497 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.

The invention relates to the technical field of traffic management, in particular to a smart city intelligent traffic management system based on big data, which comprises a multi-source data acquisition module, a data fusion module, a dynamic feature generation module, a situation prediction module, a multi-target optimization module and an instruction execution module, the multi-source data acquisition module acquires multi-source data in real time; the data fusion module generates a standardized traffic data flow and outputs a three-dimensional data cube; the dynamic feature generation module outputs a traffic feature matrix; the situation prediction module generates a dynamic situation map; the multi-objective optimization module outputs an optimization instruction set; and the instruction execution module issues a control instruction to the traffic signal controller, the variable information board and the vehicle-mounted terminal. According to the invention, traffic jam can be relieved, road traffic efficiency can be improved, bus priority scheduling can be optimized, vehicle delay can be reduced, and emergency response speed can be improved.

The invention provides an edge computing traffic light intelligent decision-making method integrated with AI video analysis, and the method comprises the steps: collecting a real-time traffic video stream of a target intersection through an edge computing device, and extracting a dynamic traffic feature set in the real-time traffic video stream, calling a pre-trained space-time analysis model to carry out multi-modal fusion processing on the dynamic traffic characteristic set, generating a traffic state vector of the target intersection, matching a candidate control strategy in a preset decision rule base based on the traffic state vector, and carrying out parameter adjustment on the candidate control strategy through a strategy optimization model to obtain a target traffic state vector; generating a target signal lamp control parameter; and issuing the target signal lamp control parameters to a traffic signal control terminal of the target intersection, and monitoring traffic state change data of the target intersection in real time to update weight parameters of the space-time analysis model. According to the invention, the traffic efficiency, pedestrian safety and traffic management intelligence degree of urban intersections can be improved.

The invention discloses a deep reinforcement learning-based network traffic optimization scheduling method suitable for periodic traffic characteristics. The method comprises the following stages: collecting historical periodic traffic data; a network state is monitored in real time through an SDN controller, and historical periodic flow data including time delay, bandwidth utilization rate and load information are acquired; data preprocessing: performing cleaning, normalization and time sequence segmentation on the collected historical traffic data; constructing an LSTM model, constructing the LSTM model by using the preprocessed historical data, and defining an input feature and an output tag; training an LSTM model, training the LSTM model by using the training set data, and generating traffic prediction data of a next period; constructing a state space, fusing the predicted traffic data with the real-time traffic data, and constructing the state space; training a DQN model, and training a deep Q network (DQN) model by using the constructed state space; network topology and link state information are obtained, the current network topology is obtained through a topology discovery module of the SDN controller, and the bandwidth utilization rate, time delay and packet loss probability information of a link are collected in real time through a flow monitoring module; large flows and small flows are distinguished; scheduling the small flow by using the ECMP; and scheduling the large flow by using the DQN model.

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 discloses a load balancing control method based on data traffic, and relates to the technical field of data transmission. Comprising the following steps: constructing a flow monitoring module based on data flow, constructing a self-adaptive flow classification model, dynamically allocating weights to load nodes with balanced loads, dynamically adjusting a transmission path of the flow, carrying out real-time load balancing scheduling, and processing abnormal flow. According to the load balancing control method based on the data traffic, the self-adaptive traffic classification model and the multi-path traffic reconstruction algorithm are introduced, so that the defects in the prior art are fundamentally overcome. The real-time performance, the stability and the resource utilization rate of the system are remarkably improved through deep classification of the traffic characteristics and a multi-path dynamic segmentation strategy. According to the method, resource allocation can be optimized in a complex traffic scene, the load balancing efficiency is improved, meanwhile, the service quality of delay-sensitive and bandwidth-intensive services is guaranteed, and the availability and reliability of the whole system are enhanced.

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 embodiment of the invention discloses a dynamic traffic signal control method and device based on data fusion and a medium, belongs to the technical field of intelligent traffic, and solves the problem that the existing traffic signal control is difficult to make effective response when an accident occurs. Performing data fusion on first traffic data acquired by a road monitoring and shooting device and second traffic data acquired by a positioning device of the floating car through a multi-head attention mechanism at the edge node to obtain multi-source traffic characteristic data; the cloud end inputs the multi-source traffic characteristic data into a preset signal optimization model every first preset duration; the preset signal optimization model is updated and optimized through a PPO algorithm and a federated average method, and a global traffic model is generated; and through knowledge distillation, compressing the global traffic model, and migrating an edge traffic model obtained after compression to a corresponding edge node so as to carry out traffic signal strategy output on a corresponding road section through the edge traffic model.

The invention provides a distributed system flow intelligent grading scheduling method and system based on reinforcement learning, and relates to the technical field of flow grading, and the method comprises the steps: constructing a double-layer neural network to extract flow features, and inputting the flow features into a reinforcement learning model employing a double Q network structure, and outputting an intelligent hierarchical scheduling strategy including the interface priority, the processing queue configuration and the resource allocation proportion. Based on the strategy, a distributed token bucket algorithm is utilized to carry out rate limitation, queue allocation and resource regulation and control on interface requests with different priorities. When the system performance fluctuates, real-time monitoring data is fed back to the reinforcement learning model for online learning, a scheduling strategy is continuously optimized, self-adaptive hierarchical scheduling of distributed system traffic is realized, the utilization rate and stability of system resources are improved, and the request response time is shortened.

The invention relates to the technical field of traffic control, and provides a regional green wave coordination control method and system for traffic safety early warning. The method comprises the following steps: traversing a target traffic area to collect traffic data, and constructing a regional dynamic traffic characteristic matrix; performing green wave coordination analysis based on the matrix, and generating a sub-region division scheme; traffic flow characteristics of the subareas are extracted, and green wave coordination control parameters are calculated; formulating and executing an initial control scheme according to parameters, and collecting feedback data; and compensating the optimization parameters according to a feedback result to obtain a final regional coordination control scheme. The technical problem that efficiency and safety are difficult to cooperate due to the fact that traditional green wave control only statically optimizes traffic efficiency and cannot dynamically respond to traffic safety risks is solved, deep coupling of safety early warning and green wave control is achieved through real-time traffic data feedback and dynamic parameter compensation, and the traffic safety is improved. And the traffic efficiency is ensured, and the accident risk is obviously reduced.

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 power grid abnormal flow detection and identification method based on a multi-mode machine learning algorithm. The method comprises the following steps: step 1, acquiring industrial control protocol message data: capturing a communication message of network flow of an electric power industrial control system through a data collector; step 2, training a multi-modal auto-encoder: training the auto-encoder based on the network traffic characteristics of the normal service; 3, abnormal traffic monitoring: detecting and collecting abnormal traffic by taking the decoding reduction degree under the normal service as a baseline; step 4, abnormal data analysis modeling: training a multi-modal neural network model in combination with the abnormal traffic and the coding result of the baseline model for classifying the abnormal traffic; according to the method, some abnormal traffic existing in the network traffic of the power industrial control system can be detected, and the abnormal traffic in the industrial control system is classified by analyzing the data features and the service scenes of the abnormal traffic.

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 discloses an end-to-end traffic road state sensing method based on a multi-modal large model. The method comprises the following steps: acquiring a traffic video data set and a traffic text data set; extracting each frame of image of at least part of the traffic video data and performing feature extraction to obtain traffic features; labeling each frame of image based on the traffic element set; carrying out cleaning, word segmentation and marking on the traffic text data set; counting the first ratio and the second ratio so as to calculate a joint probability; judging whether the traffic elements are associated with the traffic features based on the joint probability; based on the associated traffic elements and traffic features, extracting the cleaned traffic text data set by using a sliding window to obtain a training sample; pre-training the large language model to obtain a pre-trained large language model; training the large perceptual model; and inputting a to-be-input video image into the trained perception large model to obtain a scene understanding text. According to the invention, accurate target detection and text description of the road network state are realized at the same time.

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 network security intelligent monitoring method for Internet of Things equipment and a related device, and relates to the technical field of network security, and the method comprises the steps: carrying out the feature screening and integration of initial traffic feature data generated by the traffic data of the Internet of Things equipment based on correlation feature selection, and obtaining target traffic feature data; performing protection capability evaluation of a traffic abnormal state based on attack path influence analysis to construct a traffic security baseline; performing traffic anomaly detection by using the target traffic feature data based on the traffic security baseline; determining a focus time window and an abnormal access behavior sequence based on the historical access behavior information of the user; performing access behavior anomaly detection on the user behavior monitoring information based on access behavior distribution and an abnormal access behavior sequence generated by the focus time window; and judging whether to send out early warning information based on the traffic anomaly detection result and the access behavior anomaly detection result. According to the invention, the reliability of network security monitoring of the Internet of Things equipment can be more effectively improved.

The invention provides an abnormal traffic management and control method and device, electronic equipment and a computer readable storage medium, and relates to the technical field of traffic processing. The management and control method comprises the following steps: on the basis of traffic characteristic data of network traffic, identifying abnormal traffic, an abnormal type of the abnormal traffic and a service type of the network traffic, the network traffic comprising the abnormal traffic and normal traffic; predicting a self-adaptive bandwidth occupation demand of the abnormal traffic based on the traffic feature data of the network traffic, the service type of the network traffic and the abnormal type of the abnormal traffic; and adjusting the current bandwidth occupation of the abnormal traffic based on the self-adaptive bandwidth occupation demand of the abnormal traffic so as to control the abnormal traffic. The invention aims to at least solve the problems that in the prior art, high-volatility network requirements cannot be fully met, flexible bandwidth optimization capability is lacked, and the aspect of refining flow management is still insufficient. The method is suitable for abnormal traffic identification and control scenes.

The invention relates to the technical field of network communication, and discloses a network load balancing evaluation method based on a deep learning model, which comprises the following steps: S1, data preprocessing: collecting flow data, and carrying out normalization, noise reduction and enhancement processing; s2, manifold modeling: dimension reduction is performed on high-dimensional flow data, and a local structure is reserved; s3, feature extraction: extracting optimized low-dimensional features, and reducing redundancy; s4, performance evaluation: comprehensively evaluating throughput, delay and balance; s5, anomaly detection: detecting abnormal traffic and outputting a result; s6, feedback optimization: dynamically adjusting a load strategy, and improving the performance. Through deep learning, variational information bottleneck and manifold learning, accurate modeling and optimization of dynamic network traffic features are realized, the extracted features accurately reflect the traffic change trend, the adaptive capacity of the model to sudden and abnormal traffic is enhanced, performance reduction caused by feature distortion in a traditional method is effectively avoided, and the method has a good application prospect. And reliable support is provided for a load balancing strategy.

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.

The invention discloses an industrial control intrusion detection method based on an ISAE auto-encoder and AFF feature fusion, and relates to the field of industrial control network security. The problem that a conventional intrusion detection algorithm is generally only based on a data set formed by original traffic data and manually extracted features, and due to the fact that traffic features cannot be deeply mined, obtained safety information is not comprehensive enough, and the effect of detecting partial attack traffic is poor is solved. The method comprises the steps of collecting original traffic data, obtaining corresponding artificial features according to the original traffic data, and constructing a training data set according to the artificial features; performing data preprocessing on the original flow data and the artificial features; constructing an intrusion detection model and training the intrusion detection model; and inputting the test set data into the trained intrusion detection model to obtain an intrusion detection result. The method is suitable for detecting and monitoring network intrusion, and the network intrusion risk is reduced.

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 relates to the technical field of network security, in particular to a satellite network multi-dimensional threat simulation method and system based on isolated forest detection. The method comprises the following steps of: loading real-time traffic data of a satellite network through a data interface, extracting key indexes, recording traffic size, time delay and packet loss probability, and establishing an initialized traffic characteristic framework; according to the method, through accurate identification of the abnormal modes and comparison with known threat features, judgment of potential threats is more accurate, resource waste caused by misinformation is avoided, evaluation of multi-dimensional threat degrees and specific analysis of threat risks provide more comprehensive safety evaluation, defense measures are more targeted and efficient, and the method is suitable for popularization and application. And the defense configuration is adjusted, and the defense scheme is tested in the virtual environment, so that the effectiveness of the adjustment measure is further verified, the resistance of the satellite network to actual threats is ensured, and the adaptability of the defense configuration and the overall security of the network are remarkably improved.

The invention provides a network security attack classification detection method and system based on a random forest, and relates to the technical field of network security. The method comprises the following steps: collecting network flow data; determining target network traffic data through a time-based sliding window algorithm; extracting a plurality of network traffic characteristics in the target network traffic data; calculating an average entropy value of the network flow characteristics; judging whether the average entropy value of the network flow characteristics is within a preset entropy value range or not; if yes, preliminarily determining the target network traffic data as normal traffic data; otherwise, preliminarily determining the target network traffic data as abnormal traffic data, and entering the next step; performing data processing on the abnormal traffic data; and carrying out classification detection on the processed abnormal flow data through an improved random forest algorithm to obtain an accurate detection result. The feature importance can be deeply analyzed, the classification efficiency is high, and the time consumed in the classification process is short.

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.