612 results about "Anomaly detection algorithm" patented technology

The invention provides a power cable fault detection method, which comprises the following steps of: obtaining a cable material, a laying environment and an insulation aging degree attribute parameter of each monitoring segment according to a pre-established power cable segment model, determining a correction coefficient of the segment model according to load current magnitude at different operating temperatures, and determining a power cable fault detection result according to the correction coefficient; the correction coefficient is used for adjusting the electrical parameter of each segment to adapt to the actual operation condition; in the fault positioning process, a time sequence analysis method is introduced, the dynamic change trend of current and voltage data before and after a fault occurs is considered, the fault occurrence moment is recognized through an anomaly detection algorithm, and the position coordinates of a fault point are corrected according to the difference between the cable material and the laying environment in combination with a judged fault area.

The invention provides a multi-source software supply chain intelligent analysis method and system. The method comprises the following steps: analyzing software component identification information submitted by a user, extracting attribute characteristics, positioning node paths among components, and generating a weighted dependency chain list containing direct and indirect dependency chains; a Bayesian network model is combined with a predefined risk index system, the risk score of each dependency relationship chain is calculated, potential supply chain threat points are identified through an anomaly detection algorithm, and a risk report is generated; the report covers the overall risk score and the threat point of the key link or component; based on the report, drawing a comprehensive risk map by using a multi-dimensional visualization algorithm, and displaying component groups with similar risk modes; and a genetic algorithm and a simulated annealing algorithm are fused to explore a high-risk area, an adjustment suggestion is proposed, and a risk mitigation strategy is generated to reduce the overall risk score. According to the technical scheme provided by the embodiment of the invention, the security and integrity of the multi-source software supply chain are improved.

The invention provides a satellite measurement and control data anomaly detection and intelligent diagnosis early warning method and system, and relates to the technical field of satellite measurement and control, and the method comprises the steps: obtaining multi-source heterogeneous data in a satellite measurement and control system, and carrying out the preprocessing of the multi-source heterogeneous data; constructing a multi-level space-time fusion network model, inputting the preprocessed multi-source heterogeneous data into the multi-level space-time fusion network model, and extracting high-dimensional space-time features; based on the extracted high-dimensional spatial-temporal features, designing a dynamic adaptive anomaly detection algorithm, and performing anomaly detection on the satellite measurement and control data; according to an anomaly detection result, performing intelligent diagnosis by using a pre-constructed multi-level satellite system knowledge graph; based on the intelligent diagnosis result, multi-level early warning information is generated; a distributed edge computing architecture is designed, and partial data processing and anomaly detection tasks are deployed to a ground station and a satellite end; and presenting the generated multi-level early warning information through a visual interface, and automatically triggering a corresponding emergency plan according to the early warning level.

The invention discloses an electrical equipment fault diagnosis and prediction analysis system, which relates to the field of intelligent operation and maintenance of a power system and comprises an acquisition and preprocessing module, an extraction fusion module, a fault diagnosis modeling module, a prediction evaluation module and an update feedback module. According to the invention, through fusion of structured sensing data and unstructured image data, multi-modal depth feature joint representation is realized, and the accuracy and robustness of fault identification are significantly improved; a fusion time sequence prediction model is introduced, and a health degree scoring system is combined, so that accurate prediction of key parameter trends and quantitative estimation of the residual life of equipment are realized; a transfer learning and incremental learning mechanism is adopted, when a new fault or small sample data appears, model parameters can be quickly updated, and efficient adaptation to a new scene is achieved; a data alignment mechanism with time-space synchronization and an auto-encoder anomaly detection algorithm are constructed, and the multi-source heterogeneous data processing capacity and the real-time fault early warning capacity are remarkably improved.

The invention discloses a livestock health state real-time monitoring method and system based on the Internet of Things, and the method comprises the steps: collecting livestock biological data and environment parameters through a biological chip ear tag and an environment sensor, and carrying out the preprocessing, and obtaining a standardized multi-dimensional livestock data flow; in combination with image data provided by a video monitoring system, identifying livestock individuals and analyzing behavior features, and fusing biological data and visual data to generate livestock individualized feature vectors; constructing an individualized health baseline model in combination with historical health records; comparing the deviation degree between the current state and the health baseline through an anomaly detection algorithm, and identifying a potential health problem; early warning is performed according to the abnormity severity level, intervention suggestions are generated, early warning information is pushed to the breeding personnel through multiple channels, and response measures and results are recorded to form closed-loop management. According to the invention, accurate real-time monitoring of the health state of the livestock is realized, the accuracy and timeliness of anomaly detection are improved, the breeding risk is reduced, and the production efficiency of animal husbandry is improved.

The invention belongs to the technical field of battery monitoring, and discloses an electric vehicle power battery health condition real-time monitoring method and system, and the method comprises the steps: obtaining multi-parameter data such as voltage, current, temperature, internal resistance and the like through a battery management system, and carrying out the synchronous collection of the data through a preset sampling frequency, and obtaining a multi-dimensional time series data set; aiming at the multi-dimensional time sequence data set, adopting a feature extraction algorithm to separate dynamic change features of voltage, current, temperature and internal resistance to obtain a multi-parameter feature set; if the deviation of the battery health state output by the dynamic mapping relation model exceeds a preset threshold value, analyzing a fluctuation mode in the multi-parameter feature set through an anomaly detection algorithm, and judging whether a potential degradation risk exists or not; and according to the comprehensive representation of the battery health state, a time sequence prediction algorithm is adopted to analyze the future change trend of the multi-parameter feature set, and a short-term prediction value of the battery health state is generated.

The invention provides a software supply chain risk detection protection method and system, and the method comprises the steps: obtaining a third-party component from a software supply chain, and analyzing the multi-level dynamic behaviors of the third-party component; predicting a risk behavior pattern by using a Bayesian network algorithm, and constructing a behavior pattern library; monitoring a behavior of the third-party component during operation, and recording a behavior data stream; identifying an abnormal behavior deviating from a normal behavior mode by applying a time sequence anomaly detection algorithm of deep learning; evaluating risk levels of the abnormal behaviors by using a graph neural network, and generating an abnormal behavior list in combination with relevance and propagation paths among the behaviors; according to the list, a response strategy in the safety control measure library is dynamically adjusted through an adaptive strategy selection algorithm, and safety control measures are formulated for the third-party component. According to the invention, the identification and prediction capabilities of potential risk behaviors are improved.

The invention discloses a full-life-cycle auditing tracking system and method, and belongs to the technical field of data tracking backtracking, and the method specifically comprises the steps: carrying out the global unique identifier distribution of personnel configuration data during the first collection, embedding a timestamp, an operation identifier and a link identifier in each life cycle node of the data, constructing a real-time auditing model, and carrying out the real-time auditing of the data; through data change monitoring, behavior pattern recognition and an anomaly detection algorithm, each data operation is recorded in real time and compared with a preset auditing strategy, risk early warning is triggered, block chain evidence storage is established at a key operation node, a multi-block chain link relation network diagram is constructed based on multiple block chains, and when an abnormal node occurs, a data life chain is generated. Predecessor nodes of the abnormal nodes are backtracked, recursive check is carried out on each predecessor node, and an abnormal source and a responsibility subject are positioned; according to the method, the multi-step association risk can be found, the situation that tracking cannot be achieved during tracking and backtracking is prevented, the missed judgment rate is reduced, and the accuracy and efficiency of tracking and backtracking are improved.

The invention discloses an intelligent film-drawing and unwinding control method and system based on real-time tension, and relates to the field of automatic control and intelligent manufacturing, and the method comprises the following steps: ensuring normal starting of equipment and system states through equipment self-inspection and technological parameter input; tension and speed data are collected in real time and preprocessed, and support is provided for fuzzy PID control; the parameters of the fuzzy PID controller are dynamically adjusted by calculating the tension error and the change rate of the tension error so as to adapt to different membrane material characteristics and unwinding working conditions; an improved genetic algorithm is used for optimizing parameters of a PID controller, overshoot is minimized, the adjusting time is shortened, and the robustness and the anti-interference capability of the system are improved; a closed-loop control system is constructed, and the stability of unwinding tension is ensured through real-time feedback signals; and the system state is monitored in real time in combination with an anomaly detection algorithm, and a control strategy is automatically adjusted or fault protection is performed. The control method disclosed by the invention can be widely applied to the fields of film unwinding and automatic production lines, and has a relatively high intelligent level.

The invention, which belongs to the technical field of intelligent breeding equipment, discloses an underwater bionic robotic fish school cooperative breeding environment dynamic monitoring and regulation system comprising a bionic robotic fish body core module, a group cooperative control core module and an intelligent regulation core module. The bionic robotic fish module simulates a real fish swimming mode and carries various water quality sensors to autonomously cruise, so that interference to cultured fishes is reduced; the group cooperation module utilizes a cluster intelligent algorithm and an underwater acoustic communication technology to realize coordinated movement of multiple robotic fishes and full coverage of a monitoring area; the intelligent adjusting module is based on an abnormal detection algorithm, and integrates a miniature oxygenation device, a pH adjusting device and the like to achieve precise regulation and control of the local environment. By the adoption of the system, dynamic sensing and active regulation and control of the culture environment are achieved, a bionic monitoring regulation and control solution is provided for modern aquaculture through intelligent cooperation of the robot fish school and the management system, and the system has the important value of improving the culture efficiency and improving the growth environment.

The invention provides a water turbine anomaly detection and fault diagnosis method based on multi-sensor data fusion, and the method comprises the steps: collecting stress data, audio signals, vibration signals and working condition data during the operation of a water turbine, and carrying out the preprocessing of the collected data, including data cleaning, alignment and normalization; extracting time-domain, frequency-domain and time-frequency-domain features of the preprocessed stress data, audio signals and vibration signals, forming a high-dimensional feature vector in combination with the preprocessed working condition data, and performing dimensionality reduction on the high-dimensional feature vector by using principal component analysis; performing anomaly detection on the dimensionality-reduced high-dimensional feature vector according to a clustering algorithm, a local outlier factor algorithm and an anomaly detection algorithm constructed by an auto-encoder neural network, and generating a comprehensive anomaly score in combination with a preset weight to obtain an anomaly detection result; and when the water turbine is in an abnormal state, the fault type is diagnosed through the similarity between the dimensionality-reduced high-dimensional feature vector and the historical fault feature template.

The invention relates to the field of data processing, in particular to an operation state monitoring method and system for a connector production machine, and the method comprises the steps: obtaining operation state data of the connector production machine, carrying out the preprocessing, taking preset local reference data as a benchmark, determining the relative change degree of data of each dimension, and calculating the abnormal fluctuation degree. And meanwhile, performing first-order difference symbolization processing on the local reference data, and counting symbol continuous lengths to form a sequence so as to analyze the abnormal tendency. And combining the abnormal fluctuation degree and the tendency to construct a fault risk index, and calculating a local abnormal weighting factor at each moment. And finally, based on the factor, improving an LOF anomaly detection algorithm and calculating and correcting an anomaly score, thereby effectively monitoring the operation state of the connector production machine. According to the method, the LOF algorithm is improved, the defects of a traditional algorithm in time sequence and multi-type data anomaly detection are overcome, the monitoring accuracy and the fault early warning capacity are improved, and the production efficiency and reliability are improved.

The invention relates to the technical field of medicine sales inventory management, and discloses a medicine sales inventory management system based on artificial intelligence, which comprises a multi-modal anomaly detection module, a multi-modal anomaly detection framework is constructed, and medicine sales anomaly is detected by using an isolated forest algorithm, a depth auto-encoder and a sequence anomaly detection algorithm; the causal structure learning and attribution analysis module is used for constructing a drug sales causal structure learning and attribution analysis system and analyzing abnormal reasons by adopting a NOTEARS algorithm and an SHAP attribution algorithm; the intervention strategy generation and evaluation module is used for constructing an intervention strategy generation and evaluation system and simulating different strategy effects through a causal intervention formula; and the inventory strategy optimization and execution module is used for implementing inventory strategy optimization and execution and dynamically adjusting inventory management. According to the invention, automatic detection, cause attribution and intelligent correction of sales abnormity are realized, and a closed-loop management system is formed.

The invention belongs to the technical field of vehicle state detection and early warning, and particularly relates to a vehicle state monitoring and early warning method and system.The monitoring and early warning method comprises the steps that a sensor obtains real-time data, an anomaly detection algorithm is applied, and an abnormal event is marked; calculating an information priority according to the abnormal severity and the driving scene, and distributing the information priority to a high-priority queue; multi-mode early warning is generated, and high-frequency sound and vibration are used during high-speed driving; extracting a voice prompt, and generating voice waveform data through a voice synthesis module; voice input of a driver is recognized, intention is analyzed, and abnormal information feedback is provided; according to the scene and the abnormal state, interactive output is optimized, and detailed information is displayed during low-speed congestion; dynamically adjusting the interface of the instrument panel, and amplifying the key area in case of abnormal severity; and integrating the image data and the voice data, generating a multi-mode signal, and transmitting the multi-mode signal after rendering processing. The vehicle abnormal information can be timely and accurately transmitted to a driver, and the driving safety is effectively improved.

The invention discloses a fry culture dynamic monitoring and feeding optimization method based on deep learning. The method comprises the following steps: S1, installing a water quality sensor and a camera to collect water quality data and fry image data; s2, performing feature extraction on the fry image data by using an improved convolutional neural network; s3, analyzing a growth trend and an activity mode of the fry by using a long-short-term memory network to obtain a comprehensive score; s4, obtaining a feeding strategy based on a multi-target adaptive PPO algorithm, and forming a personalized feeding plan; s5, performing detection and early warning on abnormal data through an anomaly detection algorithm, and adjusting a breeding environment; and S6, continuously optimizing the fry culture dynamic monitoring model. According to the method, an efficient and scientific optimization scheme can be provided in dynamic monitoring and feeding of fry culture, and remarkable technical value and economic benefits are brought to practical application.

The invention relates to a navigation equipment health management method based on an SAITS algorithm and a digital twin platform. The method comprises the following steps: acquiring navigation equipment operation data; adopting an improved SAITS algorithm to carry out interpolation on missing data, and predicting to obtain future navigation equipment operation data; identifying current and future abnormal states of the navigation equipment by adopting a self-adaptive anomaly detection algorithm; constructing an equipment fault mode knowledge graph according to the identified equipment exception; simulating the operation of the navigation system by using a digital twin platform, verifying the confidence coefficient of equipment abnormity, and generating a health assessment report; if the confidence coefficient is not smaller than a confidence coefficient threshold value, generating an exception coping strategy in combination with an equipment fault mode knowledge graph; incremental learning or retraining is carried out on an adaptive anomaly detection algorithm to improve the detection accuracy. The method has remarkable advantages in the aspects of improving the navigation equipment management efficiency, reducing the fault risk, optimizing the maintenance cost and the like, and is particularly suitable for water transportation scenes with strict requirements on reliability and real-time performance.

The invention relates to a digital twinning-based production line simulation method and related equipment, and the method comprises the following steps: carrying out the historical operation data mining of a target production line, and obtaining the historical time sequence operation data; performing dynamic logic simulation construction on the target production line based on the mapping space digital topological structure and the historical time sequence operation data to obtain a production line operation logic model; analyzing whether an abnormal position exists in the production line operation logic model or not through a preset anomaly detection algorithm, and if yes, labeling the abnormal position to obtain an abnormal labeling position; wherein the abnormal labeling position is a logic conflict point position and a non-optimal configuration area; and performing flow reconstruction on the production line operation logic model based on the abnormal labeling position to obtain a target virtual three-dimensional model, thereby solving the technical problems that the mapping relation between the physical entity and the digital model of the production line is not accurate enough and the actual operation state of the production line is difficult to reflect truly.

The invention relates to the technical field of smart communities, and discloses a smart community intelligent security scheduling management method based on deep learning, which comprises the following steps: acquiring video monitoring data and access control record data of a plurality of communities, cleaning the data, unifying the format and content of the data by adopting a data standardization technology, and storing the unified data into a database; the method comprises the steps of obtaining standardized community security and protection data, mining a personnel flow mode and a vehicle track mode among different communities by adopting a graph algorithm analysis technology on the basis of a community relation network to obtain a cross-community personnel flow mode and a vehicle track mode, and obtaining a cross-community security and protection result according to analysis results of the cross-community personnel flow mode and the vehicle track mode. And an anomaly detection algorithm is adopted to identify potential safety hazards, and if an abnormal condition is detected, early warning information is generated. According to the invention, deep correlation analysis of cross-community security data is realized, and the intelligent level of community security management is effectively improved.

The invention provides an internal network threat intelligent analysis and prediction method and system based on a large language model, and relates to the technical field of network technology and security. According to the method, the long-range dependency processing capacity of the LLMs is utilized, global and long-range dependency analysis of the user behavior sequence is achieved, and the limitation of a traditional fixed session unit is broken through. Rich semantic features and context information are extracted from original log data through the deep semantic analysis ability of LLMs, and the feature expression ability is greatly improved. In combination with the reasoning capability of LLMs and a traditional anomaly detection algorithm, context-based deep semantic analysis is realized, and the recognition capability of complex threats is improved. In the aspects of real-time monitoring and response, the LLMs are introduced to assist in complex event processing and dynamic rule generation, and the intelligent level and efficiency are improved. Aiming at challenges of data management and analysis, an LLMs is utilized to enhance a data management strategy, a storage structure is optimized, and an intelligent data life cycle management and query interface is provided.

A data classification method includes following steps. Text samples are obtained from a dataset. The text samples are converted into text embeddings in a semantic space. An outlier-inlier ranking of the text samples is generated based on an outlier detection algorithm according to distances between the text embeddings in the semantic space. Partial samples are selected from the text samples according to the outlier-inlier ranking. A manual input command is received to assign manual-input labels on the partial samples. A prompt message is generated according to the partial samples with the manual-input labels and unlabeled samples of the text samples. The prompt message is provided to a generative pre-trained transformer model for generating inlier-outlier prediction labels about the unlabeled samples.

The invention discloses an AI-based database operating system risk prevention and control system and method, belongs to the technical field of deep learning, and aims to solve the technical problem of how to comprehensively identify, prevent and control database operation risks and improve data management efficiency. Comprising a data processing module for performing data preprocessing on historical operation logs; the risk identification module is used for carrying out grammar and semantic analysis on the SQL statements in the processed operation logs, constructing risk characteristics of the SQL statements, carrying out risk prediction through a trained risk identification model and outputting risk levels; the risk response module is used for carrying out risk warning level by level and carrying out classification and priority ranking on the risk warning based on the Bayesian network; and the user analysis module is used for monitoring user behaviors in real time based on the user portrait and an anomaly detection algorithm, forming alarm information based on a detection result and pushing the alarm information.

An anomaly detection-based attention purification graph defense method is provided, which includes: Step 1: selecting an anomaly detection algorithm, passing an input graph into an anomaly detection module, and obtaining potential abnormal edges, wherein the obtained result is specifically a two-dimensional matrix named linkpred; Step 2: passing the linkpred matrix obtained in Step 1 and other required training parameters into an ADGAT model for training, and purifying an attention layer in the training process by linkpred; Step 3: using the trained model to generate graph node features to complete a specific downstream graph task. In the downstream graph task with high security requirements, the method can enhance the robustness of the model on the premise of defending the inherent noise and malicious disturbance in graph data and ensuring the performance of GNNs.

The invention provides an electric energy metering system operation state comprehensive evaluation method and system, and the method comprises the steps: receiving a real-time energy consumption data stream, carrying out the processing through an adaptive threshold anomaly detection algorithm and a Bayesian inference method, and generating a real error range estimation corresponding to each data point; generating a target prediction result by using the time sequence prediction model in combination with historical same-period data, seasonal factors and a reinforcement learning mechanism; comparing the target prediction result with the real-time energy consumption data flow to obtain a comparison result, and generating a comprehensive performance fluctuation evaluation result based on the comparison result and the multi-dimensional state space model; constructing a comprehensive evaluation index system by using an integrated learning algorithm and a fuzzy logic algorithm, and obtaining a comprehensive score corresponding to each ammeter by using the comprehensive evaluation index system; according to the method, the prediction accuracy of the electric energy metering system, the comprehensiveness of health condition evaluation and the scientificity of maintenance decision are improved, and support is provided for efficient management and optimal scheduling of an intelligent power grid.

The invention provides a cluster reliability test method based on fault simulation, which comprises the following steps: acquiring injection parameter information, and performing fault simulation in a cluster based on the injection parameter information and a fault transfer model to obtain a complex fault scene; key performance index records are obtained in real time based on the complex fault scene; executing a reliability detection step based on a preset reliability detection model and the key performance index record, and obtaining a data analysis report; and obtaining a reliability score, an influence analysis result and a risk prediction result based on a data analysis report, a weighted scoring algorithm, an anomaly detection algorithm and linear regression analysis, and completing reliability detection of the cluster. According to the cluster reliability test method and system based on fault simulation provided by the invention, the test efficiency and the reliability detection level are greatly improved, the fault simulation is carried out in the cluster and the reliability detection model is combined, so that the accurate quantification of the reliability detection result is realized, and the reliability detection efficiency and the detection capability are improved.

The invention relates to a standardized project management and intelligent professional scheduling system, and belongs to the technical field of project management and human resource intelligent scheduling. The project standardization module is disassembled into standardization task steps through a business process association rule mining algorithm, and project complexity and resource tensity are adapted by using a task attribute dynamic weight algorithm; the archive matching module constructs professional archives, extracts feature vectors through a multi-criterion decision-weighted bipartite graph matching algorithm, and generates optimal matching pairs in combination with adaptive weight adjustment and an integer linear programming model; the dynamic scheduling module plans a task execution scheme according to a resource constraint scheduling mechanism, and realizes efficient scheduling in combination with a skill supply and demand prediction algorithm and calendar integration; and the quality optimization module adopts a deliverable anomaly detection algorithm to monitor compliance, feeds back an iterative matching and scheduling strategy through a time sequence prediction optimization algorithm, and perfects a skill map based on a map increment updating algorithm. The system realizes a project full-process closed loop.

The invention relates to the cross technical field of computers and livestock breeding, and discloses an intelligent nutrition mode management method and system based on HAP multi-modal data, and the method comprises the steps: obtaining the multi-source heterogeneous HAP data of livestock, and carrying out the feature extraction and feature fusion of the multi-source heterogeneous HAP data; utilizing a controllable generative adversarial network to generate enhanced HAP feature data and an HAP feature data sequence simulating a physiological process; early abnormal signals are detected through an abnormal detection algorithm, and multi-modal attribution analysis is carried out; continuously optimizing the nutrition demand prediction model and the feeding strategy optimization model; according to the method, by effectively utilizing the neglected unstructured information in the past and combining the multi-modal fusion analysis, the early-stage discovery capability of the weak signal for indicating the physiological abnormality or nutritional imbalance of the livestock and poultry is improved.

The invention discloses a multi-field business collaborative management system and method based on a block chain, and relates to the technical field of business collaborative management. Standardization rules are introduced, the field name, the data type and the unique identifier of data are processed in a unified manner, and by labeling sources, timestamps and operation records, the business collaborative management efficiency is improved; an anomaly detection algorithm based on statistical distribution, machine learning and rule verification is adopted, consistency verification and repeated data cleaning are combined, key features are extracted through principal component analysis, sparse coding and a deep learning embedding model, semantic alignment and feature fusion of texts, numerical values, time sequences and image data are achieved through multi-modal modeling, and the feature fusion of the text, numerical values, time sequences and image data is achieved. Cross-modal unified data representation is generated; a knowledge graph technology and a block chain technology are combined, a semantic relationship between fields is constructed through semantic alignment, an alliance chain is used for recording data circulation and operation, dynamic access authority management is realized in combination with an intelligent contract, and transparency and non-tampering performance of data are guaranteed.

The invention relates to the technical field of edge computing, in particular to an intelligent parking unattended vehicle access management system and method based on edge computing. The method comprises the following steps: a vehicle detection positioning unit obtains multi-modal data based on a multi-modal sensor array module, and realizes real-time positioning of a vehicle and real-time updating of a parking space state through a Kalman filtering algorithm in combination with a QBCN information interference variable; the license plate recognition unit completes license plate feature extraction and recognition on the basis of a multispectral imaging technology and a lightweight CRNN model under the condition of abnormal illumination. The edge calculation decision unit combines a real-time rule engine and a space-time anomaly detection algorithm, optimizes a resource allocation strategy, and realizes low-delay decision and localization control; the payment authentication unit adopts a block chain intelligent contract, dynamic rate calculation, multi-factor identity authentication and abnormal payment fusing protection; and the edge cloud collaboration unit realizes model differential updating, cross-domain task scheduling and energy efficiency optimization through federated learning and space-time data compression technologies.

The invention relates to a computer network security detection system and method. The system comprises a data acquisition module, a threat identification module and an attack path analysis module, the data acquisition module is used for collecting network traffic, log files and user behavior data from a target network in real time, and performing preliminary filtering and preprocessing on the data; the threat identification module is used for identifying potential security threats by adopting a multi-dimensional feature analysis and anomaly detection algorithm based on the data content provided by the data acquisition module; and the attack path analysis module performs modeling and analysis on possible attack paths by utilizing a graph theory algorithm based on the detection result of the threat identification module so as to predict the development trend of attack behaviors. The invention further provides a computer network security detection method, real-time monitoring, path analysis and response to network security threats are achieved through cooperative work of all the modules, and therefore the accuracy and effectiveness of network security protection are improved.

The invention provides an automatic perception model method and system for illegal access in a network security isolation area, and belongs to the technical field of computer systems based on specific calculation models.The method comprises the steps that firstly, a network topological graph matrix of the security isolation area is constructed, an equipment asset list is established, and then distributed flow collection nodes are deployed to obtain real-time network data; a deep packet detection technology is used for extracting features to establish an equipment behavior baseline library, a multi-target risk assessment function is used for carrying out risk grade division on equipment, a multi-layer perceptron and a time sequence anomaly detection algorithm are used for identifying abnormal communication, and an equipment fingerprint identification mechanism based on physical layer characteristics is established to verify the legality of the identity of the equipment. A security isolation intelligent sensing network model is utilized to analyze network behaviors, a multi-dimensional abnormal scoring system is constructed to calculate risk scores, a response mechanism based on a rule engine is realized, a federal learning technology can be selectively adopted to optimize the model, and an all-dimensional and multi-level illegal access automatic sensing protection system is formed.