generated, a power density optimization model is constructed, and the transmitting power is dynamically adjusted; an anti-interference frequency hopping sequence is generated based on a chaotic mapping algorithm, and spectrum camouflage and tracking interference resistance are realized. The method has the advantages that accurate identification and dynamic avoidance of interference are realized through multi-dimensional perception, intelligent prediction and adaptive beam forming, and the interference avoidance capability of a wireless system 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 invention discloses a behavior analysis early warning method and system based on AI situation awareness, and relates to the technical field of data recognition. The method comprises the following steps: collecting multi-source sensing data and extracting a numeralization feature vector; an edge correlation degree dynamic threshold self-learning algorithm is adopted to screen effective edges to construct a dynamic space-time diagram, and space-time correlation features are extracted; a modal weight coefficient is calculated based on historical detection accuracy, data integrity and scene adaptation degree, and multi-modal features are weighted and fused; inputting the deep reinforcement learning model, outputting a threat risk value and a safety response strategy, and executing; and updating the dynamic weight, the threshold value and the strategy parameter according to the early warning accuracy and the threat interception success rate to realize closed-loop optimization. The method solves the technical problems that multi-source heterogeneous data lacks uniform feature representation, cross-modal relevance is lost, a space-time association relationship cannot be dynamically updated, multi-modal feature fusion weight is fixed, fusion effectiveness is affected, threat assessment lacks adaptability, and performance is degraded due to model parameter updating lag.

The invention discloses an AI-based signaling network vulnerability attack simulation and prevention system, which comprises a signaling sensing layer, which is deployed in a core network element to carry out hardware-level decoding of an SS7 / Diameter / SIP / 5G-NR protocol, dynamically extracts protocol field-level metadata through a YARA rule base and generates a standardized signaling log; the threat modeling layer is used for receiving a signaling log output by the signaling sensing layer, generating mixed traffic after injecting a simulation attack, outputting the mixed traffic to the anomaly detection layer, constructing a multi-dimensional feature encoder by using a parameterized quantum gate based on a quantum generative adversarial network and a GFlowNet stream generation engine, generating a compliance attack vector in combination with the constraint of a 3GPP protocol state machine, and outputting the compliance attack vector to the anomaly detection layer; meanwhile, attack trajectory diversity sampling is completed through a Diameter protocol AVP nested state tree and a two-factor award function; the anomaly detection layer is used for receiving the mixed flow of the threat modeling layer and outputting a threat evaluation result to the response processing layer; and the response processing layer is used for receiving the threat assessment result of the anomaly detection layer and issuing a defense instruction to the signaling sensing layer.

Access to secured items in a computing system is requested instead of being persistent. Access requests may be granted on a just-in-time basis. Anomalous access requests are detected using machine learning models based on historic patterns. Models utilizing conditional probability or collaborative filtering also facilitate the creation of human-understandable explanations of threat assessments. Individual machine learning models are based on historic data of users, peers, cohorts, services, or resources. Models may be weighted, and then aggregated in a subsystem to produce an access request risk score. Scoring principles and conditions utilized in the scoring subsystem may include probabilities, distribution entropies, and data item counts. A feedback loop allows incremental refinement of the subsystem. Anomalous requests that would be automatically approved under a policy may instead face human review, and low threat requests that would have been delayed by human review may instead be approved automatically.

The invention discloses a forest fire hidden danger identification method and device for a power transmission line based on a multi-modal large model, and the method comprises the steps: constructing a forest fire hidden danger identification model for a smog or flame-containing image outputted by a conventional target detection model, and enabling the model to guide a dialogue set through the combination with forest fire hidden danger identification, thereby achieving the recognition of the forest fire hidden danger. The dialogue context and the image visual features are fused, so that the deep fusion of the text semantics and the firework image features is realized; and finally, inputting the deeply fused features into a large language model, so that a forest fire hidden danger output dialogue of the power transmission line can be obtained by utilizing the deep semantic understanding capability of the large language model, and then a forest fire hidden danger recognition result of the power transmission line is obtained. Therefore, according to the invention, the judgment of whether the smoke and fire can damage the power transmission line is realized, the technology upgrade from smoke and fire existence detection to equipment threat research and judgment is completed, and on the basis, the problems of resource waste and untimely crisis response caused by reporting all smoke and fire information in the traditional technology can be avoided.

Detecting package execution for threat assessments, including: receiving, from an agent on a host of a cloud deployment, data describing one or more active packages installed on the host, wherein each of the one or more active packages are identified by the agent from a plurality of packages in response to detecting a corresponding file open event; and generating a threat assessment for the host describing which of the one or more active packages have any known vulnerabilities.

The invention discloses a network security situation awareness and dynamic analysis method based on a graph neural network, and the method comprises the following steps: S1, collecting network related data, and constructing a dynamic security knowledge graph; s2, preprocessing the collected data to generate a node feature matrix; s3, based on the node feature matrix, generating a node embedding representation through an improved graph neural network and a graph attention mechanism, and optimizing the node embedding representation in combination with the graph attention mechanism; s4, establishing a time sequence prediction model based on the improved support vector regression model, and identifying potential abnormal behaviors and security threats; s5, evaluating the identified security threat, and predicting the risk level of the security threat; s6, generating a response strategy according to a security threat assessment result; and S7, executing the response strategy. According to the method, the improved graph neural network and the support vector regression model are utilized to carry out network security situation awareness and dynamic analysis, and the method has efficient and accurate security threat identification and adaptive response capabilities.

According to the network security risk assessment method for the unmanned aerial vehicle system, real-time analysis of an AI algorithm is adopted, dynamic assessment and an automatic defense strategy are combined, real-time monitoring and dynamic adjustment of a risk assessment result can be carried out, it is ensured that effective risk assessment is carried out in a rapidly changing threat environment in the flight process of an unmanned aerial vehicle, and the risk assessment efficiency is improved. The use requirements are met; comprising the following steps: scanning source codes of safety key components of an unmanned aerial vehicle system by adopting a static code analysis tool, and identifying all potential attack points; the system state, network data and sensor information of the unmanned aerial vehicle are collected, threat intelligence is obtained, an AI algorithm is adopted to analyze data in real time, and abnormal behaviors are identified; quantifying the security threat of the current abnormal behavior, and evaluating the attack possibility and potential influence; dynamically adjusting a defense strategy according to a risk assessment result; all security events and response conditions are recorded, attack traceability analysis is formed, the AI model is updated regularly, and the recognition capability of novel attacks is enhanced.

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 discloses an unmanned aerial vehicle dynamic environment perception response system based on an end side LLM, and belongs to the technical field of artificial intelligence and unmanned aerial vehicle application, and the system comprises a visual perception module which collects image external data in real time and outputs identification information; the warning identification module is used for generating a structured label according to the identification information; the LLM reasoning and decision-making module is used for reasoning a pre-deployed LLM model and giving threat assessment and decision-making content; the local knowledge enhancement module pre-constructs a local knowledge base, distinguishes categories of different labels and gives corresponding solutions; the bidirectional network communication module is used for realizing double-end communication between the unmanned aerial vehicle end and the ground end; the visualization and interaction module is used for displaying the image identification information in real time and displaying a target evaluation result; according to the unmanned aerial vehicle dynamic environment perception response system based on the end-side LLM, through fusion of the end-side LLM and visual perception, the high delay problem of cloud dependence is solved, the real-time response capability in a dynamic scene is remarkably improved, and the computing power and bandwidth requirements are reduced.

Access to secured items in a computing system is requested instead of being persistent. Access requests may be granted on a just-in-time basis. Anomalous access requests are detected using machine learning models based on historic patterns. Models utilizing conditional probability or collaborative filtering also facilitate the creation of human-understandable explanations of threat assessments. Individual machine learning models are based on historic data of users, peers, cohorts, services, or resources. Models may be weighted, and then aggregated in a subsystem to produce an access request risk score. Scoring principles and conditions utilized in the scoring subsystem may include probabilities, distribution entropies, and data item counts. A feedback loop allows incremental refinement of the subsystem. Anomalous requests that would be automatically approved under a policy may instead face human review, and low threat requests that would have been delayed by human review may instead be approved automatically.

The invention relates to the technical field of artificial intelligence and unmanned aerial vehicle path planning, in particular to a multi-rotor unmanned aerial vehicle adaptive obstacle avoidance strategy generation system and method, and the system comprises a multi-source environment sensing module which is used for collecting environment data; the dynamic obstacle modeling module is used for constructing a dynamic obstacle motion state model; the threat degree evaluation and analysis module is used for constructing a threat degree evaluation model and then outputting a threat evaluation result; the path generation optimization module is used for inputting the threat evaluation result into a path generation network model and generating N groups of candidate paths meeting constraints; performing simulation deduction on the candidate paths, and calculating a comprehensive score of expected energy consumption, a risk value and a task completion degree of each path based on a threat assessment result; and selecting an optimal path according to the comprehensive score, and generating a final obstacle avoidance strategy. According to the method, the defects of response lag and scene adaptability caused by dynamic obstacle prediction misalignment, layered decision delay and fixed parameter stiffness of a traditional obstacle avoidance system are overcome.

The invention relates to the crossing field of industrial control system (ICS) safety and artificial intelligence, and particularly discloses a PLC high-interaction honeypot system based on multi-agent task splitting and RAG enhancement, which adopts a localized multi-agent collaborative architecture based on edge computing and is composed of a high-simulation equipment layer and an intelligent decision-making layer. The high-simulation equipment layer comprises a PLC dynamic mirror image, an HMI interface and a sensor data generator, and an active trapping environment is constructed through protocol fingerprint confusion and virtual and real data fusion technologies. The decision-making layer deploys a multi-agent task scheduling engine, integrates four kinds of agents including protocol analysis, behavior analysis, threat assessment and response generation, and realizes attack context perception and strategy dynamic generation based on a local RAG knowledge base. The load balancing agent dynamically allocates tasks according to equipment resources, and cooperates with offline knowledge update (USB flash disk encryption synchronization threat features) to form a closed-loop defense system, thereby ensuring physical isolation of an industrial network and realizing high-fidelity active defense.

The invention provides an industrial control and infrastructure defense cooperation method and system based on digital twinning, and relates to the technical field of defense cooperation. The method comprises the following steps: based on a digital twinning technology, collecting sensor data in an industrial control system, and identifying abnormal fluctuation of equipment operation to obtain abnormal activity information; the occurrence frequency and intensity of the deviation data are analyzed, and the network data transmission security of the industrial entity is optimized to obtain security protocol optimization configuration; reconfiguring the network access authority, and monitoring the new access mode and the data flow in real time to obtain an access control optimization state; monitoring network behaviors by using an isolated forest algorithm, and quantitatively evaluating potential internal and external threats to obtain a threat evaluation update log; and adjusting the data acquisition frequency and the processing process based on the update log, and obtaining a dynamic cooperative defense mechanism by updating network defense and response rules. According to the invention, the early warning and response capability of the industrial control system can be optimized.

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.

A computerized system for dynamic cybersecurity policy using AI-based contextual adaptive learning includes an AI system that evaluates business contexts, risk tolerance, and productivity impact to generate threat intelligence assessments. The system includes a Contextual Adaptive Learning module that dynamically adjusts cybersecurity policies based on threat assessments to create security workflows. A Cybersecurity Mesh Development module that integrates policies across security frameworks. A Dynamic Scenario Catalog module that updates policy adjustments based on threat intelligence. An Automated Workflow Orchestration module that creates and refines security workflows for optimal efficiency. A Policy Recommendation and Automation module that generates prioritized security recommendations and automates policy changes based on organizational risk profiles and current security controls. This system harmonizes security policies while considering business context, risk, and productivity impacts.

The invention relates to the technical field of unmanned aerial vehicle countering, and discloses an unmanned aerial vehicle countering method and system based on image depth recognition, and the method comprises the steps: carrying out the real-time monitoring of a target airspace through a camera device, and obtaining a monitoring image; identifying the monitoring image according to the target identification model, judging whether an unmanned aerial vehicle exists, and if so, analyzing the monitoring image according to a feature matching algorithm, and judging whether the unmanned aerial vehicle is a suspicious unmanned aerial vehicle; if the unmanned aerial vehicle is a suspicious unmanned aerial vehicle, calculating a three-dimensional coordinate of the suspicious unmanned aerial vehicle by adopting a triangulation method, and predicting a flight path of the suspicious unmanned aerial vehicle according to the three-dimensional coordinate to obtain a predicted flight path; and performing threat assessment on the suspicious unmanned aerial vehicle according to the predicted flight path, determining a threat level, and triggering a corresponding countering strategy. The unmanned aerial vehicle identification and positioning accuracy is effectively improved, the suspicious unmanned aerial vehicle is accurately hit, and an effective solution is provided for airspace safety protection of an important area.

The present disclosure relates to cybersecurity architectures and systems for assessing and quantifying security threats and risks associated with machine-readable codes, such as quick response codes, barcodes, data matrix codes, and other types of codes. A security application comprises a multi-context threat assessment system configured to analyze a broad spectrum of risk assessment attributes across multiple contexts. These contexts relate to the machine-readable code itself, target network resources identified by the code, entities affiliated with the code, end-users interacting with the code, and enterprise systems policies. The system can evaluate various risk assessment attributes for each of these contexts to more accurately quantify potential security risks associated with the machine-readable codes. The security application further includes an API for extending its threat assessment capabilities to various digital ecosystems and an AI-powered learning network comprising language models and computer vision systems to enhance threat detection and risk quantification capabilities.

The invention discloses an unmanned vehicle azimuth end-based object autonomous detection obstacle avoidance intelligent identification system, which comprises the following modules: a sensing data acquisition module, which is used for acquiring and preprocessing multi-source sensing data and generating an environment sensing data set; the feature extraction module is used for inputting the environment perception data set into the Motion-DETR dynamic perception model to generate a target object feature set; the perception graph construction module is used for constructing a dynamic environment perception graph; the threat evaluation module is used for calculating threat levels, generating an azimuth end threat level matrix and establishing a dynamic risk distribution diagram; the path extension optimization module is used for dynamically selecting an extension direction by adopting a path extension optimization method introducing dynamic risk cost; the path cost optimization module is used for dynamically optimizing the expansion structure according to the principle of minimum accumulated path cost value; and the path output module is used for generating an optimal safe driving path in the dynamic environment. The invention provides an intelligent obstacle avoidance path planning method fusing perception mapping and risk optimization.

The invention relates to the technical field of unmanned aerial vehicle detection, and discloses a common-aperture unmanned aerial vehicle detection method and system, and the method comprises the steps: obtaining multi-sensor original data and a state data set, and carrying out the standardization; in combination with the state data set, performing state updating and de-noising processing; if data missing or abnormal fluctuation is detected, filling and removing and time sequence storage are carried out, and a historical track sequence is formed; performing feature calculation to obtain a motion feature vector; classifying by using a support vector machine to obtain a target motion mode classification label; if the trajectory is an evasive type or an aggressive type, triggering a high-priority tracking process, and training by using a long-short-term memory network to obtain a predicted trajectory coordinate point set; unifying the coordinates to a global reference framework to obtain a global prediction trajectory, and calculating an intersection point of the global prediction trajectory and a preset no-fly zone to obtain an intersection detection result; generating a real-time alarm signal; and performing comprehensive threat assessment to obtain a threat target detection conclusion. According to the method, the threat assessment accuracy can be improved.

The invention relates to the technical field of unmanned aerial vehicle obstacle avoidance, and discloses an unmanned aerial vehicle autonomous obstacle avoidance method based on deep learning and binocular vision. According to the method, original data streams of a left view and a right view are acquired through a binocular vision acquisition terminal, and stereoscopic vision feature representation is extracted through parallel convolutional coding branches of a deep neural network model; inputting into a three-dimensional space reconstruction module to generate a dense depth map and an obstacle initial position coordinate set; the dynamic obstacle analysis engine calculates a dynamic threat evaluation index by combining real-time flight attitude parameters of the unmanned aerial vehicle, and the space-time trajectory prediction model calculates future moving path probability distribution of the obstacle according to the dynamic threat evaluation index; and fusing the distribution with preset navigation path planning data to generate a three-dimensional obstacle avoidance course correction vector, converting the three-dimensional obstacle avoidance course correction vector into a flight control instruction set, and downloading the flight control instruction set to an execution module. According to the method, the reliability and adaptability of autonomous obstacle avoidance of the unmanned aerial vehicle in a complex dynamic environment are improved, and a powerful guarantee is provided for safe navigation of the unmanned aerial vehicle.

The invention discloses a computer network security data processing method and system based on artificial intelligence, and the method comprises the steps: building a multi-channel deep learning fusion model, extracting spatial local features in a traffic sequence through employing a 1D-CNN one-dimensional convolutional neural network, capturing a long-range time sequence dependence relation between log events, and carrying out the recognition of the long-range time sequence dependence relation between log events; modeling the user operation behavior sequence based on an LSTM (Long Short-Term Memory) network, and fusing the feature weight by using an attention mechanism to obtain a fused feature vector; inputting the fusion feature vector into a classifier established based on an OS-ELM online sequence extreme learning machine to perform real-time threat assessment, and outputting a probability index of network attacks occurring in a short time in the future; and generating a cooperative defense decision according to the network attack probability index, and sending the cooperative defense decision to security equipment for execution. Excessive defense or insufficient protection is avoided, and the cooperation efficiency of safety equipment is remarkably improved.

The invention relates to the technical field of aviation safety, and discloses a multi-modal data fused anti-collision early warning method, device and equipment for a carrier and a medium, and the method comprises the steps: obtaining multi-modal data which comprises radar point cloud data and a photoelectric image; respectively preprocessing the radar point cloud data and the photoelectric image to obtain radar effective point cloud, a semantic mask and confidence; performing cross-modal filtering fusion based on the radar effective point cloud, the semantic mask and the confidence coefficient to obtain a multi-dimensional feature point cloud; and constructing a threat assessment model based on the multi-dimensional feature point cloud, and performing early warning on collision prevention of the carrier based on the threat assessment model. According to the invention, the automatic detection of the threat target in the flight of the aerial carrier is realized, the measurement distance is farther, the measurement precision is higher, and the problems of short anti-collision detection distance and low detection precision in the prior art are solved.

The invention discloses a multi-modal target fusion and evaluation method, which comprises the steps of performing alignment preprocessing on multi-source observation data to generate multi-source time alignment observation data and multi-source sensor state data; executing single-source target detection and initial threat estimation, and constructing single-source target state data and initial target threat evaluation data; based on the single-source target state data, the initial target threat assessment data and the multi-source sensor state data, executing multi-sensor target track association and fusion by applying threat consistency constraint, and generating multi-sensor fusion target model data; and based on the multi-sensor fusion target model data and the multi-source sensor state data, executing a multi-sensor collaborative scheduling decision by applying threat reduction income evaluation, and determining multi-sensor scheduling scheme data. According to the method, deep coupling of association and scheduling on the threat assessment task is realized, and the association accuracy and the scheduling decision effectiveness are improved.

The invention relates to the technical field of data security, in particular to a quantum cryptography migration resisting method and system for a power system, and the method comprises the steps: carrying out the quantum threat risk assessment of a communication link based on the layering and partitioning architecture features of the power system; obtaining attribute data of the encryption component, and performing parallel migration risk analysis on the encryption component in combination with a quantum threat risk assessment result to generate an anti-quantum migration risk matrix; carrying out compatibility evaluation on a traditional public key algorithm and an anti-quantum cryptography algorithm based on algorithm features, and constructing a double-track encryption migration strategy; dynamically adjusting a double-track weight ratio, and constructing a double-track encryption hierarchical migration strategy based on a control hierarchy to which a communication link belongs; and executing the double-track encryption layered migration strategy and monitoring the migration effect, and performing self-adaptive correction on the double-track encryption layered migration strategy based on the migration effect. According to the method, a quantum threat assessment and double-track encryption layered migration mechanism is constructed, so that safe and smooth migration of the power system under the threat of quantum computing is realized.

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.