3512 results about "Road networks" patented technology

The invention provides a dynamic path planning method and system in an intelligent traffic system, and relates to the technical field of intelligent traffic. Constructing a dynamic road network traffic capacity model based on a space-time diagram convolutional network; generating a multi-branch trajectory prediction result with a confidence score, adaptively starting an encryption parameter synchronization mechanism in combination with vehicle density, and generating a candidate path set through multi-agent collaborative game optimization; performing multi-dimensional deviation degree evaluation based on actual driving data and planning expectation; synchronously generating a multi-mode cooperative guidance signal; the system comprises a multi-source data fusion unit, a space-time modeling engine, a hierarchical decision-making system, a path verification and execution module, a closed-loop feedback controller, an event response system and a multi-mode man-machine interface. According to the method, the global resource utilization efficiency is improved through cooperation of data-driven modeling and hierarchical game decision, and the real-time performance, the safety and the system adaptability of path planning are improved through a closed-loop feedback and event response mechanism.

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 provides an AI algorithm-based scenic spot car intelligent path establishing and planning method and system, and relates to the technical field of path planning, and the method comprises the steps: collecting scenic spot road geographic information data, and constructing a weighted road network model; collecting real-time positioning of the trolley and sensor data; using the reinforcement learning model to generate a real-time optimal path planning strategy; calculating an initial recommendation path; road condition changes are monitored, and a path is dynamically adjusted; and outputting the updated optimal path scheme and carrying out navigation. According to the method, intelligent and real-time path planning of the vehicles in the scenic area can be realized.

The invention provides an intelligent traffic decision-making method based on multi-source sensor data, and the method comprises the steps: obtaining vehicle sensor data and road information, and obtaining a standardized traffic flow feature vector through data synchronization, fusion, feature extraction and dimension reduction processing; inputting the standardized traffic flow feature vector into a traffic flow prediction model, and predicting the traffic flow through a vehicle dynamics model constraint state transition matrix; according to the predicted traffic flow and the real-time road condition information, the road congestion state is judged, and a real-time congestion map is generated; in combination with the real-time congestion map, the road network speed limit rule and the starting and ending point position information, an optimal path is planned, and path smoothing processing is performed through a driving behavior mode; and constructing a vehicle intelligent decision-making system, integrating traffic flow prediction, congestion detection and a path planning model, and outputting a vehicle driving strategy according to real-time vehicle sensor data.

The invention discloses a trajectory optimization method based on a multi-source heterogeneous positioning data fusion algorithm, and relates to the technical field of intelligent navigation and high-precision positioning, multi-modal data are acquired through a multi-modal sensor array, positioning redundancy of scenes such as tunnels and indoor scenes is enhanced, a weight distribution strategy is dynamically adjusted through an Actor-Critic network architecture, and the positioning accuracy is improved. The state space input comprises an environment semantic tag, a historical error sequence and a real-time noise variance, the output action space is continuous weight distribution of each data source, a multi-target reward function optimization strategy is combined, scene adaptability is realized, a local SLAM map, inertial navigation error parameters and a weight distribution strategy are shared in real time based on a V2X protocol, and the real-time performance of the system is improved. According to the method, a single device accumulative error is compensated by using adjacent vehicle data, a terminal locally trains an error compensation model, parameters are uploaded to a cloud end through differential privacy encryption, the cloud end adopts a FedAvg algorithm to aggregate a global model and issue the global model, the error difference between devices is inhibited, and dynamic road network updating and scene differentiation model distribution are supported at the same time.

The invention discloses an asphalt pavement health state monitoring system and method, and belongs to the technical field of traffic infrastructure monitoring. The system comprises the following modules: a multi-source data acquisition module used for realizing real-time acquisition of pavement state parameters; the data processing module is used for performing space-time alignment, quality evaluation, exception handling and standardization on the acquired multi-source data; the multi-scale feature extraction module is used for extracting features from the microscopic diseases, the road section states and the network level, and constructing a multi-scale feature representation system; the dynamic graph analysis module is used for constructing a layered dynamic graph structure and realizing unified modeling and analysis of the road network health state; the health assessment module is used for realizing real-time assessment, deterioration trend prediction and risk early warning of the road surface health state; and the decision visualization module is used for displaying the monitoring data, the prediction result and the early warning information, and automatically generating maintenance decision suggestions based on the evaluation result.

The invention belongs to the field of intelligent traffic systems, and relates to a multi-mode space-time traffic flow modeling method supporting large-scale road network real-time prediction, and the method comprises the steps: firstly designing a space-time prediction framework facing a dynamic traffic network, and then carrying out the training to obtain a final prediction model; the space-time prediction framework comprises a data embedding layer, a space-time coding module and a deep modeling and output module based on an expert hybrid mechanism; the data embedding layer comprises two parallel channels of time embedding and spectral domain space embedding and a time-space data fusion module; the space-time coding module comprises a block-level sparse time attention module, a space attention-message passing module and a weighted fusion layer which are parallel; the deep modeling and output module based on the expert hybrid mechanism comprises an MoE dynamic expert modeling module, a full-connection mapping module, a jump connection layer and an output layer; according to the design, the response speed, the prediction precision and the cross-regional adaptive capacity of the model in a high-heterogeneity scene are improved.

The invention discloses a traffic transportation operation monitoring early warning and decision analysis method based on vehicle infrastructure cooperation, and relates to the technical field of intelligent traffic. According to the method, real-time collection of dynamic traffic elements is realized through a multi-dimensional sensing network of a vehicle end, a road side and an environment and V2X communication, time-space reference unification of multi-source data is ensured, a road-vehicle-environment-event semantic network is constructed, multi-dimensional recognition of abnormal events such as accidents, congestion and severe weather is realized in combination with hierarchical feature extraction, and the method has the advantages of being high in practicability and high in practicability. The method is advantaged in that identification accuracy is improved, abnormal event propagation paths can be predicted, global road network situation prediction capability is realized, differential early warning is generated based on a comprehensive risk index, multi-level responses such as traffic signal adjustment and path planning are triggered, response time is greatly shortened, emergency response efficiency is optimized, a decision execution effect real-time feedback mechanism is established, and the method is suitable for popularization and application. The system performance is continuously optimized along with data accumulation, and the defect that a big data platform lacks an intelligent decision closed loop is avoided.

The invention provides a data transmission method and transmission device applied to a vehicle-road cloud collaborative road network system, and the method comprises the steps: collecting vehicle operation state data through a vehicle-mounted terminal, collecting real-time road network topology data through road side equipment, and transmitting the data to a cloud server for data preprocessing; performing feature extraction on the preprocessed data based on a preset time-space correlation model, generating dynamic road network feature data, calling a cooperative transmission model to perform transmission path optimization on the dynamic road network feature data, generating a path optimization parameter set, and performing dynamic adjustment on a target transmission path according to the path optimization parameter set. Generating a self-adaptive transmission path adaptive to the current road network state; and distributing the target vehicle data packet to the roadside node and the cloud node through the self-adaptive transmission path, and carrying out retransmission scheduling on the data packet which is not successfully transmitted based on a feedback confirmation mechanism. According to the invention, the communication efficiency and the system robustness in a vehicle-road cloud collaborative scene can be improved.

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 an emergency resource dynamic scheduling method and system based on artificial intelligence, and the method comprises the steps: building a dynamic road network topology through the fusion processing of multi-source disaster area state data, achieving the dynamic evolution modeling of a road network state based on a space-time attention map network model, and generating a path passing weight coefficient and a disaster diffusion simulation result; a multi-objective optimization model with material transportation timeliness, a path risk coefficient and disaster area demand urgency degree as optimization objectives is established, and space-time constraint conditions and a dynamic weight adjustment mechanism are introduced, so that a material distribution scheme with a space-time attenuation factor is solved; and finally, generating an emergency resource scheduling strategy in combination with path priority planning and a space-time matching algorithm. According to the invention, the timeliness of emergency resource scheduling in a complex environment can be improved.

The invention relates to the technical field of power distribution network source website collaborative planning, and particularly discloses a power distribution network source website collaborative planning method and system considering a V2G mode. The method comprises the following steps: acquiring an existing topological structure of a power distribution network, traffic road network structure data, electric vehicle number space-time distribution data, photovoltaic resource distribution data and a historical daily load power curve set; predicting to obtain a node load power set, a V2G charging and discharging station maximum schedulable power set and a PVG station maximum output power set in various typical day scenes in the future; establishing an upper-layer planning model by taking the minimum total economic cost of the system as a target, and establishing a lower-layer planning model by taking the minimum annual comprehensive operation cost as a target; and solving the bilayer planning model by adopting a nested iterative algorithm, and outputting a power distribution network source website collaborative planning scheme. According to the method, the economical efficiency and reliability of collaborative planning of the power distribution network, the PVG station and the V2G charging and discharging station are improved, the resource utilization efficiency is remarkably improved, and the total economic cost is reduced.

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 expressway emergency management and control method and system, and the method comprises the steps: building a multi-dimensional heterogeneous data fusion model through a time-space diagram neural network according to the traffic flow, meteorological data and accident reports collected by roadside sensing equipment, and carrying out the dynamic prediction of the risk level of a road segment, and obtaining a real-time risk level map of a whole road network; based on the real-time risk level map, outputting a dynamic shunting scheme containing variable lane marks and a speed limiting strategy; according to the dynamic shunting scheme, generating a vehicle trajectory optimization strategy; and outputting an anti-interference optimization control strategy based on a vehicle trajectory optimization strategy in combination with sudden obstacle data polled by the unmanned aerial vehicle in real time. According to the embodiment of the invention, rapid and accurate path adjustment and cooperative control can be carried out, and the emergency management level of the expressway is improved.

The invention relates to the technical field of traffic flow regulation and control, and discloses an electronic information traffic flow automatic regulation and control system based on a wireless sensor network. The multi-source sensing module collects road network multi-dimensional traffic data through a wireless sensing network, and traffic situation characteristics are generated through a specific data fusion method. The spatio-temporal feature fusion module utilizes a spatio-temporal attention mechanism to associate cross-modal features, and the collaborative decision-making module inputs fusion features into a pre-trained distributed decision-making model to generate a regulation and control instruction set. The dynamic game optimization module constructs a multi-target game optimization model, and traffic signal parameters are optimized by adopting a dynamic game strategy decomposition algorithm. And the hierarchical execution module executes regulation and control instructions in a distributed manner through a three-level control architecture of a central decision-making layer, a regional coordination layer and an intersection execution layer. The system can comprehensively collect and fuse traffic data, realizes scientific decision making and accurate regulation and control, effectively improves the road passing efficiency, balances the road network load, and relieves traffic jam.

The invention relates to the technical field of energy scheduling, in particular to an energy system capacity optimization and mobile resource space-time decoupling cooperative scheduling method, system and device under extreme disaster disturbance and a medium. Load change parameters are coupled to form a disaster-causing response database; renewable energy output fluctuation and user load demand deviation are converted into fuzzy variables, and a capacity configuration optimization model is established in combination with a disaster-causing response database to generate multi-energy complementary capacity configuration; mobile resources are introduced to establish an elastic lifting scheduling model, and an elastic lifting scheme is generated by implementing damaged road network topology reconstruction of the mobile resources, space-time transfer chain modeling and action state coordinated regulation and control. According to the method, collaborative decision-making of capacity optimization of equipment and dynamic scheduling of mobile resources is driven through the disaster-causing response database, and dual elastic gains of fixed resource optimization and after-disaster quick response of the mobile resources under extreme disaster events are achieved.

The invention relates to an urban road carbon emission prediction and optimization control method and system, and belongs to the technical field of road traffic carbon emission monitoring, and the method comprises the steps: obtaining real-time monitoring data of a to-be-detected road, and carrying out the preprocessing; performing space-time alignment on the preprocessed real-time monitoring data, extracting dynamic traffic features and static road features, and generating a space-time feature matrix; inputting the spatial-temporal characteristic matrix into a pre-trained carbon emission prediction model to obtain a prediction result matrix of the to-be-detected road; on the basis of the prediction result matrix, identifying a road grid of which the carbon emission intensity prediction value exceeds a preset threshold value as a high emission area; acquiring real-time traffic state data of the high emission area; and based on the real-time traffic state data and the prediction result matrix of the high emission area, constructing a multi-objective optimization function and carrying out solving to obtain an optimization control strategy of the high emission area. According to the invention, traffic carbon emission optimization control and real-time traffic condition synchronization can be realized.

The invention discloses a traffic network multi-mode perception abnormal event early warning method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining a path accident frequent occurrence data set of a target traffic network, constructing an abnormal event prototype set, obtaining the vehicle state information of a target vehicle based on V2X communication, and obtaining the abnormal event prototype set; probability matching is carried out in combination with the multi-modal sensing data, and a target abnormal event prototype is determined; and calling a historical abnormal event trigger feature set to perform feature enhancement, and performing real-time abnormal event early warning on the driving process of the target vehicle by taking the enhanced abnormal event trigger feature as an early warning condition. According to the invention, the technical problems of incomplete traffic abnormal event detection and early warning delay caused by dependence on a single data source and lack of real-time dynamic response capability in the prior art are solved, and the technical effects of comprehensive and real-time traffic abnormal event detection and dynamic early warning are achieved through multi-modal sensing and V2X communication technologies.

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 belongs to the technical field of intelligent traffic, and particularly relates to a Mama-based traffic flow prediction method, which comprises the following steps of: collecting and preprocessing a data set; static graph construction and spatial feature extraction; performing space-time dual-token decoupling embedding; carrying out dual-branch Mama modeling; performing space-time-variable feature fusion; and carrying out multi-step prediction, outputting a prediction result, and finally, outputting a prediction value of a multi-step sequence through projection mapping. The static graph convolutional network extracts spatial correlation from the inherent topology of the road network, and the double-branch Mama architecture captures the time dependence and variable correlation of the traffic flow. According to the space-time double-token mechanism, the independent influence and the synergistic effect between variables are explicitly distinguished by decoupling the action boundaries of multi-source factors such as traffic flow, environment variables and event interference, and the modeling robustness and the interpretability in a multivariable coupling scene are remarkably improved.

The invention relates to the technical field of cooperative path planning, and particularly discloses a vehicle and unmanned aerial vehicle cooperative path planning method in a depopulated area road-network-free environment, and the method comprises the steps: obtaining the terrain data of the depopulated area road-network-free environment, obtaining a grid terrain map, carrying out the region division, outputting a divided sub-region map, and carrying out the vehicle path planning. Carrying out unmanned aerial vehicle path planning; outputting an initial path planning result of an unmanned aerial vehicle; carrying out cooperative task allocation; outputting an optimized vehicle and unmanned aerial vehicle cooperative path planning result; and generating intelligent decision suggestions. According to the method, the problems that the coordination path is invalid, real-time resource change is not considered, the staticizing defect of task allocation exists, a single target is emphasized, multi-target coordination is neglected and the environmental adaptability is insufficient due to the fact that the map is difficult to update in real time in the existing algorithm are solved.

The invention discloses a highway data fusion management method, and relates to the technical field of intelligent traffic management, and the method comprises the steps: collecting the global operation data of a highway in real time through a multi-source heterogeneous sensing network; performing space-time alignment and semantic annotation processing on the operation data to construct a fusion data set; inputting the feature subset into a lightweight space-time fusion model to form a multi-target decision set; executing the traffic control instruction sequence in a preset space-time window according to the multi-target decision set; and generating a self-optimization report including a model iteration path and road network health assessment based on the deviation degree of the execution feedback data and the expected optimization target. According to the expressway data fusion management method provided by the invention, collaborative optimization of passing efficiency and safety of the expressway is guaranteed.

The invention relates to the technical field of traffic control, in particular to a traffic signal cooperative control method based on multi-agent reinforcement learning, and the method comprises the following steps: modeling each signal lamp intersection in a road network as an agent, and constructing a distributed multi-agent control environment; dividing the whole road network into a plurality of small subnets according to spatial correlation, and sharing and aggregating traffic information in the subnets through a neighborhood information sharing mechanism; utilizing a space-time diagram attention network to extract traffic state characteristics including a space-time dependency relationship in an intersection agent and a neighborhood thereof; and defining a traffic state, a traffic signal control strategy, a reward and punishment function, a network architecture and a target function required by training of the distributed intelligent agent, and carrying out joint training on the distributed intelligent agent until a training target is achieved. According to the invention, the real-time sensing capability of the signal control intelligent agent to the traffic flow dynamic state can be improved, and the collaborative decision-making capability among multiple intelligent agents is enhanced.

The invention discloses an urban traffic flow prediction method and system based on time sequence deep learning, and relates to the technical field of intelligent traffic, and the method comprises the steps: 1, collecting traffic flow data and road network topology, and carrying out the preprocessing; 2, constructing a static adjacency matrix and a dynamic adjacency matrix according to the preprocessed traffic flow data and road network topology; 3, calculating a space-time attention weight by adopting a space-time attention mechanism according to the preprocessed traffic flow data; and 4, according to the static adjacency matrix, the dynamic adjacency matrix and the space-time attention weight, using the improved space-time diagram convolutional network to predict and output future traffic flow. The urban traffic flow prediction method combines the space-time diagram convolutional network, the space-time attention mechanism and the adaptive diagram generation mechanism to realize accurate urban traffic flow prediction.

The invention provides a high-precision perception-driven intelligent road network collaborative optimization method and system, and relates to the technical field of intelligent traffic, and the method comprises the steps: collecting road sensing data through multi-sensor fusion, and obtaining multi-source perception data; performing dynamic traffic flow modeling according to the multi-source sensing data to obtain a traffic state prediction result; performing optimization decision on the traffic state prediction result to obtain an intelligent optimization decision; performing road network level cooperative control on the intelligent optimization decision to obtain a road network level cooperative control result; and performing real-time dynamic adjustment by using the road network level cooperative control result to obtain a road network scheduling result. According to the method and the device, the technical targets of global collaborative scheduling, improvement of traffic flow prediction precision, optimization of a real-time dynamic scheduling scheme and improvement of the intelligent level of urban traffic management can be realized, and the technical effects of reducing traffic congestion, improving road traffic efficiency, optimizing traffic resource allocation and improving emergency response capability are achieved.

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 the technical field of intelligent traffic systems, and discloses a hard shoulder driving and variable speed limit cooperative control method and system.The method comprises the steps that traffic data are collected, and the traffic data comprise traffic information and speed limit values; according to the hard shoulder lane-borrowing driving motivation, the lane-borrowing safety condition and the emergency or rescue vehicle requirement of the networked autonomous vehicle, whether the networked autonomous vehicle can run through the hard shoulder lane or not is judged; inputting the collected traffic data into a pre-constructed DDPG algorithm to determine a state, an action space and a reward function of the DDPG algorithm, and outputting an optimal speed limit value; and the optimal speed limit value of each lane of the control road section is generated through a DDPG algorithm, and the network-connected automatic driving vehicle adjusts the vehicle speed according to the optimal speed limit value. The method can be used for highway entrance ramp sections with serious congestion, the average travel time and carbon dioxide emission of the road network can be reduced, and a new method is provided for efficient and low-carbon targets of smart highways in China.

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.