37 results about "Traffic flow analysis" patented technology

The invention discloses an urban life body full-cycle monitoring system based on a digital twinborn technology, relates to the technical field of urban governance, integrates multi-source data such as GIS data, remote sensing information, surface permeability data and an underground drainage pipe network structure, quantifies a flood high-risk area, and provides an urban life body full-cycle monitoring system based on the digital twinborn technology. The AI flood prediction module deduces the ponding range and the drainage capacity under different rainfall situations by adopting extreme weather analogue simulation based on a historical rainfall trend, a current meteorological condition and a drainage network state, and dynamically adjusts a flood diffusion trend prediction model, so that the flood risk identification is more accurate; the intelligent emergency scheduling module automatically matches flood control resources, combines flood high-risk areas, ponding point locations and traffic flow analysis, optimizes a drainage pump station start-stop strategy, remotely regulates and controls an underground drainage gate, dynamically adjusts the overflow adjustment capability of a sewage treatment plant, and optimizes an emergency risk avoiding route in combination with an intelligent traffic management system. And the accuracy and the execution efficiency of the flood control scheduling scheme are improved.

The invention discloses a space-ground integrated dynamic traffic flow analysis and road topology reasoning method and device. A cloud server generates and issues real-time traffic information through multi-level processing such as map matching, traffic flow analysis, prediction and early warning and road topology analysis and updating; specifically, firstly, the high-precision positioning and communication capability of a low-orbit satellite is utilized, data fusion of an inertial measurement unit and a wheel speed sensor is combined, and a multi-source fusion algorithm is constructed to improve the positioning precision; secondly, traffic flow dynamic analysis and prediction are carried out based on a Transform interaction analysis model, and real-time updating of a road topological structure is realized at a cloud end by using a clustering algorithm and a graph theory model; finally, the traffic flow and road topology information is sent to related vehicles and a traffic management system, and real-time monitoring, early warning and optimization of the traffic state are achieved. According to the invention, the accuracy and real-time performance of traffic information acquisition, processing and issuing can be effectively improved, and the intelligent level of traffic management and decision making is enhanced.

The invention relates to the field of computers, and discloses a visual target detection method and system based on hypergraph calculation, and the method comprises the steps: collecting the traffic data of different modes of a city through a multi-source sensor device; performing space-time alignment and denoising on the traffic data of different modes, and extracting multi-scale features through a feature encoder; traffic participants, road infrastructures and dynamic environment information are used as hypergraph nodes, interaction relations between the nodes are used as hyperedges to construct a hypergraph, and in a hypergraph feature space, a classifier is used to carry out target category prediction on the nodes; and target tracking and prediction: based on a hypergraph reasoning mechanism, fusing historical trajectory data and real-time traffic flow information, and predicting a target position. The method can solve the problems of low detection precision, low calculation efficiency and the like of an existing visual target detection technology in a complex traffic environment, and is suitable for application scenes such as urban road monitoring, traffic flow analysis and abnormal target detection.

The invention discloses a traffic flow prediction method and device, a storage medium and electronic equipment. The method relates to the technical field of traffic flow analysis, and comprises the following steps: constructing a multi-source traffic data pool, and preprocessing historical traffic data in the multi-source traffic data pool to obtain a target multi-source traffic data pool; performing data enhancement processing on the historical traffic data in the target multi-source traffic data pool by adopting a pre-trained GANs model to obtain an enhanced sample set for training a target traffic flow prediction model; performing model training on an initial traffic flow prediction model by adopting a Dropout method based on the enhanced sample set to obtain a target traffic flow prediction model meeting a preset condition; and performing traffic flow prediction on multi-source traffic data acquired in real time by using the target traffic flow prediction model to obtain a traffic flow prediction result. According to the method, the accuracy of urban traffic flow prediction can be improved.

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 mixed traffic flow-oriented automatic driving taxi dynamic scheduling system and a collaborative decision-making method, belongs to the field of intelligent traffic systems, and solves the problem that in the coexistence environment of manual driving vehicles and automatic driving taxies, the automatic driving taxies cannot be automatically scheduled. The technical problem of how to efficiently and cooperatively dispatch vehicles, accurately predict demands, optimize energy management and improve the overall operation efficiency of the system is solved. The system comprises a scheduling server which is connected with a road side sensing unit, a vehicle-mounted control unit and a charging station management platform. The scheduling server comprises a traffic flow analysis module; a demand prediction module; a dynamic scheduling module; and an energy collaboration module. The system is mainly used for realizing real-time, dynamic and intelligent scheduling and energy management of the automatic driving taxis in the mixed traffic flow, improving the operation efficiency, relieving the traffic jam and optimizing the charging resource utilization.

The invention, which relates to the technical field of traffic flow analysis, discloses a real-time traffic flow prompting system based on the Internet of Vehicles, comprising a data acquisition module, a road section flow evaluation module, a route comparison module, a prompting module and a response module. The data acquisition module collects data from multiple channels of vehicles, road infrastructures and traffic management departments, and provides comprehensive basic information for subsequent traffic flow evaluation and the like. According to the invention, through integration of the Internet of Vehicles technology, multi-channel data collection from vehicles, road infrastructures and traffic management departments is realized, the real-time performance and accuracy of traffic flow information are significantly improved, and the data acquisition module can monitor and analyze vehicle driving speed, road conditions and traffic events in real time. The road section flow evaluation module dynamically evaluates the traffic flow condition according to the real-time data and historical traffic parameters, so that more accurate traffic flow information and route selection suggestions are provided for drivers.

The invention discloses a space-time density peak value clustering method and system based on shared neighbor weighting, and the method comprises the steps: generating a weight coefficient through quantifying the proximity of a space-time neighbor in time and space dimensions; fusing a weight coefficient, a sample density degree and a shared neighbor similarity, and innovatively calculating a shared neighbor weighted local density which can better reflect spatio-temporal data characteristics; a decision value is generated in combination with the relative distance to accurately select a class cluster center; class cluster division is carried out by adopting a dynamic priority ranking strategy based on a maximum heap priority queue, so that the boundary point distribution precision and efficiency are effectively optimized; and finally, abnormal points are identified through a self-adaptive dynamic threshold value based on statistical distribution characteristics. According to the method, the problems that traditional density peak value clustering is poor in spatio-temporal data adaptability and high in noise sensitivity are solved, and the clustering accuracy, robustness and efficiency in applications such as intelligent traffic flow analysis and regional hot spot monitoring are remarkably improved.

The invention provides a vehicle-road cooperation roadside signal processing device. The vehicle-road cooperation roadside signal processing device comprises a high-definition camera, a laser radar, a millimeter-wave radar and a signal lamp collector which are arranged on the roadside. The RSU module is connected with the intelligent network connection vehicle and the edge computing node and is used for transmitting the vehicle position, the driving intention, the early warning information and the control instruction to the intelligent network connection vehicle and the edge computing node; the edge computing node comprises a data management module, a fusion sensing module, an application function module and a system management module, and the system management module performs monitoring, log level-to-level management, crash restart strategy and OTA updating functions on equipment states. The invention also provides a vehicle-road cooperation roadside signal processing method, which comprises the steps of designing parameter configuration, collecting data, generating traffic participant data based on a multi-sensor data fusion algorithm, and carrying out ROI partitioning and marking to generate an event detection analysis result and a traffic flow analysis result.

The invention relates to an industrial vehicle safety control and traffic flow analysis method and system based on an end-side large model, and the method comprises the steps: obtaining a forklift comprehensive image sample, carrying out the marking of the forklift comprehensive image sample, and obtaining a marking data set which comprises a forklift data subset, a cargo data subset, and a fork personnel data subset; based on preset model training parameters, model training samples are obtained from the forklift data subset, the cargo data subset and the fork personnel data subset in a classified mode; based on a classified model training sample, performing classification training on the constructed end-side deep learning model until training is finished to obtain a forklift safety control-traffic flow analysis model; and obtaining a to-be-identified forklift comprehensive image, and analyzing the to-be-identified forklift comprehensive image based on the forklift safety control-traffic flow analysis model to obtain forklift safety control-traffic flow result data. The forklift can be safer in the operation process, and vehicle allocation is more timely and accurate.

The invention discloses a traffic scene simulation method based on an accident report, and aims to solve the problem of insufficient complex dynamic scene data in traffic simulation. According to the method, physical clues (such as vehicle speed, collision angle, quality and the like) and context information (such as road types and environmental conditions) are extracted from a real traffic accident report, and a high-fidelity traffic scene data set is generated. In foreground processing, the extracted physical clues are used for accurately constructing a vehicle motion trajectory, diversified dynamic traffic scenes are generated through a pre-collision trajectory planning algorithm, and trajectory prediction is optimized in combination with a large language model to ensure that the real physical law is met. In background processing, high-quality three-dimensional scene reconstruction is carried out according to environment information in an accident report, a vivid background image is generated, and the consistency of a visual angle and illumination is kept. And finally, through fusion of the foreground vehicle track and the background image, a visual and physical double real traffic simulation video is generated. The method significantly improves the diversity and authenticity of traffic scenes, reduces the dependence on real data collection, is widely suitable for high-fidelity traffic simulation scenes such as automatic driving test and traffic flow analysis, and effectively promotes the progress of the traffic simulation technology.

The invention discloses a road network traffic state generation method based on graph embedded coding, and belongs to the technical field of traffic road network traffic flow analysis and processing. Comprising the following steps: capturing road network spatial information features by using GraphSAGE, obtaining time sequence features through a self-attention mechanism, and constructing a road network embedded representation learning model based on spatio-temporal information aggregation; and constructing a road network traffic state generation model of graph embedded coding, obtaining a decoder based on Transform and GCN based on a road network embedded representation learning model of spatial-temporal information aggregation, and restoring to target output according to feature representation generated by STGE to complete a traffic flow prediction task and a data completion task. According to the method, fitting can be better carried out for the spatial-temporal characteristics of the road network, and a certain support is provided for establishing a unified traffic flow coding-decoding model.

The invention discloses a traffic signal control system based on traffic flow detection, and relates to the technical field of intelligent traffic. The system comprises a traffic flow acquisition module, a two-dimensional traffic flow analysis module, a dynamic phase splicing module and a signal control execution module. The traffic flow acquisition module acquires original data of each lane; the two-dimensional analysis module generates a dynamic traffic flow characteristic value through spatial dimension correlation analysis and time dimension fluctuation period extraction; the dynamic phase splicing module splits and recombines the fixed phase, generates a dynamic phase sequence in combination with the conflict matrix and the priority, and determines the duration according to the traffic flow ratio and the fluctuation period; and the signal control module executes control. The system solves the problems that a traditional system is insufficient in detection precision and rigid in timing, improves the intersection passing efficiency, and has both practicability and economical efficiency.

The invention discloses a highway reconstruction and extension opportunity decision-making method based on traffic flow analysis, and the method comprises the steps: building a three-stage progressive decision-making framework which comprises a technical evaluation layer, a management optimization layer and a policy adaptation layer, the first stage is based on a technical level, the second stage is based on a management level, and the third stage is based on a policy level. A composite technology evaluation model of real-time traffic flow saturation and full-life-cycle economy is constructed, an initial reconstruction and extension scheme is generated through dynamic threshold analysis, and the full-life-cycle economy is used for evaluating the total cost of the expressway, including direct cost of construction and maintenance and indirect cost of environmental maintenance and scrap treatment. In the second stage, a road network level space-time correlation analysis module is introduced, four management parameters including the synergistic effect of adjacent road sections, the shunting path bearing margin, the construction period traffic organization cost and the user willingness to pay are comprehensively evaluated, and the decision-making method for the reconstruction and extension opportunity is more comprehensive and macroscopic.

A traffic flow analysis device according to the present invention includes: a memory configured to store instructions; and one or more processors configured to execute the instructions to: acquire an image from a camera installed at a location where a moving object subject to traffic flow analysis is configured to be imaged; store a plurality of types of identification methods for identifying an attribute of the moving object captured by the camera; select an identification method suitable for a tendency of the moving object captured by the camera from the plurality of types of stored identification methods; and identify a moving object appearing in the acquired image and an attribute of the moving object using the selected identification method.

The embodiment of the invention relates to the technical field of traffic, and discloses a traffic signal machine flow analysis device and analysis method based on artificial intelligence, and a storage medium, and the method comprises the steps: firstly obtaining an initial vibration curve formed by the collection data of a vibration membrane sensor of a target traffic road section, and carrying out the compensation and correction of the initial vibration curve based on environment parameters, and obtaining a target vibration curve. And then key features of a target vibration curve are identified and extracted, a plurality of candidate vibration data sets containing information such as vibration amplitude and corresponding duration are formed, real-time traffic flow is preliminarily estimated according to the candidate vibration data sets, and a traffic flow estimated value is obtained. If the estimated value is greater than or equal to a preset flow threshold value, triggering a correction instruction, controlling a cloud camera communicating with a cloud server to start, and correcting the estimated value to obtain a target value; and if the estimated value is smaller than the threshold value, directly taking the estimated value as a target value. According to the scheme, vibration data and visual correction are combined, so that the method can adapt to various traffic flow analysis scenes, and accurate analysis results can be obtained in different scenes.

[Task to be Accomplished]To enable a user, when presenting a result of traffic flow analysis to the user, to check states of moving bodies in a measurement area and supplemental information on a traffic environment of the measurement area, thereby efficiently examining of the traffic environment.[Solution]A traffic flow measurement server identifiably detects moving bodies in a measurement area based on detection results of sensors (camera and lidar), and in response to a user's operation on a user terminal, generates a screen 531 in which an area image 561 representing areas of a road component, an area image 562 representing a moving object, a self-driving label 563 indicating a self-driving vehicle, and a relative position label 564 indicating a positional relationship between a moving body and a road component are overlaid on sensor images (camera image 533, lidar point cloud image 534), and causes the user terminal to display the generated screen.

The invention relates to the technical field of traffic flow analysis, in particular to a road network traffic flow analysis method based on robust principal component tracking. The method comprises the steps of collecting traffic information of a target area road network; performing compensation processing and protocol processing on the traffic information to obtain an original traffic matrix of the traffic information; performing singular value decomposition on the original traffic matrix to obtain an original noise matrix; obtaining a random matrix constraint parameter of the relaxation principal component tracking method according to the original noise matrix; decomposing the original traffic matrix by using a relaxation principal component tracking method to obtain a low-rank matrix of traffic information; and decomposing the low-rank matrix by adopting a singular value decomposition method to obtain a time-varying feature component and a spatial feature component, and carrying out traffic flow analysis after clustering the spatial feature component. According to the method, sudden change traffic flow components contained in traffic information and noise components caused by various reasons can be effectively eliminated, and the accuracy of traffic flow analysis is improved.

The application provides a traffic flow analysis method and a server based on a space-time graph neural network. The method comprises the following steps: first, acquiring traffic state parameter sequences of each traffic node in continuous collection periods to form a sequence set; constructing a traffic node state change correlation graph of each collection period according to the traffic state parameter change amount between adjacent periods to represent the synchronization relationship of the change amount between nodes; tracking the dynamic evolution of the correlation graph in time sequence to generate a time sequence causal coupling pair set representing the state-induced relationship between nodes; performing a nonlinear causal edge pruning operation on the time sequence causal coupling pair set to eliminate the coupling pairs satisfying the nonlinear causal inhibition condition to obtain a pruned coupling pair set; and finally generating a causal propagation diffusion path graph structure according to the state-induced direction in the pruned coupling pair. The causal influence relationship between nodes is autonomously discovered from time sequence data and a propagation path graph is constructed, so that dynamic and high-fidelity analysis of the traffic flow propagation path is realized.

The invention provides a construction area road network simulation model and a simulation device thereof.The simulation model comprises a simulation base, a traffic light control board and a vehicle monitor, a base chassis is fixedly arranged at the bottom of the simulation base to achieve the stable supporting effect, and a plurality of information interface modules are fixedly arranged on the lower portion of the side face of the simulation base; the information interface modules are arranged in an annular array, and the front ends of the information interface modules are connected with signal data lines. Through the real-time construction area road network simulation model, the traffic condition can be accurately analyzed and predicted, comprehensive data support and decision basis are provided for construction area management through the simulation system, managers can better master the traffic condition of the construction area, the management level is improved, and the construction area management efficiency is improved. According to the invention, through accurate traffic flow analysis and prediction, the road resource allocation is optimized, the road traffic capacity is improved, and the traffic jam phenomenon is reduced.

The present invention discloses a smart city traffic signal light control system, which relates to the field of traffic signal control technology. The system includes the following components: a regional division module, a traffic flow detection sensor, a regional traffic flow analysis module, a signal light control strategy generation module, and an inter-regional signal light collaborative control module, as well as a variable information sign, an information processing and generation module. The present invention can monitor and analyze urban traffic flow conditions in real time and accurately by comprehensively utilizing advanced regional division algorithms, traffic flow detection technologies, and precise traffic flow status assessment models. Based on the analysis results, the system can intelligently generate signal light control strategies, including adopting a congestion relief dynamic timing algorithm in congested areas to reduce the number of signal light switching times, and adopting a flow balancing guidance timing algorithm in areas with less traffic. In addition, the system can also realize the collaborative control of inter-regional signal lights to ensure efficient connection and balanced distribution of traffic flows between regions.

The present application belongs to the field of traffic technology, and particularly relates to a method and system for analyzing average traffic flow at an intersection and a storage medium. First, a traffic flow monitoring platform is constructed, and traffic flow monitoring devices are arranged at each intersection according to a preset device arrangement method. Then, the traffic flow monitoring devices are connected to vehicle terminals at each intersection, real-time data and recent data of the vehicle terminals at each intersection, as well as real-time flow data and recent flow data are obtained, and transmitted to the traffic flow monitoring platform. The obtained data is input into a traffic average flow model in the traffic flow monitoring platform for training and verification, and a trained traffic average flow model is output. Finally, the trained traffic average flow model is used to generate average flow data of the current period at the intersection. The present application can solve the problems of high cost and difficult maintenance in the analysis of average traffic flow at an intersection in the existing traffic management process.

The application discloses a kind of based on AI and ship AIS big data offshore traffic safety analysis method, belong to offshore wind farm and offshore mariculture area siting technical field, specifically include: obtaining the ship AIS data of target sea area generation ship AIS traffic flow analysis diagram;Target sea area is divided into analysis grid unit and the external navigation risk score of each grid unit is calculated;Based on risk score screening and aggregation generate several candidate site area, extract site boundary parameter;Determine the candidate mother port corresponding to each candidate site area, based on electronic chart data planning each mother port to site operation and maintenance route, calculate navigation difficulty coefficient and select minimum value as operation and maintenance path conflict index;The external navigation risk score and operation and maintenance path conflict index are weighted and summed to obtain comprehensive score, select the lowest site area as target site area as score.The application includes external navigation risk and operation and maintenance path safety into unified decision framework, realizes the multidimensional collaborative optimization of wind farm siting.

The application discloses a kind of intersection mixed automatic driving traffic flow analysis control method, system, equipment and medium, it is related to automatic driving technical field, including the driving behavior characteristics of artificial driving vehicle and intelligent network connection vehicle are analyzed, obtain artificial driving decision criterion;Based on artificial driving decision criterion, construct model predictive control framework under intelligent network connection vehicle controller, and form the control strategy of different driving styles by setting different control cost weight;Intelligent network connection vehicle controller is embedded in intersection mixed traffic flow simulation environment, cooperates with artificial driving vehicle and quantitatively evaluates control effect;According to quantitative evaluation result, the control parameter of controller is dynamically adjusted.By data-driven behavior cognition, quantifiable effect evaluation and adaptive parameter updating based on evaluation result, the compatibility of intelligent network connection vehicle in mixed traffic flow, running stability and overall control performance are improved.

The invention discloses a highway traffic state division method for unbalanced data, solves the problem of how to adapt to traffic operation state division under different regions and different road types, and belongs to the field of highway traffic flow analysis. The method comprises the steps that collected traffic data are analyzed, minority traffic data are enhanced, an enhanced data set serves as input of a particle swarm optimization algorithm, a candidate clustering center is generated and serves as an initial clustering center of a fuzzy C-means clustering method, and in the iteration process, the candidate clustering center is updated by means of a genetic algorithm, so that the clustering accuracy of the fuzzy C-means clustering method is improved. A fitness function value is calculated according to the traffic state jump rate of the current candidate clustering center, the consistency of the upstream and downstream traffic states and a target function of a fuzzy C-means clustering method, and a final clustering center is obtained through iteration; and performing defuzzification processing on the final clustering center, allocating each data point to the category with the maximum membership degree, and outputting a category division result.

The invention discloses a marine traffic transportation safety analysis method based on AI and ship AIS big data, and belongs to the technical field of offshore wind power plant and sea culture area site selection, and the method specifically comprises the steps: obtaining ship AIS data of a target sea area, and generating a ship AIS traffic flow analysis chart; dividing the target sea area into analysis grid units and calculating an external navigation risk score of each grid unit; screening and aggregating to generate a plurality of candidate site areas based on the risk scores, and extracting site boundary parameters; determining a candidate mother port corresponding to each candidate site area, planning an operation and maintenance route from each mother port to the site based on the electronic chart data, calculating a navigation difficulty coefficient, and selecting a minimum value as an operation and maintenance path conflict index; and performing weighted summation on the external navigation risk score and the operation and maintenance path conflict index to obtain a comprehensive score, and selecting the site area with the lowest score as a target site area. According to the method, external navigation risks and operation and maintenance path safety are incorporated into a unified decision framework, and multi-dimensional collaborative optimization of wind power plant site selection is realized.

The invention discloses an enclosure time optimization method based on historical traffic flow analysis, and relates to the technical field of traffic prediction, and the method comprises the following steps: S1, predicting the traffic flow based on historical traffic flow features; s2, integrating the real-time monitored traffic flow and the predicted traffic flow to carry out comprehensive traffic flow prediction modeling; s3, constructing a traffic capacity loss model to quantify traffic capacity loss caused by enclosure; s4, constructing a multi-objective optimization function, and solving the optimal enclosure time; and S5, dynamically adjusting the enclosure time in real time according to the actual traffic flow. According to the method, through deep coupling of a theoretical model and real-time monitoring, dynamic optimization of the traffic enclosure time period is achieved, and the construction efficiency is maximized on the premise that the traffic capacity is guaranteed.

The invention discloses a video monitoring scene intelligent analysis and event classification system based on semantic segmentation. The system comprises a data preprocessing module, a preliminary classification module, a weight generation module and a correction and detection module. The data preprocessing module is used for acquiring and preprocessing original video data of a video monitoring scene, and extracting scene semantic features of the preprocessed video data through a semantic segmentation model; and the preliminary classification module is used for inputting the scene semantic features into a classifier for performing preliminary event classification to obtain an initial classification result. The invention relates to the technical field of video monitoring and intelligent analysis. According to the video monitoring scene intelligent analysis and event classification system based on semantic segmentation, the coupling degree between modules of the system is low, the semantic category number, the classifier structure and the updating strategy can be freely configured according to different monitoring tasks, and the system is suitable for various application scenes such as traffic flow analysis, personnel gathering monitoring and industrial operation anomaly detection.

The invention provides a road service area operation strategy generation method and system based on traffic flow analysis, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the preliminary optimization of a to-be-optimized traffic flow analysis network through unsupervised sample data, and generating a temporary traffic flow analysis network; and furthermore, by means of supervised sample data carrying service area state description knowledge, the temporary traffic flow analysis network is further refined and optimized, and finally a target traffic flow analysis network is generated. The target traffic flow analysis network can accurately predict the service area state description label corresponding to the target traffic flow monitoring data, and provides reliable data support for making an operation strategy of a road service area. Through the method, the scientificity and pertinence of the operation strategy of the road service area can be remarkably improved, the service resource configuration is optimized, and the service quality and the operation efficiency of the service area are improved.

The invention relates to a dynamic simulation test scene generation method based on deep learning, and belongs to the technical field of automatic driving automobile testing. The method comprises the following steps: S1, dividing a real traffic data set and extracting traffic flow updating characteristics; s2, scene data are extracted based on a time sliding window to construct a data loading module; S3, data enhancement means of corresponding scales are adopted for different contents of input track data; s4, constructing an adversarial generative network for generating a dynamic test scene; and S5, establishing a dynamic scene rolling generation framework. According to the method, the universality of a deep learning model is enhanced through a data processing means, long-term deduction of a real test scene is realized in combination with traffic flow analysis and rolling generation, and steady-state generation of a complex interactive simulation test scene is realized. Compared with a traditional dynamic test scene generation method, the method can achieve the generation of a real test scene of multiple traffic participants of non-fixed fragments.