15 results about "Traffic flow analysis" patented technology

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 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 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 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 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 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.

Techniques for federated statistical and traffic flow analysis for anomaly detection in a cloud environment are disclosed. A plurality of payloads are received from first one or more components of the cloud environment and at a gateway of the cloud environment. The plurality of payloads are destined for second one or more components of the cloud environment. One or more attributes of each of the plurality of payloads are determined. Based on the one or more attributes of each of the payloads, the plurality of payloads is divided into two or more groups. For each group, one or more statistical data are gathered, based on the corresponding subset of the plurality of payloads for the corresponding group. The statistical data are analyzed, to detect an anomalous issue with one group of the two or more groups. Information associated with the anomalous issue are displayed on a user interface.

This application provides a method and system for improving the accuracy of power system network attack monitoring. It utilizes Direct Traffic Flow Analysis (DFI) technology to analyze collected traffic data and obtain traffic behavior information. Based on this information, it identifies the accessed service and the logical combination and sorting of communication protocols, and then combines this with CMDB (Content Management Database) basic data to construct a service characteristic model. Next, it performs Direct Traffic Flow Analysis (DPI) on the traffic data, and based on the analysis results and the service characteristic model, determines whether abnormal attack behavior exists within the traffic data. Therefore, by combining DFI and DPI analysis techniques to detect traffic data, and simultaneously judging abnormal attack behavior based on traffic behavior and the structural characteristics of the data packets themselves, the accuracy of power system network attack monitoring is significantly improved.

The utility model discloses an expressway construction safety management and control system based on a mooring unmanned aerial vehicle, which belongs to the field of expressway construction management and control and comprises the mooring unmanned aerial vehicle and a visual platform. A communication module is arranged in the mooring unmanned aerial vehicle; a communication and data storage module is arranged in the visual platform; the mooring unmanned aerial vehicle is in communication connection with the visual platform; an intelligent monitoring and recognition module, a high-precision measurement module, a topographic mapping module, a remote safety inspection module and a construction progress monitoring module are arranged in the mooring unmanned aerial vehicle; a task management module, a task planning module, a traffic flow analysis module, an intelligent traffic flow analysis algorithm module, a construction quality management module, an early warning module, a remote control module, a thermal imaging linkage module and a voice interaction module are arranged in the visual platform. According to the utility model, the monitoring range is greatly increased, the working efficiency is improved, and rapid deployment and automatic task scheduling can be realized.

1. The name of the design product: real-time traffic flow analysis and decision-making graphical user interface for electronic equipment. 2. The use of the design product: an electronic device. 3. The design points of the design product: the graphical user interface content in the screen. 4. The picture or photo that best shows the design points: design 1 change state diagram 1. 5. Design 1 is designated as the basic design. 6. The use of the graphical user interface: design 1 front view is the navigation interface display diagram after the system is opened by the controller; design 1 change state diagram 1 is the interface display diagram of the terminal area flight entering amount, which is obtained by clicking the traffic state under the regular chart module in the design 1 front view; design 1 change state diagram 2 is the interface display diagram of the takeoff and landing cycles of the Shuangliu Airport, Tianfu Airport and Mianyang Airport in the Chengdu approach airspace, which is obtained by clicking the airport takeoff / landing cycles (terminal) in the design 1 change state diagram 1; design 2 front view is the navigation interface display diagram after the system is opened by the controller; design 2 front view is the navigation interface display diagram after the system is opened by the controller; design 2 change state diagram 1 is the interface display diagram of the terminal area flight entering amount, which is obtained by clicking the traffic state under the regular chart module in the design 2 front view; design 2 change state diagram 2 is the interface display diagram of the takeoff and landing cycles of the Yibin Airport and Luzhou Airport in the South Sichuan approach area, which is obtained by clicking the airport takeoff / landing cycles (South Sichuan) in the design 2 change state diagram 1; design 3 front view is the navigation interface display diagram after the system is opened by the controller; design 3 change state diagram 1 is the interface display diagram of the terminal area flight entering amount, which is obtained by clicking the traffic state under the regular chart module in the design 3 front view; design 3 change state diagram 2 is the display diagram of the terminal area flight entering amount with freely customizable statistical targets, which is obtained by clicking the terminal area flexible chart in the design 3 change state diagram 1.