23 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 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 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 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.

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 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.

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 present invention discloses a method and device for ground-to-space dynamic traffic flow analysis and road topology reasoning. The cloud server generates and publishes real-time traffic information through multi-level processing such as map matching, traffic flow analysis, prediction and warning, and road topology analysis and update. Specifically, the method first utilizes the high-precision positioning and communication capabilities of low-orbit satellites, combined with the data fusion of inertial measurement units and wheel speed sensors, to construct a multi-source fusion algorithm to improve positioning accuracy. Secondly, a Transformer-based interactive analysis model is used to perform dynamic analysis and prediction of traffic flow, and clustering algorithms and graph theory models are used to achieve real-time updates of road topology structures in the cloud. Finally, traffic flow and road topology information are sent to relevant vehicles and traffic management systems to achieve real-time monitoring, warning, and optimization of traffic status. The present invention can effectively improve the accuracy and real-time performance of traffic information collection, processing, and publication, and enhance the intelligent level of traffic management and decision-making.

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.

The application relates to the field of computers and discloses a visual target detection method and system based on hypergraph calculation, which collects traffic data of different modes of a city through a multi-source sensor device; the traffic data of different modes is subjected to space-time alignment and denoising, and multi-scale features are extracted through a feature encoder; a hypergraph is constructed by taking traffic participants, road infrastructure and dynamic environment information as hypergraph nodes and taking the interaction relationship between the nodes as hyperedges; in a hypergraph feature space, a classifier is used to predict the target categories of the nodes; target tracking and prediction are carried out based on a hypergraph reasoning mechanism, historical trajectory data and real-time traffic flow information are fused, and the target position is predicted. The method can solve the problems of low detection precision and low calculation efficiency of existing visual target detection technologies in complex traffic environments and is suitable for application scenarios such as urban road monitoring, traffic flow analysis and abnormal target detection.

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.