648 results about "Threat level" patented technology

The invention provides a network security penetration detection method and system based on artificial intelligence, and the method comprises the steps: firstly obtaining real-time network flow data containing multi-dimensional communication behavior records and protocol metadata of a target network system, and then extracting network behavior sequence features capable of describing communication behavior time relevance and a protocol interaction mode from the network flow data; inputting the network behavior sequence features into a pre-trained multi-modal threat prediction model, generating a potential attack vector prediction result containing an attack intention label, penetration path probability distribution and a threat level score, and dynamically verifying the potential attack vector prediction result based on a threat knowledge base, and finally, generating a self-adaptive defense strategy according to the penetration threat detection result, triggering defense interface deployment of a target network system, updating attack mode characteristics and a defense rule set of a threat knowledge base, and realizing intelligent and efficient network security penetration detection and defense.

The invention provides an information network security self-defense method and system based on trusted computing, and the method comprises the steps: constructing a trusted computing platform which comprises the steps: deploying a trusted platform module TPM, building a trusted software stack TSS, and implementing a trusted network connection TNC; designing a self-defense architecture, wherein the self-defense architecture comprises deployment of a network layer, an application layer and a data layer defense system, integration of an intelligent sensing system and establishment of a central control and decision center; network traffic and user access behaviors are monitored, and embedded attack behaviors are preprocessed; performing anomaly detection on the network flow and the user access behavior by applying a machine learning algorithm; calculating a real-time risk score based on the anomaly detection result, and generating a security situation awareness report; and based on the security situation awareness report, defining a threat level and a response level, and executing automatic defense.

The invention provides an unmanned aerial vehicle countering plan generation method and system based on an artificial intelligence technology, and the method comprises the steps: analyzing the dynamic monitoring data flow of a target unmanned aerial vehicle, recognizing the flight path characteristics and electromagnetic signal frequency spectrum of the target unmanned aerial vehicle, and constructing a three-dimensional space threat situation model to evaluate the threat level. Decomposing behavior patterns and attack intention parameters of a target unmanned aerial vehicle based on threat levels, generating a dynamic decision factor set in combination with geo-fence constraint conditions and environmental interference factors, generating a plurality of candidate countering plans by matching countering means basic plans in a plan knowledge graph, and constructing a plan logic tree; a multi-dimensional evaluation parameter set is generated based on a three-dimensional space threat situation model, a dynamic decision factor set and a countering efficiency index, collaborative optimization processing is performed on a plan logic tree, an optimal countering plan is generated and converted into an executable instruction set, the executable instruction set is synchronized to a countering device for execution, and the real-time performance and scene adaptability of the unmanned aerial vehicle countering plan are improved.

A system and methods for implementing adversarial-robust compression and reconstruction using a vector quantized variational autoencoder (VQ-VAE) with secure latent space management. The system provides comprehensive protection against adversarial attacks through multi-channel threat detection, adaptive defensive parameters, and coordinated response mechanisms. Input data is continuously monitored for potential threats, and defensive parameters are dynamically adjusted based on detected threat levels. The system implements bounded constraints and hierarchical projections to maintain latent space security while preserving compression efficiency. Multi-stage reconstruction with progressive validation ensures reliable data recovery even under adversarial conditions. The system coordinates defensive responses across all compression and reconstruction processes, implementing various recovery mechanisms when security violations are detected. This approach enables robust compression and reconstruction of time-series data while maintaining protection against various forms of adversarial manipulation.

The invention relates to the technical field of network security, in particular to a computer network information security monitoring method, system and device and a medium, and the method comprises the steps: obtaining encrypted traffic data and application log data in a network environment, and constructing a space-time associated original data set based on the encrypted traffic data and the application log data; performing protocol analysis on the original data set with the time-space association to generate a protocol fingerprint feature vector; inputting the protocol fingerprint feature vector and the application log data into a preset heterogeneous multi-modal analysis model, and obtaining a multi-dimensional security situation assessment result containing a threat level and an attack path; and based on the multi-dimensional security situation assessment result, generating a dynamic defense strategy instruction set through a reinforcement learning algorithm, and issuing a strategy instruction to a network execution node in real time. The method and the device have the effects of realizing real-time accurate detection of encryption threats and constructing a dynamic defense system with balanced security and efficiency.

The invention discloses a power transmission line multi-mode warning system and a power transmission line multi-mode expelling method, belongs to the technical field of power transmission line safety monitoring, and aims at solving the problems that power transmission line invasion target monitoring is not accurate, and the warning and expelling effect is poor. An environment image is acquired through an acquisition unit, and redundancy compensation is carried out on a fault unit. In the aspect of image splicing, a database is constructed by using structural feature points of a power transmission line, rapid projection transformation of a fixed area is realized, incremental feature matching is adopted for a dynamic area, and an environment panoramic image is generated. The method comprises the following steps: establishing a background coordinate system based on an environment panoramic image by aligning a fixed structure region, extracting multi-modal features of an intrusion target, constructing a dynamic trajectory parameter set, completing species classification and behavior recognition, constructing a multi-dimensional evaluation index system, dividing threat levels, and generating a thermodynamic diagram. And finally, according to data such as threat levels, a multi-mode grading warning system is constructed, warning equipment is dynamically adjusted, a target track is tracked, a warning effect is evaluated, and accurate and efficient invasion target expelling is realized.

The invention relates to a multi-defense-area intelligent linkage alarm method based on an AIoT gateway and related equipment, and the method comprises the following steps: fusing the environment data of an Internet of Things sensor of a plurality of defense areas with a video image stream to generate three-dimensional reconstruction data of a defense area scene; and monitoring and analyzing the data, and extracting an environment feature sequence and a trajectory analysis sequence. And when it is detected that the threat levels of the multiple defense areas exceed a threshold value, causal inference is carried out in combination with gateway historical alarm data, and a cross-defense-area association event chain is generated. According to the method and the system, the AIoT gateway is used as a core, then a cooperative response strategy preset by the AIoT gateway is matched, and the SP voice call module is linked through a 4G / 5G network, so that multi-channel alarm pushing is realized, and the technical problem that comprehensive understanding and dynamic modeling of a defense area scene are difficult to form due to the lack of deep fusion and cooperative analysis means between video image data and environment sensor data is solved.

The invention provides a space competition situation threat level evaluation method and system based on credibility weighting, and relates to the technical field of information, and the method comprises the steps: constructing a threat feature vector based on historical orbit data and an operation state, and obtaining an initial target threat probability vector through a deep learning model; then, a credibility weighting coefficient is calculated based on the multi-source situation awareness data, and an initial threat probability vector is corrected in combination with a feature weighting coefficient; and finally, classifying target threat levels by adopting an integrated learning method, determining a target priority in combination with the target orbit parameters and an ant colony algorithm, and outputting a threat target sequence and a situation threat assessment report. By fusing the multi-source situation awareness data and the credibility weighting mechanism, the threat level of the space target can be evaluated more accurately, and the reliability and efficiency of space situation awareness are effectively improved.

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 discloses a financial network security defense method and system based on multiple Agents and a dynamic large model. A detection Agent is deployed in an edge layer, financial network node flow data and system logs are collected in real time, time sequence features are extracted through a lightweight convolutional network, and a preliminary anomaly score is generated. And the cloud layer constructs a decision Agent, receives the feature abstract transmitted by the edge node in an encrypted manner, inputs the feature abstract into a dynamic large model for multi-modal feature fusion, and outputs defense action probability distribution. And the intelligence Agent constructs a cross-institution federated learning network. And constructing a dynamic game engine, constructing a revenue matrix based on the attack cost and the defense revenue, solving a Nash equilibrium strategy, and generating an optimal defense instruction set. And dynamically allocating detection tasks according to the threat level and the edge computing power state. According to the method, efficient acquisition and analysis are realized, the abnormal behavior recognition capability is improved, support is provided for making a defense strategy, the defense strategy is optimized, and the intelligent, automatic and efficient levels of defense are improved.

The invention discloses an airport bird condition automatic detection and intelligent bird repelling device and method, and the method comprises the steps: collecting airport airspace environment data in real time through a multi-source sensor array, carrying out the preprocessing of the environment data, and generating a fusion data set; a target detection model is constructed based on a deep learning algorithm to perform bird target recognition on the fusion data set, and target feature information is extracted and comprises bird positions, movement tracks, the number and types; calculating a threat level of bird activities to flights through a risk assessment model established based on fuzzy logic and a Bayesian network in combination with airport flight take-off and landing time sequence data and target feature information; triggering a grading early warning signal according to the threat grade, and linking the bird repelling equipment to execute directional bird repelling operation. According to the invention, an airport bird situation automatic detection and emergency prevention and control system is formed, the bird strike risk detection precision, the early warning accuracy and the bird repelling efficiency are improved, and the aviation operation safety is guaranteed.

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 provides an anti-unmanned aerial vehicle defense method and system based on radar and audio, and the method comprises the steps: constructing a three-dimensional airspace monitoring network through radar sensors and audio capture devices which are disposed at different heights and positions, carrying out the time-space synchronous multi-modal information fusion, and obtaining signal audio alignment data; a real-time behavior track of the target in a three-dimensional space is generated through a space positioning algorithm, a behavior profile modeling technology is combined with a machine learning algorithm to obtain a behavior profile with unique identification, the behavior profile is analyzed to obtain an analysis result, and a threat level judgment model is constructed according to the analysis result; the threat level judgment model is utilized to evaluate the intention and risk level of the abnormal behavior of the unmanned aerial vehicle to generate a threat evaluation result, and a self-adaptive threat evaluation engine is utilized to generate a target defense scheme; according to the technical scheme provided by the invention, the precision and reliability of target positioning and the intelligent level are improved, and the capability of coping with complex threats is enhanced.

The invention relates to the technical field of network security, in particular to a network security risk assessment method and device based on artificial intelligence and a medium, and the method comprises the following steps: S1, collecting network flow data, user behavior data and device state data, and generating a standardized data set; s2, extracting features related to network threats, and constructing a risk data set with labels; s3, dividing the risk data set with the label into a training set and a verification set, and training the risk assessment model by using a support vector machine algorithm; s4, outputting a risk score and a threat level, and identifying a high-risk event; and S5, performing backtracking analysis on the high-risk events identified in the step S4, and forming a risk assessment report. According to the invention, through multi-source data processing, intelligent feature extraction and support vector machine model training technologies, accurate identification and real-time evaluation of network threats and deep traceability analysis of high-risk events are realized, and the initiative of network security protection is 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.

Systems and methods for ransomware protection in advanced injection-based attacks. The call stack(s) of injected threads are analyzed and a preliminary verdict of benign or malicious can be determined. Additional sensors collect data to associate injected threads with other activities or actors to further estimate the injected thread being benign or malicious. If the threat level is high enough, such as over a given threshold, the preliminary verdict can be determined to be malicious. Subsequently, one or more virtual honeypots are generated for the suspicious threads and the injected thread's response to the virtual honeypots included in anti-ransomware heuristic analysis.

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.

A third-party authentication and encryption method, known as spatio-temporal encryption, is used to protect sensitive information by guaranteeing the origin and integrity of its content for recording or transmission purposes. This method is based on hidden characteristics associated with the message's location and creation date, derived from GNSS signal processing. Without requiring additional dedicated security infrastructure, the method allows for the protection and authentication of any data entered into the information system, anytime and anywhere, and enables the assessment of associated security risks at any system level. Leveraging the various levels of access protection for GNSS signal services, the solution offers different levels of robustness, with complexity and security that can be tailored to the threat level, while minimizing the impact on communication network performance.

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 discloses a network attack path prediction method and system based on a knowledge graph, and relates to the technical field of knowledge graphs, and the method comprises the steps of multi-source heterogeneous data processing, dynamic knowledge graph construction, attack path prediction, high-confidence attack path acquisition and information feedback. According to the method, space-time reference alignment of data is guaranteed, a dynamic knowledge graph is constructed to update entity attributes in real time, a double-storage mode is adopted, the storage efficiency is improved, a graph attention mechanism and an attack path prediction model are constructed, attack paths are predicted, the attack path detection and prediction precision is improved, and the attack path detection and prediction efficiency is improved. The problem of limitation in the current network attack path prediction process is solved, the threat level of the high-confidence attack path is obtained through analysis, information feedback is carried out, updating of the dynamic graph is completed, and the accuracy, reliability and authenticity of a network attack path prediction result are guaranteed.

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 discloses a network security alarm automatic research and judgment method, device and equipment and a medium, and relates to the technical field of artificial intelligence, original alarm data of various network security equipment and systems are acquired, and the original alarm data are processed; the processing comprises data preprocessing, feature extraction and classification aggregation; performing deep packet analysis on the processed original alarm data to obtain an analysis structure and content, and performing abnormal behavior identification and attack purpose derivation on the analysis structure and content to obtain an abnormal behavior attack purpose; the method comprises the following steps: performing threat level evaluation on an abnormal behavior attack purpose based on a threat intelligence source in a preset third-party knowledge base to obtain a network security alarm threat level, performing dynamic research and judgment evaluation on the network security alarm threat level to obtain a dynamic research and judgment evaluation result of network security equipment and system, and sending the dynamic research and judgment evaluation result to the network security equipment and system. According to the invention, deep interpretation and analysis of network security alarms can be realized, real attack events are automatically screened out, and the accuracy and efficiency of alarm research and judgment are improved.

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.

A low-altitude aircraft intelligent identification and emergency countering system relates to the technical field of low-altitude aircraft safety management and comprises a detection unit, an identification unit, a command decision platform, a scheduling unit and a countering unit. The detection unit is used for detecting all low-altitude aircrafts in a prevention and control area and obtaining flight data of all the low-altitude aircrafts; the identification unit is used for receiving the flight data and analyzing and judging the threat level of the low-altitude aircraft based on a fusion algorithm; the command decision platform is used for receiving the threat level of each low-altitude aircraft and sending a disposal instruction to the countering unit; the scheduling unit is used for giving a specific countering equipment scheduling instruction through an intelligent optimization scheduling algorithm according to the disposal instruction; the countering unit controls different countering devices to accurately strike low-altitude aircrafts with corresponding threat levels according to the scheduling instruction; according to the method, the malicious unmanned aerial vehicle can be accurately identified and can be countered in time.

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 image recognition and storage, and discloses a driving video recording method and system based on four-way monitoring. The method comprises the following steps: synchronously acquiring and processing the surrounding environment of a vehicle and the head posture of a driver through a video stream to obtain driving attention reference data; compressing and storing the sight blind area data according to the head posture features to obtain hierarchical video data; performing enhanced mapping on the graded video data to obtain an attention compensation image; performing threat identification and sorting on the compensation image to obtain threat level data; establishing a threat level index structure; and performing hierarchical storage on the data according to the attention coverage degree to obtain a storage data set. According to the method and the device, the technical problem that key scene data may be lost due to lack of consideration of driver attention distribution in a multi-channel video real-time recording process is solved.

The invention provides an intelligent management method and system for a network attack blacklist, and relates to the technical field of network blacklists, and the method comprises the steps: carrying out the threat level evaluation of attack data through building a multi-level network attack feature library in combination with a decision tree and a dual-channel neural network model; implementing a differentiated blacklist strategy on the IP address according to the threat level; threat detection is carried out by adopting a distributed edge node network, and intelligent sharing and updating of a blacklist are realized by utilizing deep learning, a graph neural network and federal learning. According to the invention, accurate identification and dynamic protection of network attacks can be realized, the intelligent level of blacklist management is improved, and the network security protection effect is enhanced.

According to the priority asynchronous processing method and system for network security, multiple data sources are integrated, the threat level of each data packet is evaluated in real time, and the processing priority of each data packet is dynamically adjusted according to the threat level. Different from a traditional static method, the method provided by the invention utilizes an advanced machine learning algorithm, can perform dynamic evaluation in multiple dimensions, and timely identifies and preferentially processes high-risk data packets. The mechanism obviously improves the response capability of the system to novel and complex attacks. Meanwhile, a multi-node cooperative attack response mechanism is introduced, when a certain node detects abnormal traffic, other nodes can be automatically notified, threat situation information can be shared, and therefore rapid cooperative reaction is achieved. The integrated innovation not only enhances the overall security of the network, but also optimizes the data packet processing efficiency, effectively solves the main defects in the traditional network security system, and provides a new thought for constructing a safer and more flexible network environment.

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.