2351 results about "Road networks" patented technology

A dynamic evaluation method for an extreme rainstorm waterlogging disaster risk for disaster prevention and reduction. The method comprises: investigating and surveying urban system data and disaster prevention and reduction data, using GIS technology to divide disaster-bearing objects into refined risk units on the scale of urban buildings and road networks, and determining the spatial distribution of the disaster-bearing objects; on the basis of an extreme rainstorm waterlogging scene, simulating the disaster influence of a dynamic change process of a flood ponding depth on the disaster-bearing objects; developing refined dynamic evaluation on a waterlogging risk by combining the two methods of waterlogging process simulation and an indicator system; using a spatial complex network and a shortest path plan to calculate a traffic capacity and emergency service accessibility of a road network system; and on this basis, taking into comprehensive consideration the rational allocation of disaster prevention emergency drainage and emergency rescue services to a high-risk area, and proposing dynamic evaluation technology for a waterlogging risk that integrates a disaster evolution process and a disaster prevention response process, and ultimately realizing the dynamic evaluation of the waterlogging risk of each disaster-bearing unit during the waterlogging disaster evolution.

The invention relates to the technical field of highway emission reduction, and discloses a highway carbon emission simulation deduction system based on digital twinning. The system comprises a carbon emission data acquisition layer, a digital twin modeling layer, a multi-dimensional carbon emission calculation layer, a simulation deduction optimization layer and an execution feedback adjustment layer. The carbon emission data acquisition layer acquires multi-source traffic parameters through a distributed sensor network, generates a dynamic carbon emission factor matrix and performs sensitivity grading; the digital twinborn modeling layer constructs a road network twinborn body, generates a road three-dimensional topological structure, and superposes a vehicle energy consumption model to form a dynamic twinborn scene; the multi-dimensional carbon emission calculation layer establishes a space-time mapping relation, and integrates emission data to generate a road section-level carbon emission intensity map; the simulation deduction optimization layer converts the atlas into a management and control strategy set, predicts a carbon emission change trend and outputs a Pareto optimal strategy combination; and executing feedback adjustment layer monitoring data, calculating a deviation rate, generating an adaptation degree index, and dynamically correcting model parameters until the index is stable.

The invention discloses an urban traffic intelligent regulation and control system and method based on digital twinning. The method comprises the following steps: S1, collecting and fusing multi-source data in real time; s2, constructing a digital twinborn model; s3, traffic flow prediction and optimization; s4, multi-system cooperative control is carried out; the system comprises the following modules: a multi-source data real-time acquisition and fusion module which comprises a video data acquisition and processing unit, a geomagnetic sensor network construction unit and a floating car data integration unit; the digital twinborn model building module comprises a three-dimensional road network model building unit and a data fusion and calibration unit; the traffic flow prediction and optimization module comprises an intelligent prediction model unit and a real-time optimization control unit; and the multi-system cooperative control module comprises an emergency priority passing system unit, a reversible lane dynamic adjustment unit and a parking lot induction unit. The traffic efficiency can be remarkably improved, the cooperative response speed is greatly increased, and carbon emission is effectively reduced.

The invention relates to the field of artificial intelligence, particularly discloses an intelligent optimal management system for urban traffic congestion based on artificial intelligence, and aims to solve the problems of congestion and low efficiency caused by response delay, local optimization and low data utilization rate of an existing traffic management system. The system comprises a data acquisition and fusion module, a traffic state perception and prediction module, a decision optimization module, an instruction issuing and execution module and a man-machine interaction and visualization module. Through multi-source data fusion, graph neural network prediction and multi-agent reinforcement learning, traffic flow real-time perception, accurate prediction and adaptive control are realized, congestion is effectively relieved, and the overall operation efficiency and toughness of a road network are improved.

The invention relates to a large-scale road network traffic control method based on a deep reinforcement learning large model, and belongs to the technical field of intelligent traffic control. The method comprises the following steps: sensing real-time multi-modal road network information including urban road intersections, highway entrance ramps and emergency lanes, and generating a space-time fusion representation vector representing a current traffic network state by fusing a space diagram construction method and a time sequence embedding method; the space-time fusion representation vector and historical state memory are spliced to serve as input, a backbone network of a pre-training large language model is used for state feature distillation so as to enhance state representation, and a traffic control decision is output through a strategy network with a layered action space; through cross-modal knowledge migration and a progressive course learning strategy, a training process of a deep reinforcement learning algorithm is guided and optimized so as to improve model training efficiency and generalization ability. According to the method, the generalization performance and the accuracy of the control strategy are improved while the real-time response speed is ensured.

The invention relates to the technical field of traffic safety, in particular to a beforehand prevention and control strategy generation method for traffic safety risks of a highway network in a mountainous area. Comprising the following steps: risk diagnosis and data preparation: collecting road basic information, historical traffic flow data, meteorological data and accident-prone point data of a mountainous area expressway network; constructing a risk assessment model: selecting road alignment, traffic flow, weather and environmental toughness indexes, determining index weights by adopting an improved analytic hierarchy process, constructing the risk assessment model through a fuzzy comprehensive evaluation method, and calculating the traffic safety risk level of each road section; a prevention and control strategy is generated; and strategy verification and iteration. According to the method, the total factor evaluation model is constructed by integrating the road network topological structure, the traffic flow characteristics and the meteorological sensitive section data, so that the problem of one-sided risk identification caused by single factor analysis in the prior art is solved, and systematic description of the mountainous area highway composite risk scene is realized.

The invention provides a construction method of an urban low-altitude wind field digital twinning system, which comprises the following steps: constructing an urban basic road network skeleton and performing region division, generating a building block model with real textures by using oblique photography data, and forming an urban three-dimensional space geometric model library; capturing atmospheric information in real time through a radar to generate high-resolution three-dimensional wind field scanning data covering a target area; processing detection data of the laser wind finding radar, performing simulation calculation on a wind field in combination with an urban three-dimensional space geometric model, and generating sub-meter gridding dynamic wind field data of an urban low-altitude area; and performing three-dimensional reduction and vivid representation on the obtained dynamic wind field data by using a visual rendering technology to form an interactive urban low-altitude wind field digital twin system. By integrating multi-scale modeling, laser wind finding radar, wind field simulation and visual rendering technologies, real-time monitoring, dynamic simulation and visual display of an urban low-altitude three-dimensional wind field are achieved, and low-altitude flight safety and operation efficiency are improved.

The invention relates to the field of real-time visualization of road conditions, in particular to a data simulation visualization rendering method and system for urban traffic road conditions. The method comprises the following steps: extracting a real-time satellite streetscape image based on urban satellite remote sensing scanning, and performing scene pixel-level segmentation to obtain scene texture rendering parameters; scene illumination visual identification is carried out according to the real-time satellite streetscape image, traffic scene background modeling is carried out based on scene texture rendering parameters, and a real-time scene background model is constructed; the method comprises the following steps: acquiring urban-level multi-source traffic monitoring data flow, performing vehicle state sensing, and constructing a multi-dimensional particle feature matrix; road network topological correlation analysis and global traffic network state perception are carried out according to the real-time satellite streetscape images, and a road network state perception model is constructed. According to the invention, a real real-time traffic environment is visualized, scene effects in different traffic states are presented, the current road condition can be rapidly evaluated, and the traffic control decision efficiency is improved.

The invention belongs to the field of artificial intelligence and intelligent traffic systems, particularly relates to a vehicle path planning system based on multi-agent collaboration, and aims to solve the problems of path response lag, insufficient global optimization ability and conflict between individuals and group targets in a dynamic traffic environment. The system adopts a three-level collaborative architecture of a central coordination server, a regional scheduling agent and a vehicle-mounted agent, realizes macroscopic regulation and local resource allocation through a global guidance potential field and an improved auction algorithm, and improves the dynamic adaptive capacity by combining event-driven replanning and a robustness verification mechanism. Different scheduling and multi-time scale coupling operation of heterogeneous vehicles are supported, and the road network passing efficiency and the path reliability are effectively improved.

The invention is suitable for the technical field of earth surface monitoring, and provides an earth surface deformation monitoring method and system based on a time sequence InSAR, and the method comprises the following steps: carrying out the SAR image screening and interferogram generation, and collecting corresponding meteorological data and thermal infrared data; performing space-time adaptive atmospheric phase correction, taking the air pressure vertical gradient and the temperature anomaly as driving factors of atmospheric delay, and separating an atmospheric phase through a space-time weighted model; dynamic deformation modeling is carried out, and deformation is decomposed into linear deformation and nonlinear deformation; the method comprises the following steps: taking a mining area road network as a geometric constraint, unwrapping a coherent region by adopting a minimum cost flow algorithm, converting an unwrapping phase into sight-line-direction deformation, and calculating horizontal and vertical deformation components in combination with InSAR sight-line-direction deformation and a digital elevation model. The method adapts to a complex deformation mechanism of a mining area by capturing linear and nonlinear deformation. Through sight line deformation and DEM geometric projection, vertical and horizontal deformation separation is realized, and the deformation direction is determined.

The invention discloses a road congestion prediction system and method based on spatial-temporal feature extraction, and belongs to the field of intelligent traffic. The system adopts a layered distributed architecture, and comprises a multi-source data acquisition module, a data preprocessing unit, a double-flow spatio-temporal feature extraction network, a two-stage spatio-temporal attention mechanism module, a congestion prediction model and a result feedback interface. Multi-modal data such as a vehicle-mounted GPS track, checkpoint flow, video monitoring and meteorological data are integrated, a double-flow feature extraction network is constructed by adopting a graph convolutional network and a bidirectional gating circulation unit, and a key space-time region is dynamically focused in combination with a multi-head self-attention and time weighted dot product attention mechanism; and finally, optimizing the generalization ability of the model through a composite loss function. According to the method, a dynamic adaptive learning framework and multi-source data combined modeling mode is adopted for urban road traffic flow characteristics, the space-time precision and the real-time response capability of road network congestion prediction are remarkably improved, and reliable decision support is provided for intelligent traffic control.

The invention relates to the technical field of intelligent traffic, in particular to an urban traffic flow prediction method fusing a dynamic graph convolutional network and a Transformer, which comprises the following steps: firstly, collecting traffic data in a road network to form a data set, then constructing a space-time dynamic GCN unit to capture dynamic evolution of road network topology, and extracting multi-scale space-time characteristics through expansion time convolution; then, constructing a Transform unit for modeling a global time sequence dependency relationship of the traffic flow; finally, in combination with a gating fusion prediction unit, spatial-temporal features are fused through a spatial-temporal cross attention mechanism, and the prediction precision and robustness are effectively improved. Through verification and evaluation of real data, the method is suitable for a traffic flow prediction scene in an urban road network dynamic environment, and especially has good performance in sudden road conditions and peak hours.

The invention provides an urban subway network toughness pre-disaster and post-disaster dual-stage optimization method under flood disasters. The method comprises the following steps: acquiring urban subway network information, road network information and flood disaster information; constructing an urban subway topology network and a road topology network; simulating flood depth space-time change in the subway and road network during the duration of the flood disaster, and determining a subway network failure station set based on the accumulated water depth; calculating flood control resource demand quantity based on the platform number, the platform type and the flood depth of each station in the failure station set; establishing and solving a pre-disaster-post-disaster dual-stage optimization model of urban subway network toughness to obtain an optimal pre-disaster site closing decision, an optimal flood control resource warehouse site selection decision and an optimal post-disaster restoration decision; wherein the double-stage optimization model is a double-layer planning model; and the upper-layer model and the lower-layer model are mutually coupled and optimized through a pre-disaster closing site set and a post-disaster repairing process.

The invention provides a traffic flow intelligent prediction method based on multi-source data fusion, and the method comprises the steps: calculating the historical proportional relation between floating traffic flow and actual traffic flow at different time periods and different nodes according to historical time sequence data; identifying a change mode of the permeability and calculating to obtain a reference dynamic permeability value; establishing a permeability time relation matrix based on the reference dynamic permeability value; inputting the floating traffic flow data into the permeability time relation matrix to obtain a query dynamic permeability value; dividing the floating traffic flow data by the corresponding query dynamic permeability value to obtain a continuous estimation sequence of the traffic flow of the whole road network; fusing the continuous estimation sequence with multi-source historical traffic data to generate space-time fusion features; based on the space-time fusion features, the traffic flow prediction result of the future time period is output, the traffic flow prediction precision is significantly improved, the model generalization ability is enhanced, the prediction real-time performance and continuity are improved, and the problem of large prediction deviation is effectively solved.

The invention discloses a regional highway road network habitat quality evaluation method and system, and aims to construct a habitat feature library reflecting habitat spatial features and a biological utilization mode by integrating remote sensing image data and biological activity trajectory data. Identifying an ecological fault zone caused by road network cutting by adopting a space fracture analysis technology, quantifying the barrier strength of the road and determining an influence range; analyzing and extracting a key migration gallery by using a species migration network, and evaluating a landscape connectivity condition; a restoring force loss area is identified through ecological restoring force evaluation, and the habitat degradation dynamic state is revealed in combination with time sequence analysis; establishing a multi-scale space evaluation system, and fusing the quality grading sequence and gallery optimization information to generate a habitat quality evaluation matrix; and finally, a complete habitat quality grading scheme is formed through spatial interpolation and natural breakpoint grading, comprehensive evaluation of the habitat quality of the regional highway network is realized, and a scientific basis is provided for ecological protection planning and road construction decision making.

The invention provides a time-varying graph neural network traffic flow prediction method based on a dynamic memory bank, and belongs to the technical field of traffic prediction. The method adopts a layered deep neural network architecture, and comprises a data embedding layer, a space-time coding layer, a memory enhancement layer and a prediction output layer. The data embedding layer preprocesses a traffic flow sequence, associates a collaborative coding time sequence mode with a road network, and synchronously constructs a dynamic graph structure; the space-time coding layer is subjected to space-time stream decoupling extraction, a space branch models multi-scale space dependence through a time delay graph convolution module and a space Mama module, and a time branch extracts multi-granularity time features through a hierarchical time sequence sensing module and a time Mama module; the memory enhancement layer performs pattern matching and reconstruction on the space-time fusion features by means of a dynamic memory bank; and the prediction output layer generates a prediction result by taking the GCRN as a decoder. According to the method, the space-time dependence of the traffic situation is accurately captured, the prediction curve is highly fit with the true value, and the high-precision prediction of the traffic flow is realized.

The invention discloses a county and rural traffic planning decision-making method and system based on a road traffic index. The method comprises the following steps: acquiring navigation API data, social economic data and POI information related to county and rural traffic planning; the method comprises the following steps: acquiring data, processing the data, then calculating a route growth index, a per-hour time index, a holiday influence index, a broken road index and a planning simulation index, carrying out weighted fusion to obtain a road traffic index, when the road traffic index is calculated, distributing an initial weight for each index, and adopting a self-adaptive weight adjustment algorithm to calculate the road traffic index. Dynamically adjusting the weight of each index according to the real-time traffic data and the simulation result; county and rural traffic planning is carried out by taking reduction of the road traffic index as a target, and a road traffic index difference value before and after planning is calculated as a decision basis. According to the invention, the problem of asymmetry of basic traffic management information is effectively solved, and the connection accuracy of the road network is improved.

The invention discloses a multi-modal dynamic compensation road disease intelligent detection and risk assessment system, and relates to the technical field of artificial intelligence and computer vision, and the system comprises an image collection module which is used for obtaining a road surface image in real time through a camera device, and transmitting the image to a preprocessing module; the preprocessing module is electrically connected with the image acquisition module and is used for carrying out graying, noise reduction, contrast enhancement and geometric correction operation on the image and outputting a standardized image; the feature extraction module is electrically connected with the preprocessing module. According to the road disease detection system provided by the invention, by integrating a plurality of modules, high efficiency and intelligence of road disease detection are realized, compared with traditional manual inspection, the system not only improves the detection efficiency, but also remarkably enhances the objectivity and accuracy of detection, and is particularly suitable for real-time monitoring requirements of a large-scale road network; the image acquisition quality is effectively improved, and the effectiveness of feature extraction can be ensured.

The invention relates to the technical field of intelligent traffic, in particular to a dynamic road network collaborative capacity expansion decision-making system based on federated learning-digital twinning, which comprises the following steps of: calibrating pulse type and periodic type data flow weights through a dynamic weight distributor, and generating a normalized feature vector; fusing the normalized feature vectors of all the domains through a privacy protection aggregation engine to obtain a passenger flow pressure distribution prediction matrix; the reconstruction module is used for constructing a digital twinborn body based on the prediction matrix and initializing the digital twinborn body; and based on the initialized twinborn environment, recognizing and sensing a missing region through a blind area data reconstructor, and fusing historical features and real-time data streams of adjacent nodes by adopting a space-time correlation algorithm to reconstruct a complete road network state. According to the method, the dynamic road network collaborative expansion decision system is constructed by fusing federated learning and digital twinning technologies, and the expansion decision efficiency of the traffic road network is improved.

The invention discloses a congestion management system and method based on vehicle-road cooperation and dynamic group path optimization, and relates to the technical field of intelligent traffic systems. The method comprises the following steps: S1, collecting original traffic data through a multi-modal sensor array to carry out space-time alignment, and constructing a microscopic traffic flow data set; s2, bidirectional information interaction is carried out through a vehicle-road cooperative communication network; s3, calculating a multi-modal fusion confidence coefficient parameter based on the data quality index, and generating traffic state information and road section real-time saturation; s4, fusing historical and static road network data, constructing a dynamic digital twinborn model and a road network state feature map, and generating a road network load balancing index; and S5, making a decision by adopting a hierarchical-distributed architecture, and generating a group path induction strategy through multi-objective optimization. The efficient collaborative decision is realized through the graph convolutional network and the attention mechanism, and the road network traffic efficiency is comprehensively improved through stable and reliable multi-objective optimization on the premise of guaranteeing the user fairness.

The invention relates to a tea-picking robot autonomous navigation method based on tea ridge composite boundary identification and driving area adjustment, and belongs to the technical field of robot navigation. The method comprises the steps that tea ridge growth state information is evaluated, and a global path planning strategy is made; a root-ground intersection ground navigation datum line and lateral boundary constraints are established, the ground navigation datum line, the lateral boundary constraints and tea tree growth density and distribution characteristics are integrated, a tea ridge three-dimensional drivable road network model is established, and key geometric characteristics of the model are analyzed to establish an adaptive risk assessment system. Dividing the drivable area into areas with different risk levels; and establishing a multi-objective optimization function in combination with the risk region distribution diagram, fusing the multi-objective optimization function through an exponential weighted fusion mechanism, and generating an optimal navigation path under the risk gradient constraint. Guiding of intelligent navigation of the tea garden is achieved, the tea leaf picking efficiency and quality are improved, and technical support is provided for mechanical and intelligent picking of the tea garden.

The invention discloses an electric vehicle charging load power grid operation real-time scheduling optimization method and system, belongs to the technical field of electric vehicles, and solves the problems of insufficient coordination of new energy consumption and network congestion regulation and weak real-time dynamic response capability in electric vehicle charging load power grid operation real-time scheduling optimization. Comprising the following steps: constructing a vehicle-road-network coupling three-domain knowledge graph fusion framework, bidirectionally coupling electric vehicle charging and discharging behavior dynamic sensing and charging pile load sensing, outputting a final optimization scheme, and pushing the optimization scheme to a user side, a power grid side and a road network side; according to the method, the coupling relation among the vehicle, the network and the power grid is considered, the power grid blocking peak regulation problem is solved through coupling of the three knowledge maps and the charging and discharging characteristics of the electric vehicle, the collaboration of new energy consumption and network blocking regulation and control is enhanced, the real-time dynamic response capability of the electric vehicle is improved, and the power grid blocking peak regulation and control efficiency is improved. And technical reference is provided for power grid operation management personnel.

The invention provides a traffic flow prediction method, system and device and a medium, and belongs to the technical field of intelligent traffic. The method comprises the steps of obtaining multi-source heterogeneous original traffic flow time sequence data, performing preprocessing to generate time sequence tensor data, and constructing graph structure data based on road network topology and traffic flow correlation; inputting the time sequence tensor data and the graph structure data into a pre-constructed time-space diagram neural network model, processing the graph structure data through a graph convolution module of the time-space diagram neural network model to extract spatial features, and processing the time sequence tensor data through a time sequence modeling module of the time-space diagram neural network model to extract time features, fusing the spatial features and the time features to form joint spatial-temporal features; inputting the joint spatial-temporal characteristics into a prediction layer of a spatial-temporal diagram neural network model for processing, and outputting predicted value data of the traffic flow in a future period as prediction result data; and feeding back prediction result data to the traffic control and management system to drive the traffic control and management system to execute control operation.

The invention discloses an overhead transmission line mechanical construction three-map one-table intelligent generation method, which comprises the following steps of: fusing a digital orthoimage model, elevation data and a power transmission and transformation project model through a coordinate transformation matrix, and establishing a three-dimensional scene with a terrain curvature attribute; based on the terrain curvature and equipment coordinates of the three-dimensional scene, a multi-objective optimization algorithm is adopted to synchronously plan a composite path of a cableway, a track and a road, and a road network list map containing an obstacle avoidance topology is generated; recognizing a tower footing projection polygon in the three-dimensional scene, segmenting the types of surface attachments in combination with a digital orthoimage model image, and generating a surface feature list map with a water and soil conservation identifier; and establishing a mapping function of the terrain curvature and the environment water conservation rule, outputting equipment types and spatial poses according to the hoisting safety margin and the earth surface disturbance threshold value, and generating an equipment list map. According to the method, a unified space reference and multi-system dynamic cooperation mechanism is constructed, and the planning precision and construction compliance of three graphs and one table are remarkably improved.

The invention relates to the technical field of traffic control, and provides a traffic adaptive control method and system based on vehicle state perception and an intelligent road network. The method comprises the steps that a vehicle state sensing module is installed on a target vehicle, and a vehicle state sensing multi-dimensional data stream is collected; an intelligent road network module is adopted to monitor a road network multi-dimensional data flow; carrying out fusion modeling on the vehicle state perception multi-dimensional data flow and the road network multi-dimensional data flow to generate a dynamic traffic scene model; traffic operation trend parameters are predicted based on a dynamic traffic scene model, and driving dynamic decision and traffic adaptive control are performed on a target vehicle through the parameters, so that the problem that the state of the target vehicle and the road network condition cannot be acquired and accurately analyzed in real time in the existing traffic control technology is solved. The technical problem that traffic flow and traffic adaptive control effects are poor due to the fact that vehicle state sensing and intelligent road network fusion modeling are solved, and the technical effects that accurate prediction and control of traffic flow and vehicle dynamic states are achieved through vehicle state sensing and intelligent road network fusion modeling, so that traffic management is optimized, and driving safety is improved are achieved.

The invention provides a strong earthquake mountainous area road network toughness improvement decision-making method, and relates to the technical field of road network disaster prevention. The method comprises the following steps: constructing a road network topology model based on road network data; the road network topology model describes a road network connection relation through an adjacent matrix; performing fitting modeling on the regional historical seismic oscillation data to obtain a seismic oscillation intensity probability distribution model; determining a vulnerability quantitative index of each road section based on the road network topology model and the seismic oscillation intensity probability distribution model; determining connectivity of each key node in the road network topology model based on the vulnerability quantitative index of each road section to obtain a road network connectivity reliability index; constructing a comprehensive toughness index based on the vulnerability quantitative index and the road network connectivity reliability index; the comprehensive toughness index is used for generating a toughness improvement decision scheme. According to the method, the accuracy of the seismic toughness of the mountain road network can be improved by constructing a coupling analysis framework of the road network topology model and the seismic oscillation probability model.

The invention discloses a complex terrain-oriented tracked vehicle energy efficiency optimization path planning method and system, and the method comprises the steps: firstly constructing a joint energy consumption model on a rasterized three-dimensional terrain, and superposing the straight rolling resistance, gradient and steering equivalent resistance moment energy consumption to obtain node cost; then, under the framework of a weighted A * function, the product of Euclidean distance and energy consumption rate is used as a heuristic function, and rapid rough search is achieved; halton sampling is carried out on the coarse path nodes, an environment vector is calculated based on the distance between a sample and the center and the gradient difference, offsets are carried out, impassable samples are removed, secondary search is carried out after a probability road network is reconstructed, and the local path quality is improved; and finally, smoothing the broken line by using a three-dimensional fourth-order B spline, eliminating the conflict with the impassable area by densifying nodes, and outputting an executable track with continuous curvature. Simulation shows that compared with A *, Dijkstra and PRM algorithms in complex terrains, the method saves energy by 5.2%-12% on average, and the energy saving performance of the tracked vehicle is achieved.

The invention discloses a global dynamic perception space-time traffic flow prediction method based on graph packet representation learning, and belongs to the technical field of traffic flow prediction, and the method comprises the following steps: S1, traffic data input, S2, traffic graph packet construction, S3, graph packet initial feature extraction, S4, time sequence feature extraction, S5, spatial feature extraction, and S6, traffic flow prediction and output. Through a space-time modeling technology of graph packet representation learning and global dynamic perception, space-time characteristic elements of a traffic road network can be comprehensively covered, traditional traffic indexes such as flow and speed are concerned, elements such as road network topological association and cross-regional multi-hop association are also included, a dynamic dependency relationship between a time sequence and a spatial dimension is deeply mined, and a real-time dynamic perception effect is achieved. Therefore, the prediction result can reflect the real evolution law of the traffic flow more accurately, and a more scientific basis is provided for traffic management and decision making.