3841 results about "Traffic flow" patented technology

The invention discloses a traffic supervision system applied to an intelligent street lamp and an intelligent supervision method thereof, relates to the technical field of intelligent traffic, and solves the problems that an existing intelligent street lamp system lacks a physical-digital mapping relation, edge computing resource allocation is low in efficiency and cloud computing delay is high. According to the scheme, on the basis of multi-sensor data fusion, space-time reference unification is carried out by adopting an atomic clock and a GNSS, and a dynamic causal graph is constructed through a graph neural network, so that abnormal event detection is optimized; an improved Jaccard space-time similarity algorithm is adopted to optimize calculation task allocation, an edge calculation cluster is constructed based on 5G-V2X, and high-risk region identification and traffic flow prediction are carried out; a LiFi or 5G-UWB communication medium is adaptively selected through a multi-modal fusion reinforcement learning algorithm, and efficient early warning information synchronization is realized; according to the method, the multi-source data fusion value and the early warning precision are remarkably improved, the computing power resource utilization rate is optimized, and the instruction real-time performance and the system self-adaptive capability in a complex environment are enhanced.

The invention discloses a road traffic flow prediction method based on a space-time mixed attention network, and the method breaks through the limitation of a conventional time sequence model and a single deep learning architecture based on the systematic analysis of urban road traffic flow space-time heterogeneity, periodic non-stationarity and road network topological relevance, constructs the space-time mixed attention network, and achieves the prediction of road traffic flow. Spatial heterogeneous correlation of road network nodes is captured through a graph convolution network, dynamic time sequence evolution characteristics of traffic flow are modeled by adopting a hybrid architecture, a residual attention mechanism is introduced to realize layer-by-layer refining of multi-scale spatio-temporal characteristics, and the overall architecture of the method has remarkable advantages in the aspects of spatial topology modeling and time dynamic capture compared with a traditional model. Feature decoupling learning is carried out on multi-source heterogeneous data such as weather and events, adaptive integration of environment sensitive features is realized through a parameterized gating fusion strategy, and the prediction error fluctuation amplitude in an extreme weather scene is reduced by 34.8%.

The invention provides a real-time map updating method and system based on multi-source geographic information data fusion. Wherein a three-dimensional model is constructed by integrating traffic flow data, remote sensing images and road network topology, the road traffic pressure is quantified, and a feature correlation index is established. And obtaining a vehicle displacement vector and a speed gradient in combination with laser point cloud and video monitoring data, generating a dynamic road feature data set, and establishing a mapping relationship with the index table. On the basis, a road state prediction model is constructed, and through correlation analysis of traffic pressure data and a real-time feature data set, deep correlation between vehicle motion features and road states is established. And dynamically adjusting a road network connection structure, synchronously updating mapping parameters and predicting model precision, and realizing real-time adaptive adjustment of the road network topology. According to the technical scheme provided by the invention, the real-time performance of map updating and the road condition prediction accuracy are remarkably improved, and the urban traffic congestion index can be reduced.

The invention discloses a traffic flow prediction method based on graph diffusion and dynamic graph fusion. The method comprises the following steps: S1, acquiring historical traffic flow time sequence data of each traffic node in a target road network; s2, preprocessing historical traffic flow time series data to obtain a road network node adjacency matrix; taking the historical traffic flow time sequence data and the road network node adjacency matrix as sample data, and dividing a training set, a verification set and a test set according to a preset proportion; s3, constructing a traffic flow prediction model based on graph diffusion and dynamic graph fusion; and S4, performing model training and verification on the traffic flow prediction model through the training set and the verification set to obtain an optimal traffic flow prediction model, and realizing traffic flow prediction of the test set through the optimal traffic flow prediction model. The problems that an existing method does not have the dynamic topology modeling capacity, the high heterogeneous feature fusion capacity and the self-adaptive space-time modeling capacity, and consequently the bottleneck problem of a current model in the aspects of prediction precision, stability and practicability cannot be effectively solved.

The invention discloses a low-altitude integrated management system based on a grid digital twin model and an intelligent algorithm, and relates to the technical field of low-altitude aircrafts. The method comprises the following steps: multi-source sensing data fusion and grid coding are carried out, and an airspace environment space-time database is constructed; constructing an airspace environment space-time database to carry out three-dimensional subdivision on the airspace; generating a multi-scale grid twinborn body, and associating a corresponding attribute for each level of grid according to a management requirement; generating an airspace risk thermodynamic diagram in the future 5-30 minutes; establishing a grid state change triggering rule; and generating a multi-target optimal path set by taking the grid navigation cost as a weight. According to the invention, by combining the real-time operation data of the low-altitude aircraft, the meteorological environment and other data with the artificial intelligence algorithm and the navigation rule base, the airspace traffic flow is analyzed, the flight plan is optimized, the flight route is intelligently distributed, the automatic route setting of any two points is realized, and the response speed of the low-altitude flight service system and the processing capability of various flight data are improved.

The invention relates to the technical field of signal devices, and discloses a traffic signal control method and system based on vehicle-road cloud multi-modal data fusion, and the method comprises the steps: collecting multi-modal traffic data synchronously in real time through a vehicle-end sensor, road-side sensing equipment and a cloud Internet platform; fusing the heterogeneous data by adopting a space-time alignment algorithm, and constructing a standardized space-time feature matrix; traffic flow prediction is carried out based on a multi-layer space-time diagram neural network trained by a federated learning mechanism, and a signal control instruction is generated through reinforcement learning and a multi-objective optimization model; and issuing the green wave parameter, the dynamic timing scheme and the cross-domain coordination strategy to a roadside signal machine through the cloud edge coordination architecture to execute control. The problems that in the prior art, low-delay private network communication cannot be achieved, the data fusion efficiency is low, unmanned driving is not supported, and the deployment cost is high are solved, and the purposes of low-delay communication, high reliability and low risk are achieved.

The invention provides a multi-modal remote sensing and knowledge graph-based urban planning decision-making method and system, and the method comprises the steps: integrating multi-source heterogeneous data, achieving the feature alignment and fusion of an optical image and SAR data in a satellite remote sensing image through a deep learning technology, and generating an urban ground feature feature vector; associating the urban ground feature feature vector with an urban planning policy database, outputting a structured early warning report of an illegal construction early warning event set and a policy compliance label, and forming a dynamic policy constraint condition for subsequent multi-objective optimization; processing historical traffic flow data based on the dynamic graph model, and outputting a time-space distribution prediction result of future traffic conditions; and generating a Pareto optimal city planning scheme by combining multi-objective optimization with a spatial-temporal distribution prediction result of a future traffic condition. According to the method, high-precision urban surface feature classification, real-time violation extension early warning and traffic flow accurate prediction are realized through multi-modal remote sensing data fusion and a space-time knowledge graph technology, and multi-target optimization and digital twinborn verification are combined, so that the planning efficiency is improved, and extension applications such as carbon neutralization are supported.

The invention provides a non-stationary traffic prediction method based on wave flow decomposition and time delay perception. The method mainly comprises the following steps: constructing a traffic network diagram and inputting historical traffic flow data; constructing a decoupling flow layer, and decoupling the original flow into a wave component and a flow component; constructing a time gating convolution module, and capturing short-time dependency; constructing a space-time causal chain of time-delay perception directed graph attention and processing wave components; constructing an adaptive graph convolutional network, and processing global steady-state features of flow components; constructing a time gating convolution module, and capturing long-term time dependence; constructing an adaptive event fusion module; constructing a full connection layer; and outputting the predicted traffic flow data. The method overcomes the limitation of a traditional method in the aspects of prediction accuracy and coping with a complex traffic network, and effectively solves the problems that a traditional traffic flow prediction method is insufficient in prediction accuracy in an intelligent traffic system, cannot reflect the influence of the traffic network and the like.

The invention provides a high-speed traffic flow high-precision prediction method based on multi-source disturbance characteristics, and relates to the field of data prediction, and the specific steps are as follows: firstly, a multivariable entropy driving interaction field module maps the multi-source disturbance characteristics into a unified energy field, calculates joint information entropy density and constructs a joint interaction field; processing the original feature sequence; secondly, the collaborative disturbance reconstruction module adopts a learnable mapping matrix and a multi-scale mechanism to extract dynamic differences of features under different time scales, and generates enhanced disturbance response features through a decoupling network after global disturbance collaborative response is fused; then, a spatial manifold mapping and partitioning module realizes spatial expression and partitioning modeling of a traffic flow tension evolution trend; and then, the prediction module constructs an asymmetric prediction structure in combination with the disturbance amplitude factor and the weighted disturbance characteristics, adopts a mean square error, introduces a disturbance constraint term to train the model, and outputs a final traffic flow prediction result through the trained high-speed traffic flow prediction model.

The invention discloses an end-cloud cooperative detection method and system for traffic anomalies, and relates to the technical field of intelligent traffic control. The method comprises the following steps: collecting traffic video streams through edge equipment, and identifying abnormal behaviors and generating structured event data by using a lightweight YOLOv3-tiny model; when the confidence exceeds a dynamic threshold and the event type is a high-risk type, uploading a video clip and data to a cloud; the cloud integrates a historical road condition map, meteorological data and a real-time traffic flow state, reconstructs a three-dimensional event scene by adopting a space-time attention pyramid network, and verifies the authenticity of an event in combination with a traffic flow sudden change detection algorithm; and generating a signal lamp forced switching instruction for the risk level overrun event, and issuing the signal lamp forced switching instruction to roadside equipment within 3 seconds to execute emergency response. According to the invention, full-link closed-loop control of traffic accidents from identification to response is realized, and the false alarm probability is greatly reduced while the identification accuracy is guaranteed.

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 city-level path guidance method and system based on dynamic clustering in a vehicle-road cloud cooperation scene, and the method comprises the steps: constructing a vehicle-road cloud cooperation architecture, and collecting traffic data; the traffic management center updates the road weight and initializes a penalty matrix, and performs congestion detection at the same time; when a congested road section is detected, vehicles possibly affected by congestion are screened out, and a to-be-planned vehicle set is formed; performing spatial clustering on the to-be-planned vehicle set to obtain a plurality of vehicle clusters, and constructing an independent path planning sub-graph for each vehicle cluster; distributing priorities for the vehicles in each vehicle cluster; planning an alternative path for the vehicle; performing batch dynamic adjustment on the road weight and the penalty value on the path planning subgraph; the traffic management center issues the planned alternative path to the corresponding vehicle through the road side unit, and the vehicle runs according to the new running path to realize path induction; finally, reasonable distribution of traffic flow, improvement of road network traffic efficiency and balanced allocation of time-space resources are realized.

The invention relates to a gated multi-graph convolution perception modeling method for traffic flow prediction. The method integrates multi-graph structure construction, gating graph convolution and time feature extraction, and aims to solve the problems of strong time fluctuation and heterogeneous spatial relationship in traffic data. The method comprises the following steps of: firstly, respectively constructing a geographic map and a semantic map according to the maximum mutual information measurement between the spatial distribution information of a sensor and historical traffic data; and then, designing a dual-adaptive gating graph convolution module, and dynamically adjusting an information propagation path of a multi-graph structure by introducing an attention mechanism and a gating factor, thereby improving the modeling performance of the model on spatial isomerism dependence. On the time dimension, a time sequence interactive sensing module is constructed in combination with multi-scale causal convolution and an attention mechanism, time dependence characteristics of a short period and a long period are captured, and fusion and expression of time characteristics are completed. According to the method, the modeling precision and stability of the traffic prediction model in a complex traffic scene can be effectively enhanced, and the method has relatively high practical application value.

The invention relates to the technical field of carbon emission prediction, and discloses a combined carbon emission prediction method based on multi-source heterogeneous tensor data. The method comprises the steps that multi-source carbon emission data streams such as industrial emission, traffic flow and energy consumption in a target area are collected, and a carbon emission tensor sequence with the unified space-time dimension is generated through heterogeneous tensor conversion; multi-scale space-time correlation features in the sequence are extracted through a dynamic feature fusion algorithm, and a combined prediction model containing a long-period trend prediction branch and a short-period fluctuation prediction branch is constructed. And iteratively training the model by using a historical tensor sequence until convergence, and inputting a real-time multi-source data stream to output a combined prediction result. According to the method, effective integration and deep feature mining of multi-source heterogeneous data are realized, different change rules of carbon emission are accurately captured through branching model design, the comprehensiveness and reliability of prediction are improved, and scientific reference is provided for carbon emission management and control.

The invention discloses a traffic flow prediction method based on multi-view fusion and diffusion diagram convolution. The traffic flow prediction method is suitable for dynamic modeling and space-time dependence extraction in a complex traffic scene. The method comprises the following steps: firstly, extracting short-term fluctuation and long-term periodic characteristics through double-path time slice convolution, and modeling multi-scale time dependence; in the aspect of spatial modeling, three types of graph structures including a static physical graph, a historical semantic graph and a current feature graph are fused, a sparse dynamic graph is generated through a Top-K mechanism, multi-order diffusion graph convolution is executed in combination with a static graph, and local and global spatial features are extracted. Furthermore, a space-time fusion strategy of bidirectional cross gating is provided, the information circulation direction between short-term and long-term features is dynamically adjusted, and coordinated fusion of multi-scale features is realized. And finally, the traffic state of multiple time steps in the future is output through a gating structure and a non-regression prediction module. According to the method, the modeling capability of the model for the complex traffic dependency relationship is effectively enhanced, and the prediction precision and robustness are remarkably improved.

The invention discloses a multi-modal traffic large model real-time regulation and control method and system for vehicle-road cooperation, and relates to the technical field of artificial intelligence, and the method comprises the steps: combining multi-modal data fusion, edge intelligent reasoning, federated learning, cross-regional knowledge migration, reinforcement learning optimization and adaptive closed loop iteration; and efficient and accurate vehicle-road cooperative regulation and control are realized. The model is adopted to perform space-time alignment and high-dimensional feature extraction on vehicle-mounted, roadside and cloud data, so that the environmental perception precision is improved; cross-regional traffic knowledge sharing is realized through gradient aggregation and decentralized training, and data privacy leakage is avoided; a transfer learning and self-supervision mechanism is adopted, so that the model can quickly adapt to different cities and different road environments, and the generalization ability is improved; by adopting cloud multi-agent reinforcement learning, the optimal decision of signal lamp timing and path recommendation is realized, the traffic flow change is dynamically adapted, and the problem that efficient and real-time model adjustment cannot be realized in consideration of privacy and global optimization in the prior art is solved.

The invention provides a traffic flow prediction method and system based on an embedded physical information deep neural network, and belongs to the technical field of intelligent traffic system and deep learning crossing. The method comprises the following steps: firstly, carrying out variable grid division on an urban high-density road network, and establishing a physical model integrated with signal control to generate traffic state prediction; then constructing a physical information neural network, jointly inputting historical detection data and a physical model prediction result, and embedding a traffic flow conservation equation and a vehicle transmission rule as physical constraints through a loss function; weighted loss is utilized to optimize network parameters, and short-time density, flow and congestion propagation prediction conforming to the traffic flow theory is achieved. The problems that a traditional model is low in precision and a pure data driving method is insufficient in physical consistency are solved, and the accuracy and reliability of urban complex road network traffic situation prediction are remarkably improved.

The invention relates to a multi-scene self-adaptive high-speed obstacle early warning method and device, equipment and a medium. The method comprises the following steps: firstly, acquiring multi-source data of traffic sensing equipment, and carrying out space-time calibration and semantic analysis on the data to obtain a vehicle track feature vector set; thirdly, calculating real-time traffic flow parameters of the gridding area according to the feature vector set, and further generating a traffic feature database; secondly, modeling is carried out on the traffic characteristic database according to time periods and weather, and theoretical speed baseline parameters of all scenes are obtained; and finally, constructing a multi-dimensional feature matrix according to the theoretical speed baseline parameters, inputting the multi-dimensional feature matrix into the classification model to calculate an abnormal probability value, and if it is detected that a continuous abnormal probability value exceeds a dynamic threshold value in a preset time window, generating an early warning signal. By adopting the method, real-time monitoring and early warning of obstacles in multiple scenes of the expressway can be realized, and more effective guarantee is provided for driving safety of the expressway.

The invention belongs to the technical field of intelligent traffic, and particularly discloses a traffic jam analysis method based on an intelligent traffic platform, which comprises the following steps: collecting multi-source traffic flow data of a target road section in real time, judging whether the target road section is jammed in combination with historical data, verifying the traffic flow in a continuous monitoring period to improve the accuracy, and if the target road section is jammed, judging whether the target road section is jammed or not. If yes, the congestion influence range is dynamically determined by identifying a core area and analyzing the upstream and downstream speed propagation trend, then frequent or accidental congestion types are accurately distinguished and the congestion level is evaluated by calculating the deviation degree of current data and a historical traffic mode in multiple dimensions, and finally the congestion influence range is determined according to the congestion types, the congestion level and the increase trend. A differentiated traffic dispersion scheme is generated and executed; according to the invention, through deep fusion of type identification, degree evaluation, range determination and scheme generation links, an integrated decision link is formed, and the intelligent level and response efficiency of the system for coping with a complex congestion scene are significantly improved.

The invention provides a road marking quality intelligent evaluation system based on image analysis, and relates to the technical field of road facility monitoring, and the system comprises a detection unit, a marking quality evaluation unit, a marking wear prediction unit, a GPS positioning unit, a vehicle driving information unit, a control unit and a remote central control unit. The marking quality evaluation unit is constructed based on a differential geometry theory and comprises a curvature flow edge detection module, a marking geometric characteristic manifold representation module and a multi-scale differential invariant evaluation module, the system regards a marking as a two-dimensional manifold, and the marking quality is evaluated by calculating differential geometric quantities such as a Gaussian curvature, an average curvature and a shape index. And constructing geodesic distance measurement on the feature manifold, and evaluating the completeness, visibility and reflective performance of the marked line. And the marking wear prediction unit predicts the service life of the marking based on the traffic flow information and the environment characteristic mapping relation model, and generates a maintenance suggestion.

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 traffic accident prediction method fusing multi-source features and a self-adaptive structure, and the method comprises the steps: extracting spatial features such as a geographic position, traffic flow and interest point distribution, combining the time features such as traffic flow change trend, periodicity and anomaly detection, and the external features such as weather and signal lamp density, and carrying out the prediction of a traffic accident. Node multi-dimensional feature representation is comprehensively constructed, a static adjacency matrix and a dynamic adjacency matrix are respectively constructed, geographic distance and node feature similarity information are fused, a self-adaptive adjacency matrix is generated by utilizing learnable parameters, and road network structure changes are dynamically described. Finally, traffic accidents are modeled and predicted based on a graph convolutional neural network, and accurate identification and early warning of accident risks in a complex traffic environment are realized. According to the method, the modeling capability of the prediction model for nonlinear and strong space-time correlation characteristics of traffic data is effectively improved, the accuracy and robustness of traffic accident prediction are remarkably improved, and the method has wide engineering application prospects and popularization value.

The invention relates to the technical field of traffic prediction, in particular to a multi-layer space-time structure correlation perception traffic flow prediction model, which comprises a space-time structure decomposition layer for decomposing original traffic flow data into a road hierarchical structure feature matrix, a dynamic time period feature matrix and a hidden space topology feature matrix; the dynamic adjacency matrix generation layer is used for constructing a self-adaptive adjacency weight matrix based on the hidden space topological feature matrix; the cross-level correlation perception layer performs bidirectional feature modulation on the road hierarchical structure feature matrix and the dynamic time period feature matrix to generate a space-time coupling feature tensor; and the prediction layer inputs the space-time coupling feature tensor into a space-time diagram convolution prediction network and outputs a traffic flow prediction value in a future time period. According to the method, the expression ability of the model on potential heterogeneous association between the nodes is improved, and the generalization ability across regions and time periods is effectively improved.

The invention provides a traffic control strategy self-adaption method and system based on simulation feedback, and belongs to the technical field of traffic prediction and control, and the method comprises the steps: firstly obtaining traffic control scene data containing traffic flow data and road condition information, then constructing a simulation evaluation environment, configuring scene parameters based on the traffic control scene data, and carrying out the simulation evaluation environment; the method comprises the following steps: simulating traffic operation states under different traffic control strategies, calling a pre-trained reinforcement learning model to perform simulation evaluation on each strategy in a traffic control strategy set, generating a strategy effect feedback set comprising a traffic operation efficiency index and a traffic order stability index, and according to the strategy effect feedback set, calculating the traffic order stability of the traffic control strategy. And performing parameter adjustment on the strategy in consideration of the index association relationship to obtain an adjusted strategy, and finally outputting the adjusted strategy to the traffic control system to realize strategy updating, thereby effectively improving traffic operation efficiency, ensuring traffic order stability, and realizing adaptive optimization of the traffic control strategy.

The invention relates to the technical field of safety early warning, and particularly discloses a vehicle driving safety early warning method fused with meteorological data, which comprises the following steps: acquiring real-time multi-modal data of meteorological, traffic flow and vehicle state of a target road area, performing exception handling, space-time alignment and standardization to form a standardized data sequence, then constructing a multi-modal fusion tensor, and finally performing data fusion on the multi-modal fusion tensor. Extracting each modal dynamic mode, fusing cross-modal features, outputting a joint feature vector, inputting the joint feature vector into a safety risk prediction model to calculate a dynamic safety risk value, combining digital twin simulation risk conduction, generating graded early warning according to a preset threshold value, and performing management and control through vehicle-road collaborative network publishing and high-risk scene linkage traffic facilities. And finally, collecting feedback data evaluation effects, associating decision data to generate hash records, recording the hash records in the block chain, and carrying out federated learning incremental training optimization model based on feedback. According to the invention, accurate early warning under multi-factor coupling can be realized, data privacy is guaranteed, closed-loop optimization is formed, and road traffic safety and stability are improved.

The invention relates to the technical field of smart city data visualization, in particular to a visual smart city display method, which comprises the following steps: S1, collecting traffic flow data, energy consumption data, environment monitoring data and city three-dimensional geographic information data in real time; s2, constructing a reference three-dimensional model with a space coordinate system; s3, mapping the data into a dynamic data layer with a timestamp; s4, superposing the dynamic data layer to the reference three-dimensional model according to space-time coordinates to generate a fused dynamic scene; s5, receiving deduction parameters input by a user, and calculating a corresponding evolution path in real time; and S6, converting the evolution path into a dynamic visualization effect, and outputting the dynamic visualization effect to a display terminal. According to the invention, through integrated processing of multi-source dynamic data fusion, space-time path deduction and visual output, continuous expression and dynamic evolution display of the operation state of the smart city are realized.

The invention provides a low-altitude economic area traffic control optimization method and system, and relates to the technical field of traffic control, and the method comprises the steps: carrying out the spatial division of a low-altitude economic area, generating a multi-layer air corridor topological structure, and mapping the traffic flow data to each layer of air corridor; according to the air corridor capacity constraint of each layer, drawing up a first flight control condition; according to the same-layer air corridor safety distance constraint, a second flight control condition is drawn up; and performing traffic scheduling control in the low-altitude economic area by using the first flight control condition and the second flight control condition. The technical problem that the traffic management and control efficiency of the low-altitude economic area is low due to the fact that the traffic density of the low-altitude economic airspace is increased and airspace resources are difficult to distribute effectively in the prior art is solved, and the traffic management and control efficiency of the low-altitude economic area is improved by comprehensively considering the capacity constraint of the airspace corridors and the safety distance constraint between the airspace corridors on the same layer. And the traffic control efficiency of the low-altitude economic area is improved.

The invention belongs to the technical field of traffic optimization, and particularly relates to a traffic optimization method based on a three-layer Stackelberg game, and the method comprises the steps: obtaining mixed traffic data; according to the mixed traffic data, constructing a three-layer Stackelberg game model; an equilibrium solution of the three-layer game model is solved through a deep reinforcement learning algorithm, an SAC algorithm is introduced into an upper layer to generate a global constraint strategy, and a DDPG algorithm is adopted in a lower layer to optimize cooperative driving and safe obstacle avoidance driving behaviors of the intelligent vehicle; according to the global constraint strategy and the intelligent vehicle driving behavior optimization result, the traffic efficiency, safety and energy consumption and carbon emission indexes of the mixed traffic flow are optimized, the traffic state is continuously updated through behavior feedback data of a middle layer and a lower layer, and strategy optimization is carried out in combination with SAC and DDPG models. Therefore, the problems of insufficient dynamic adaptability, low efficiency of main body interaction processing, limitation of a game model and the like in the prior art are solved.

The invention provides a cellular network flow space-time prediction method and system based on a hybrid neural network, and belongs to the technical field of intelligent communication. The method adopts a layered deep neural network architecture, and comprises a data embedding layer, a space-time coding layer, a feature fusion layer and an output layer. The data embedding layer maps a historical traffic sequence, cross-domain external data and metadata into high-dimensional features; the space-time coding layer is used for respectively fusing one-dimensional causal convolution and a Mama neural network to extract multi-scale time features and densely connecting convolution and a multi-head attention mechanism to capture multi-scale space features through time and space modeling branches; the feature fusion layer realizes adaptive weighted fusion of spatial-temporal features, cross-domain features and metadata features by using a gating fusion mechanism; and the output layer performs linear transformation on the fusion features to generate a final prediction result. According to the method, the spatial-temporal dynamic capture of the service traffic is accurate, the prediction curve is highly fit with the true value, and the accurate prediction of the multi-service traffic of the cellular network is realized.

The invention discloses a traffic operation and maintenance fault intelligent scheduling method and system based on an AI large model, and belongs to the technical field of traffic control. The method comprises the following steps: collecting a vehicle driving track GPS coordinate set, a traffic flow density matrix, a vehicle-mounted camera monitoring image frame sequence and a fault vehicle owner speed anomaly detection result in real time; constructing a traffic operation state analysis model, and outputting real-time traffic operation state characteristics; generating a traffic fault probability distribution curved surface in a future time window; generating a comprehensive fault positioning confidence coefficient matrix; and planning an optimal maintenance resource path according to the pheromone updating rule, and updating the optimal maintenance resource path to the visual scheduling platform in real time. According to the method, space-time diagram convolutional network dynamic modeling is constructed according to multi-source data, so that the limitation of a space blind area of a single data source is broken through, the fault positioning speed is improved, and the problems of incomplete coverage and low positioning speed in the prior art are solved.