399 results about "Traffic characteristic" patented technology

The invention discloses a security risk autonomous sensing system and method based on a digital twin industrial control network. The method comprises the following steps: performing four-dimensional multi-modal modeling on an industrial control network, and constructing a twin model integrating entities, rules, services and behaviors; constructing a high-fidelity simulation network by utilizing a containerization technology based on the model; deploying a gateway at an actual network boundary, collecting traffic characteristics in real time and identifying suspicious behaviors; performing protocol restoration and behavior reconstruction on the suspicious traffic to generate a behavior sequence; performing anomaly recognition and attack classification on the behavior sequence through a deep learning model; evaluating a risk level; and matching and verifying a defense strategy according to an evaluation result, and automatically synchronizing the defense strategy to a master control network to realize a detection-response-protection closed loop. According to the invention, autonomous identification of suspicious behaviors in the industrial control network and intelligent decision-making of security policies are realized, and the method has the advantages of high simulation, low invasion, closed-loop controllability and the like.

Provided are a method and an apparatus for constructing a road congestion prediction model, a device, a medium, and a product. A road traffic network is defined as a directed weighted graph. Historical dynamic traffic features of each road segment in the road traffic network are obtained as sample data, including recent dynamic traffic features and periodic dynamic traffic features. The sample data is input into a mixture of adaptive graph learners (MAGL) model for learning, and a probability prediction vector is output. The sample data is input into a trend expert model, and a trend distribution vector of a predicted probability of future traffic conditions is output. The periodic dynamic traffic features are fused to determine a periodicity prediction vector. An aggregated logit vector is obtained. An objective function is determined based on the aggregated logit vector. Congestion prediction training is performed to obtain a road congestion prediction model.

The invention provides a working method of a blind guiding robot based on a laser radar and a camera, and belongs to the technical field of mobile service robots. Accurate space structure information is provided through three-dimensional point cloud data, dynamic traffic characteristics such as traffic lights and zebra crossings are captured through visual images, and comprehensiveness of environmental perception is achieved through complementation of the space structure information and the visual images; through combination of environment information and traffic characteristics, passable areas and barrier distribution in a map are dynamically adjusted, dynamic changes such as traffic light switching and pedestrian movement are adapted, and a navigation route can be updated in real time; the characteristics of traffic light states, zebra crossing and the like are fused into path planning, it is ensured that the robot complies with traffic rules, real-time position and speed constraint, dynamic adjustment of motion trails and efficient obstacle avoidance can be carried out on the robot, and the robot can make a decision and optimize a path autonomously according to human social rules.

The invention discloses an urban road traffic entrance and exit influence evaluation method based on big data, and relates to the technical field of urban traffic management. Constructing an urban road traffic network topological graph based on the traffic feature vectors, proposing a dynamic weight graph embedding algorithm, and establishing a road network association mapping model; applying a graph neural network algorithm based on an attention mechanism to the road network association mapping model, performing road node influence factor evaluation, and quantitatively analyzing the road entrance and exit influence degree; fusing influence factor evaluation results, and constructing a multi-dimensional traffic influence evaluation model by adopting a cross-domain ensemble learning method; and according to a performance evaluation result of the multi-dimensional traffic influence evaluation model, generating urban road traffic entrance and exit optimization decision suggestions through an intelligent recommendation algorithm, and completing accurate scheduling of traffic network nodes. The intelligent recommendation algorithm is developed based on reinforcement learning, and reliable optimization suggestions are provided for traffic management decisions.

The invention relates to a multi-scene self-adaptive high-speed obstacle early warning method and device, equipment and a medium. The method comprises the following steps: firstly, acquiring multi-source data of traffic sensing equipment, and carrying out space-time calibration and semantic analysis on the data to obtain a vehicle track feature vector set; thirdly, calculating real-time traffic flow parameters of the gridding area according to the feature vector set, and further generating a traffic feature database; secondly, modeling is carried out on the traffic characteristic database according to time periods and weather, and theoretical speed baseline parameters of all scenes are obtained; and finally, constructing a multi-dimensional feature matrix according to the theoretical speed baseline parameters, inputting the multi-dimensional feature matrix into the classification model to calculate an abnormal probability value, and if it is detected that a continuous abnormal probability value exceeds a dynamic threshold value in a preset time window, generating an early warning signal. By adopting the method, real-time monitoring and early warning of obstacles in multiple scenes of the expressway can be realized, and more effective guarantee is provided for driving safety of the expressway.

The invention discloses a network security analysis early warning system based on artificial intelligence, and relates to the technical field of network security, and the system comprises the following steps: S1, constructing a multi-dimensional state table based on original traffic features; s2, generating an attack graph based on a causal relationship by using the state table; s3, extracting a topological evolution path by using an attack graph; s4, performing joint mode mapping by using the state table and the risk path; and S5, performing neural discriminant embedding modeling by using the composite risk event set. Through setting the attack graph based on the causal relationship, the technology significantly improves the integrity of risk propagation condition modeling, and lays a more stable data foundation for intelligent early warning of network security risk propagation. The joint modeling overcomes the problems of insufficient data granularity and incomplete application conditions caused by only depending on adjacency relation or simple condition judgment in the prior art, and can more clearly describe the internal relation between conditions when risk propagation occurs.

The invention discloses a distributed heterogeneous node optimization method and system, and relates to the technical field of data processing, network traffic characteristic data and data access mode data of a distributed heterogeneous node system are collected, association analysis is performed through a traffic access association model, key association characteristics are extracted, and the network traffic characteristic data and the data access mode data are acquired; inputting the key correlation features into a performance influence evaluation model, generating a system performance influence level, and if it is judged that the system is in a performance reduction state according to the system performance influence level, identifying the performance bottleneck through a performance bottleneck identification model based on causal reasoning. Identifying a performance bottleneck source based on the network flow feature data, the data access mode data and the system performance influence level, inputting key associated features into an optimization strategy generation model based on deep reinforcement learning based on the performance bottleneck source, dynamically adjusting an optimization threshold, and generating a corresponding data fragmentation strategy; and the comprehensiveness and the accuracy of system monitoring are improved.

The invention belongs to the technical field of urban traffic flow prediction, and particularly relates to a traffic flow prediction method and device based on dynamic contrast learning and multi-scale 3D convolution, and the method comprises the steps: obtaining a traffic feature matrix and an environment feature vector based on traffic track data and environment data, mapping the environment feature vector to the spatial dimension of the traffic grid to obtain an environment feature matrix, and splicing the traffic feature matrix and the environment feature matrix to obtain a target space-time matrix; constructing a traffic flow prediction model comprising a multi-scale 3D convolution module, a dynamic contrast learning module, an environmental feature gating fusion module and a time-space deconvolution prediction module, taking the target space-time matrix as input, and constructing a joint loss function based on contrast learning loss, gating fusion loss and prediction loss; therefore, the traffic flow prediction model is optimized. According to the method, the problems of a traditional prediction method in the aspects of capturing nonlinear space-time dependence, dynamic emergency response, environment factor collaborative modeling and the like are solved.

The invention discloses a data processing method and device, equipment and a medium. The method comprises the steps that a detection threshold value is determined based on a first access traffic log; determining an access behavior for the service domain name based on the number of domain name requests determined by the access traffic indicated by the second access traffic log and a detection threshold; when the access behavior aiming at the service domain name belongs to the abnormal access behavior, taking a behavior determination time point corresponding to the abnormal access behavior as a critical time point, and taking a third access traffic log obtained based on the critical time point as an abnormal access traffic log; when a normal traffic feature determined based on the first access traffic log is obtained, determining an abnormal traffic feature of the service domain name based on the abnormal access traffic log, and performing feature comparison on the abnormal traffic feature and the normal traffic feature to obtain an attack feature of the service domain name; a protection policy for the service domain name is determined based on the attack feature. According to the invention, the security and stability of the business server can be maintained.

The invention discloses a multi-intersection traffic signal cooperative control method driven by a cross attention neural network, and the method employs a local cooperative Transform architecture, integrates a decision converter and a shared memory mechanism, and achieves the efficient modeling of a space-time dependence relation of a multi-intersection traffic state. The method comprises the following steps: firstly, through a memory head module, extracting a hidden state of each agent in a sequence modeling process, and updating global shared memory for supporting information interaction and strategy collaboration among multiple agents; and then, a cross attention module is adopted to carry out cross calculation on the local representation and the shared memory of each agent, so that dynamic perception and efficient modeling of the global state of the traffic system are realized. A backbone network of the model is based on Transform, and the understanding ability of time and space traffic characteristics is enhanced through position coding, self-attention and cross attention mechanisms. In the fine tuning stage, only the inserted Adapter module and the output layer are subjected to parameter updating.

A method includes receiving image data captured by a traffic feature detection system. The image data is representative of a traffic feature within an environment. The method includes identifying a type of the traffic feature. The method includes isolating a portion of the image data containing the traffic feature. Based on the portion of the image data containing the traffic feature, the method includes determining a pixel color value for the traffic feature. Based on the type of the traffic feature, the method includes determining an expected pixel color value for the traffic feature. Based on a comparison of the expected pixel color value and the determined pixel color value for the traffic feature, the method includes determining a health status for the traffic feature.

The invention provides a network attack detection method and device, and a protection system, and belongs to the field of network security. After the protection equipment receives network traffic, whether the network traffic is suspicious traffic or not is judged through a traffic detection model on the basis of traffic characteristics of the network traffic, and after the network traffic is determined to be suspicious traffic, the traffic characteristics of the network traffic are further matched with attack characteristics in a characteristic library; and determining whether the network traffic is normal traffic or attack traffic based on a matching result. According to the method and the device, the network traffic is preliminarily screened through the traffic detection model, so that the number of the network traffic needing to be subjected to attack detection based on the feature library is reduced, and the detection efficiency of the protection equipment is improved. Besides, the protection device can allocate more processing resources for the detection of the single network traffic, so that the single network traffic can be detected more comprehensively based on the feature library, and the detection accuracy is improved.

The invention discloses an expressway toll collection robot system, relates to the technical field of expressway toll collection, and constructs a full-process automatic expressway toll collection robot system through collaborative design of a toll collection control module and a special situation scheduling module. The charging control module integrates various types of hardware, realizes automatic identification of vehicle information, fee calculation and passage control, replaces manual work to complete a standardized charging process, and remarkably reduces manual operation errors and time consumption. The special situation scheduling module is used for quickly positioning the type of a special situation through object association relationship analysis for an abnormal state in a charging process, and scheduling staff to intervene; different from a traditional post response mode, the module predicts the severity level of a special situation based on flow characteristics, equipment states and environment characteristics by training a special situation prediction model and configures the number of workers in advance, so that lane congestion can be effectively avoided, and the overall traffic efficiency is improved.

The invention relates to the technical field of network security detection, and discloses a malicious network traffic detection and analysis method based on artificial intelligence. The method comprises the following steps: acquiring network flow data through preset equipment, and constructing a network flow characteristic representation containing a time sequence statistical characteristic and a protocol attribute characteristic; determining a multi-level associated entity of each traffic fragment in combination with the network security knowledge graph, and aggregating the features to generate aggregated network traffic features; performing malicious probability evaluation on the aggregation features, and determining target malicious traffic by means of the maximum response value of the thermodynamic map; determining a similar malicious traffic mode based on the aggregation feature similarity; generating a detection prompt text in combination with the target malicious traffic and the similar mode, and inputting a preset model to output a detection result; and adjusting model noise covariance matrix parameter optimization detection according to the flow dynamic index. The method can comprehensively capture traffic characteristics, mine associated information, improve the accuracy and adaptability of malicious traffic detection, and effectively cope with malicious attacks in a complex network environment.

The invention provides a traffic control decision-making method, device and equipment based on data analysis, and aims to solve the technical problems of high subjectivity, insufficient data value mining, disjunction of strategy and practical application and lack of continuous learning optimization capability in the traditional traffic control decision-making. Through standardized fusion processing of multi-source traffic data and reverse optimization configuration of a traffic feature library, in combination with a progressive effect evaluation and reverse deduction verification mechanism, a multi-time scale effect tracking and strategy evolution trajectory acquisition system is innovatively established, and a decision cycle mechanism with autonomous learning and dynamic adjustment capabilities is constructed. The association relationship between the traffic state evolution rule and the control strategy is systematically analyzed, and finally an intelligent traffic control decision framework based on operation state data driving is formed; scientific and reliable decision support and technical basis are provided for application scenes such as urban traffic fine management, intelligent traffic system optimization control, traffic jam treatment and traffic safety guarantee.

The invention discloses a traffic message service type identification method, device and equipment, a medium and a product. A gateway receives a traffic message; analyzing the flow message to obtain a flow characteristic; and identifying the service type of the traffic message according to the traffic feature. According to the scheme of the invention, the traffic service type is quickly analyzed by grasping the traffic characteristics, the service type can be identified when the service traffic is forwarded, and the service function limitation is reduced.

The invention provides a traffic flow prediction method fusing air pollution factors, and the method comprises the steps: obtaining a traffic topological structure, historical traffic flow and air pollutant concentration data, constructing a traffic feature matrix, an adjacent matrix, a related pollutant concentration attribute matrix, and a mapping function, the air pollutant concentration data are fused through an attribute enhancement unit; adopting K-means clustering to screen pollutants with the highest correlation with the traffic flow, and utilizing an attribute enhancement unit to integrate the pollutants into traffic flow features; and building and training a deep learning model for traffic flow prediction. According to the method, by combining the relevance between the spatial-temporal characteristics of the traffic flow and the air pollutants, the limitation that an existing method only depends on traffic flow data is overcome, and the prediction accuracy is effectively improved.

The invention provides an ultra-large city comprehensive traffic data and intelligent research and strategy system, which establishes a multi-dimensional traffic research and strategy task through a traffic research and strategy task module, and processes the traffic research and strategy task through a traffic characteristic diagnosis module to obtain a traffic characteristic index vector rank and a candidate traffic root cause index set. The traffic traceability analysis module performs further processing to obtain main traffic root cause indexes and measure indexes, and the traffic strategy generation module outputs traffic characteristic indexes according to the main traffic root cause indexes and the measure indexes to optimize a pre-generation strategy; the traffic simulation deduction module optimizes a simulation task item distribution model generated by a pre-generation strategy according to traffic characteristic indexes to obtain a pre-selection result, and the traffic strategy evaluation module and the traffic strategy release module finally perform evaluation and release, thereby solving a problem that a single factor outputs a single execution scheme with relatively high precision. Therefore, when single execution schemes are overlapped and fused into a set of optimal executable solutions for the comprehensive traffic of the super-large city, scheme execution conflicts are easy to occur.

The invention discloses an Internet of Things equipment automatic identification method and device based on a cellular network environment and a storage medium, and the method comprises the following steps: S1, collecting IP flow information of Internet of Things equipment, exporting record data, and storing the record data to a flow data storage module; s2, extracting equipment traffic features, generating feature vectors, and storing the feature vectors in a feature database; s3, training an equipment identification model by using an improved structure sparse variational automatic encoder in combination with a space-time diagram convolutional network, and extracting an equipment traffic mode and behavior topological characteristics; s4, optimizing model training by adopting a category balance loss function; s5, collecting real-time flow data of to-be-identified equipment, converting the real-time flow data into feature vectors, inputting the feature vectors into the identification model, and calculating a matching result through cosine similarity; and S6, setting a similarity threshold value, outputting the type and the model of the equipment, or marking and storing unknown equipment. According to the method, the identification accuracy and the real-time performance are improved, and the identification capability of the shared IP equipment and the small sample equipment is enhanced.

The invention relates to a Decision Transform-based website fingerprint defense method, and solves the problems that a traditional defense method mainly adopts a traffic shaping or fixed mode data packet filling strategy, only aims at static traffic characteristics to interfere, and cannot realize data packet filling; a time sequence analysis mode of an attacker is difficult to model effectively; and a defense rule is solidified and is easy to suffer from adaptive attacks. The method comprises the following steps: generating a defense track data set; training a DT decision model; monitoring a network fingerprint attack; and defending the website fingerprints. According to the method, time sequence behavior characteristics of network flow are extracted, a long-term dependency relationship of flow interaction is modeled by adopting a Transform architecture, and a virtual data packet sequence matched with real service flow time-space characteristics is generated by utilizing a Decision Transform; according to the method, the defense strategy is dynamically optimized through the reinforcement learning framework, intelligent evolution of defense rules in the confrontation environment is achieved, flow feature analysis of attackers is effectively confused, and the defense success rate of website fingerprint attacks is remarkably increased.

The invention provides a long-term traffic flow prediction method based on a dynamic space-time diagram neural network, and the method comprises the following steps: inputting historical traffic data and a static adjacency matrix, and generating a dynamic diagram adjacency matrix through a diffusion probability model, which comprises the steps: carrying out the embedded representation learning of the static adjacency matrix and traffic features through a diagram convolution network, generating an initial dynamic adjacency matrix; iteratively optimizing the initial adjacency matrix through multi-step noise addition and conditional denoising, and generating a dynamic adjacency matrix reflecting the time-varying characteristics of the road network; based on the static adjacency matrix and the dynamic adjacency matrix, extracting spatial dependence characteristics of local and remote nodes through multi-hop diffusion convolution and bidirectional diffusion convolution; performing parallel processing on the time sequence through a simplified gating loop network SimGRU to eliminate serial dependence on a historical hidden state and retain long-term trend and periodic characteristics; and fusing the space and time features, and outputting a traffic flow prediction result of a future time period.

The invention provides an urban traffic management intelligent evaluation system and method based on a large language model, and belongs to the technical field of intelligent traffic management. The system comprises a data processing module, an MECA traffic flow analysis module, a feature engineering module, a driving behavior evaluation module, a clustering analysis module, an LLM intelligent decision module, a visualization generation module and a visualization module. The system analyzes the road traffic data collected by the unmanned aerial vehicle, adopts the MECA technology to automatically identify the road type and the traffic environment, intelligently judges the congestion level, evaluates the driving behavior, and combines a big language model to generate a targeted traffic management optimization suggestion. According to the method, the adaptive context sensing technology is innovatively combined with multi-criterion learning, adaptive congestion judgment of different road types is realized, different traffic characteristics of urban expressways, common urban roads and expressways can be accurately recognized, and differentiated management strategies are provided accordingly. The system supports real-time processing of large-scale traffic data, and provides scientific and accurate decision support for urban traffic management departments.

The invention discloses a flow characteristic adaptive QoS intelligent prediction adjustment method, and relates to the field of network flow management, and the method comprises the steps: 1, collecting flow data in real time, and constructing a multi-dimensional characteristic vector based on protocol types, port numbers and user behavior dynamic classification; 2, high-frequency / low-frequency components are separated, and QoS parameter prediction is output through fusion of an LSTM short-term prediction module and a periodic trend analysis module; 3, solving a resource pre-allocation scheme by adopting reinforcement learning by taking minimization of packet delay as a target; and 4, executing traffic identification, speed limiting and priority queue scheduling by using NPU hardware unloading. According to the method, the precision is improved through a high-frequency / low-frequency combined prediction architecture, decision delay is compressed to a large extent through reinforcement learning and NPU cooperation, and meanwhile online model iteration is achieved through a prediction error triggering mechanism.

The invention relates to the technical field of network abnormal traffic monitoring, in particular to a network abnormal traffic monitoring method, device and system based on a large model, and the method comprises the steps: collecting the network traffic data of each period at a network interface, and building a periodic network data set; determining a flow characteristic value of the same period, determining a periodic sequence of the same network protocol, and calculating a time sequence fluctuation coefficient of a period determined by each period adjacent to the period before the period; marking a target period, determining the influence weight of the characteristics of the network flow data in the target period, and performing dimensionality reduction on the network flow data of all the periods according to the influence weights of all the periods; and constructing an isolated forest model according to the dimensionality-reduced network traffic data, performing dimensionality reduction on the network traffic data for network abnormal traffic monitoring, inputting the network traffic data into the isolated forest model, and obtaining a network abnormal traffic monitoring result according to an anomaly detection result. According to the invention, the interpretability of the data after dimension reduction of the network traffic can be ensured, and the abnormal network traffic can be accurately identified.

The invention discloses an industrial control network flow abnormity real-time detection method based on a P4 programmable switch, and the method comprises the steps: extracting network flow characteristics related to a timestamp in a data plane in real time, carrying out high-line-speed preliminary detection on the traffic by using an industrial control network traffic anomaly detection algorithm based on a threshold rule to discover suspicious traffic, and uploading a preliminary detection result to a control plane; the control plane obtains suspicious traffic characteristic data based on the periodically maintained key traffic characteristic data and uploads the suspicious traffic characteristic data to the detection plane; the detection plane performs fine-grained detection on the suspicious traffic feature data by using a traffic anomaly detection model based on machine learning to obtain a final detection result; and when the difference between the final detection result and the preliminary detection result exceeds a given value, generating a new preliminary detection threshold rule based on an optimization algorithm or deep reinforcement learning, and updating to optimize the detection accuracy. According to the method, the real-time detection delay of the flow abnormity of the industrial control network can be greatly reduced, and the detection accuracy is remarkably improved.

The invention discloses a traffic anomaly monitoring method and system based on big data and a storage medium, and belongs to the field of network security. The method comprises the following steps: determining a monitoring target and a monitoring range, and defining a network range in which traffic monitoring needs to be carried out; data collection and arrangement: using a traffic monitoring tool to collect network traffic data, and performing data cleaning and preprocessing; analyzing traffic characteristics, including statistical analysis, protocol and port analysis and traffic mode identification; setting a baseline threshold, setting the baseline threshold based on the flow characteristic analysis result and the service demand, and establishing an adaptive threshold adjustment mechanism; and continuous verification and optimization: monitoring whether the network flow exceeds a baseline threshold in real time, triggering an anomaly detection mechanism, generating early warning information, executing a response strategy, and optimizing the baseline threshold according to feedback. By establishing the network flow base line and monitoring the deviation condition in real time, network abnormity can be found in time, the network security protection capability is improved, and the method is suitable for security monitoring of various network environments.

The invention discloses a risk overspeed behavior identification and early warning method based on track space-time matching analysis. The method comprises the following steps: extracting a risk overspeed event data set based on track space-time matching analysis; constructing a risk over-speed event multi-dimensional feature vector, and obtaining historical data of the risk over-speed event multi-dimensional feature vector corresponding to the risk over-speed event data set; constructing a risk over-speed prediction model, and training the risk over-speed prediction model; and obtaining a risk overspeed behavior prediction result based on the real-time data of the risk overspeed event multi-dimensional feature vector and the trained risk overspeed prediction model, and then executing hierarchical early warning operation. According to the invention, through integrating multi-dimensional data such as the vehicle track data, the static traffic characteristics, the dynamic traffic characteristics and the environmental factors, the risk overspeed behavior is accurately identified, and graded early warning is carried out according to the risk overspeed index, so that the traffic safety management level is improved.

The invention provides an intrusion detection method based on boundary sensitive federated expert multi-modal detection, which comprises the following steps of: splicing, synthesizing and fusing seven types of discriminative characteristics based on original traffic characteristics, designing a multi-modal collaborative attention model MultiModalFusion, dividing the characteristics into four modals, namely a protocol state, a traffic behavior, statistical distribution and a connection relationship, and realizing cross-modal information interaction by utilizing dynamic weight learning. In order to solve the problem of data imbalance, a boundary sensitive condition generator BSGenemator is developed to guide generation of minority class samples through a dynamic boundary strategy in combination with a composite loss function method. And finally, constructing a federal element strategy expert committee, dynamically fusing decisions of four experts by adopting a learnable strategy network, and verifying the characteristic contribution degree through an SHAP interpretable module. And finally, the efficiency of the scheme is verified by using a data set UNSW-NB15, through comparison of multiple schemes, the scheme has significant accuracy, the weighted average F1 score is improved, and a new normal form is provided for a real-time intrusion detection scheme.

A transportation system for generating transportation recommendations may (1) receive a transportation request associated with a user; (2) identify, using the transportation request, a first location and a second location associated with the transportation request; (3) determine, using the first location and the second location, routes between the first location and the second location; (4) receive travel data associated with each of the plurality of the routes, the travel data including information indicating a current or predicted future travel condition along each of the routes; (5) generate, using historic transportation characteristics associated with the user and the travel data, a safety or other score for each route, the score indicating an estimated level of safety of traveling along the route; and / or (6) generate a user interface providing indicators associated with the scores of the routes.

The invention relates to the technical field of network security management, and discloses a federated learning-based dynamic encryption network security management system, which comprises a network data acquisition module, a federated learning processing module, a security state monitoring module and the like. The network data acquisition module acquires flow and encryption state data in real time, and generates a characteristic value and encryption strategy dynamic adjustment set; the federal learning processing module generates a personalized encryption strategy according to the traffic characteristic value; the safety state monitoring module screens abnormal safety data; the strategy matching module determines a target adjustment strategy through multi-dimensional matching; the strategy adjustment module drives the encryption engine to correct the strategy; and the strategy self-learning module updates the strategy correction factor based on the feedback data. The system realizes dynamic encryption strategy adjustment and self-optimization, improves the intelligence and self-adaptive capability of network security protection, and is suitable for distributed network security management.