303 results about "Traffic intersection" patented technology

The invention relates to the technical field of smart city traffic, in particular to a smart city traffic indication system and a working method thereof. According to the technical scheme, the system comprises a multi-source heterogeneous data acquisition module, a data preprocessing and fusion module, a feature extraction and analysis module, a traffic flow prediction and signal timing decision module and a signal display and communication module, the acquisition frequency is dynamically adjusted according to the traffic flow. Traffic and meteorological data are dynamically collected through multiple types of sensors, the quality of data preprocessing and fusion is guaranteed, features are accurately extracted, signal timing is optimized through reinforcement learning, the display and communication module effectively transmits information and cooperates with surrounding traffic, emergency situations can be dealt with, tail gas emission is reduced, pedestrian waiting time is shortened, and traffic safety is improved. And a basis can be provided for long-term traffic planning, and the traffic efficiency, the environmental protection property and the pedestrian friendliness are comprehensively improved.

The invention discloses a processing method for dynamically adjusting a traffic light signal scheme according to traffic flow, which comprises the following steps of: deploying sensors including a ground induction coil, a camera and a pedestrian button at each traffic intersection, and preprocessing collected traffic flow and pedestrian flow data; and predicting trends of traffic flow, non-motor vehicle flow and pedestrian flow of each intersection in a short period, modeling traffic light states of each intersection into a dynamic system, calculating an optimal traffic light adjustment strategy, and adjusting the traffic light states in real time. By dynamically adjusting the traffic light signals, the system can more effectively respond to the actual on-site conditions including the flow of pedestrians and non-motor vehicles, more reasonable signal duration distribution is achieved, waiting time is shortened, traffic efficiency is improved, meanwhile, the pedestrians and the non-motor vehicles are considered, the safety of the system can be improved through an optimized signal control strategy, and the traffic light control method is suitable for popularization and application. The signal lamp is adjusted to provide more transition time for pedestrians and non-motor vehicles, so that the accident risk is reduced.

The invention provides a traffic signal lamp intelligent control method based on a fiber bragg grating sensing network, and relates to the technical field of traffic signal lamps, and the method comprises the steps: obtaining the detection data of each fiber bragg grating sensing line, and enabling the fiber bragg grating sensing lines to be transversely laid at a selected traffic crossroad along a lane, three fiber bragg grating sensing lines parallel to the stop line are laid on each road; determining the length range of the motor vehicle queuing length of each lane according to the detection data of each fiber Bragg grating sensing line; and controlling the working state of the corresponding traffic signal lamp according to the length range of the motor vehicle queuing length of each lane. The actual traffic flow can be determined through the detection data of the fiber Bragg grating sensing lines, the green light time can be dynamically allocated based on the actual traffic flow, the vehicle queuing time is shortened, and the passing efficiency can be effectively improved.

The invention discloses a sensing intelligent driving multi-mode traffic signal optimization method and system, and relates to the technical field of traffic signal optimization, and the method comprises the steps: collecting traffic flow data; constructing a multi-mode traffic flow model; connecting a plurality of traffic lights in the target area to obtain a street lamp control signal; based on the traffic flow data and the street lamp control signal, using the multi-mode traffic flow model to predict a traffic flow change trend; and configuring a timing optimization scheme of a plurality of traffic lights in the target area according to the traffic flow change trend prediction result. According to the method and the device, the technical problem of low traffic intersection passing efficiency caused by the fact that a traffic signal lamp timing scheme cannot be dynamically adjusted accurately according to real-time traffic conditions and multi-mode traffic behavior interaction relations in the prior art is solved, and the purposes of optimizing the traffic signal lamp timing of the target area according to the prediction result and improving the traffic signal lamp timing efficiency are achieved. And the traffic efficiency of the traffic intersection is effectively improved.

The invention relates to a visual analysis method for traffic intersection collaborative perception, and the method comprises the following steps: S1, employing an intersection rapid calibration algorithm, and obtaining camera images at different positions; s2, for the camera image of each position, inputting a target detection model to obtain a target detection result; s3, inputting the target detection result of each position into a target tracking model, and outputting a target tracking result; s4, converting the target tracking result into a unified global coordinate system based on the affine transformation matrix, and executing global cross-lens ID matching to obtain a cross-camera tracking result; s5, smoothing the cross-camera tracking result to obtain a smooth tracking result; and S6, performing visual perception based on the smooth tracking result to obtain a visual perception result. Compared with the prior art, the method has the advantages of ensuring that the same target can be correctly associated under different camera visual angles, realizing cross-camera consistency identification and the like.

The invention belongs to the field of visual tracking, and relates to a multi-target visual tracking method based on cooperation of a dynamic neural field and a neural network, which takes a cross-modal cooperation architecture as a core and comprises a dynamic neural field module based on multi-target trajectory maintenance and shielding matching and an improved MoESDQ neural network module. Meanwhile, a collaborative decision-making mechanism is designed, when the activation peak value of the dynamic neural field is attenuated to a preset threshold value, neural network feature matching is triggered, and disappearance target reproduction correlation is achieved based on cosine similarity. The objective of the invention is to solve the visual tracking capability under the condition of scene and target motion change in a monitoring range, for example, under an intelligent traffic intersection scene. The problems of high ID switching rate, multi-target misassociation and low tracking precision under a real-time tracking background caused by scene change or frequent shielding of vehicles and pedestrians, similar target appearances, transient disappearance and reproduction of the targets and sudden illumination change are solved.

The invention belongs to the technical field of intelligent driving decision, and discloses a no-signal crossroad intelligent vehicle decision method, which comprises the following steps: constructing a no-signal crossroad intelligent vehicle decision model including a strategy network and a value network; each of the strategy network and the value network is composed of an LSTM neural network and a full connection layer; the method comprises the following steps of: forming state parameters by self-vehicle information, traffic participation vehicle information, relative position information of a traffic intersection and a self-vehicle and relative position information of a target task point and the self-vehicle from a # imgabs0 # moment to a # imgabs1 # moment as input variables of a strategy network; using an accelerator control signal, a steering wheel angle control signal and a brake control signal corresponding to the # imgabs2 moment state parameter to form an action parameter as an output variable of the strategy network; optimizing the decision model by adopting a PPO algorithm and taking the minimum loss function of the strategy network and the value network as an optimization target to obtain an optimal decision model; and outputting a vehicle decision by using the optimal decision model.

The invention discloses an automatic driving decision-making method and system for a port mixed traffic intersection based on deep reinforcement learning, and relates to the technical field of automatic driving. Comprising the following steps: acquiring real-time state information of automatic driving transportation equipment in a port mixed traffic intersection environment, and fusing the real-time state information into an automatic driving transportation equipment state vector in a heterogeneous manner; constructing a reinforcement learning decision model based on the DDPG; building a port traffic environment simulation platform; the reinforcement learning decision model based on the DDPG is trained; and solidifying and deploying the trained reinforcement learning decision model based on the DDPG in a vehicle-mounted calculation unit of the automatic driving transportation equipment, and controlling the driving of the automatic driving transportation equipment. The traffic efficiency, stability and safety of the automatic driving transportation equipment at the port intersection can be improved, the unmanned transportation process of the port mixed traffic scene is promoted, and the wharf transformation and operation cost is reduced.

The invention relates to the technical field of automatic driving, in particular to an automatic driving system based on pedestrian motivation and a control method, and the system comprises a data acquisition module, a pedestrian detection module, a pedestrian intention reasoning module and a vehicle control module. The data acquisition module is used for acquiring a video sequence and environment information of a traffic intersection when the intelligent driving vehicle drives to the intersection without the signal lamp. The pedestrian detection module detects pedestrians through video processing and a pedestrian recognition strategy and outputs pedestrian inter-frame images. The pedestrian intention reasoning module classifies pedestrian crossing motivations based on the trajectory and attitude information of pedestrians. The vehicle control module dynamically adjusts the vehicle speed according to the pedestrian motivation type and automatically brakes when necessary so as to ensure that pedestrians safely pass through the intersection. According to the system, self-adaptive comity of the intelligent driving vehicle to pedestrians at the intersection without signal lamps is realized, and the traffic safety and the adaptive capacity of intelligent driving are effectively improved.

A method and a system for indicating a presence of a pedestrian using traffic lights at a traffic intersection, the method comprising: setting a human body sensing device to detect whether a pedestrian is present in a pedestrian waiting area. When detecting that there are pedestrians in the pedestrian waiting area, controlling the operation state of a pedestrian warning light among the traffic lights in accordance with the setting position of the human body sensing device and the state information of the driving signal lights of the traffic lights. The method and the system could reduce the likelihood of traffic accidents while protecting the priority right of pedestrians.

The present invention relates to method and system for controlling speed of vehicle by using GPS location, real-time traffic light status and machine learning techniques to recommend cruising speed to vehicle without stopping at the traffic lights. The method includes determining status of traffic lights located on route of traffic junctions and associated time period for status of traffic lights based on data received from sensors. The method includes broadcasting status of traffic lights and associated time period. The method includes calculating in real-time a recommended cruising speed of the vehicle based on GPS location associated with vehicle, status of traffic lights and associated time period, and a time period required by vehicle to arrive at the traffic lights using machine learning techniques. The method includes providing in real-time the recommended cruising speed to vehicle. The method includes adaptively controlling in real-time speed of vehicle to recommended cruising speed.

Technical problems and their solution are disclosed regarding the location of mobile devices requesting services near a site from a server. Embodiments adapt and / or configure the transmitting device near the site, the mobile device communicating with the transmitting device using a short haul wireless communications protocol to deliver a token based upon a key shared with the server but invisible to the mobile device. The server can determine the proximity of the mobile device to the site to control actuation of the requested service or disable the service request, and possibly flushing the service request from the server. Solutions are disclosed for traffic intersections involving one or more traffic lights, elevators in buildings, fire alarms in buildings and valet parking facilities.

Technical problems and their solution are disclosed regarding the location of mobile devices requesting services near a site from a server. Embodiments adapt and / or configure the transmitting device near the site, the mobile device communicating with the transmitting device using a short haul wireless communications protocol to deliver a token based upon a key shared with the server but invisible to the mobile device. The server can determine the proximity of the mobile device to the site to control actuation of the requested service or disable the service request, and possibly flushing the service request from the server. Solutions are disclosed for traffic intersections involving one or more traffic lights, elevators in buildings, fire alarms in buildings and valet parking facilities.

The invention discloses a non-motor vehicle passenger-vehicle identification method and system in a crowded scene, and belongs to the technical field of intelligent traffic management. In order to solve the problem of accurate recognition of non-motor vehicles, persons and vehicles in a crowded scene, image data of a traffic intersection is collected through a traffic camera, target detection processing is performed on the image data by using a target detection model, and a person target and a non-motor vehicle target are detected to obtain a person target frame and a non-motor vehicle target frame; judging whether the scene category to which the image data of the traffic intersection belongs is a crowded scene or not to obtain crowded scene image data; personnel key points and non-motor vehicle key points in the crowded scene image data are extracted; associating the personnel key points with the non-motor vehicle key points to determine personnel key points associated with the non-motor vehicle key points to obtain a personnel-vehicle association target; and identifying whether the person-vehicle associated target has traffic violation behaviors or not through an image tracking technology, and if so, identifying the identity information of the person target for traffic violation early warning and processing.

The invention discloses an urban traffic monitoring management system based on cloud computing, which relates to the technical field of traffic supervision, and is characterized in that traffic associated data of each traffic crossing in an urban road network is acquired, a data topology network is constructed for mapping, and a data transmission link is constructed to send the traffic associated data to a cloud computing terminal; the cloud computing terminal configures computing power resources according to the data volume of the traffic associated data, generates a data to-be-processed task of each traffic crossing, performs feature processing on the data to-be-processed task of each traffic crossing to generate corresponding traffic live features, constructs traffic monitoring nodes, inputs the traffic live features, and transmits the traffic monitoring nodes to the cloud computing terminal. The method comprises the following steps: monitoring traffic events under corresponding traffic crossings, integrating all traffic events in an urban road network to generate a traffic list map and analyzing the traffic list map, further positioning traffic crossings with traffic anomalies, generating abnormal early warning, and performing path decision and travel path planning for traffic execution objects under traffic crossings without traffic anomalies.

The invention discloses an intelligent network connection traffic signal lamp and vehicle cloud side cooperative control method, and the method comprises the steps: predicting the overall traffic flow direction through the traffic flow information of a graph structure formed by the topological relation between a plurality of traffic intersections, and planning the phase duration of each traffic signal lamp according to the predicted traffic flow direction; the method improves the coupling among a plurality of intelligent network connection traffic intersections, employs a federal learning method, builds a cloud-side cooperative training mode of a traffic signal lamp time length planning network, enables each side end device to form a cloud-side cooperative intelligent traffic whole under the guidance of a cloud end device, effectively reduces the training turns of the side end devices, and improves the training efficiency. The training efficiency is improved, and the side end load is reduced.

The invention discloses an image quality enhancement method and system based on an MESH ad-hoc network, and relates to the technical field of ad-hoc network image processing. The image quality enhancement method based on the MESH ad-hoc network comprises the following steps: node data acquisition delay error evaluation; evaluating a signal collision delay error; and evaluating a link fracture delay error. According to the method, whether detection node optimization is carried out or not is judged through a data acquisition delay error result, signal delay error evaluation is carried out through signal collision data so as to judge whether routing signal transmitting power fraction optimization is carried out or not, and link delay error evaluation is carried out through link fracture data so as to judge whether link gain optimization is carried out or not; the effect of improving the accuracy of the real-time passenger flow image analysis of the smart city traffic intersection is achieved, and the problem that the accuracy of the real-time passenger flow image analysis of the smart city traffic intersection is not high due to the fact that node image data corresponds to MESH ad hoc network route switching delay in the multi-hop transmission process in the prior art is solved.

The invention discloses a road traffic safety dynamic early warning system, and relates to the technical field of traffic monitoring, when the system is operated, pedestrian behavior data X at a traffic intersection is acquired in real time through a pedestrian behavior monitoring module, and accurate judgment of pedestrian behaviors is ensured by combining a high-precision image acquisition device. And the risk behavior determination module compares the acquired behavior data X with the traffic light data, accurately identifies the potential risk state of the pedestrian, and effectively reduces the potential traffic safety hazard by triggering an early warning mechanism. With the triggering of the early warning mechanism, early warning prompt information of different levels is generated according to the early warning index Vw of the passing pedestrians, timely warning is provided for traffic management departments and drivers, and the response capability of traffic safety management is further improved. Self-adaptive optimization adjustment can be carried out according to the current traffic signal lamp timing of the traffic intersection, so that the traffic smoothness and safety are optimally balanced.

The invention discloses a trajectory prediction method and device in a traffic intersection scene, a terminal and a storage medium. The method comprises the steps of obtaining a road scene, a traffic signal lamp state and a historical motion trajectory; according to the road scene, a plurality of directed graphs are constructed, and the directed graphs are used for simulating interaction relations among all motion subjects in the road scene; obtaining spatio-temporal characteristics of the motion subjects according to the historical motion trails and the digraphs; determining a plurality of candidate movement action probabilities of the vehicle according to the traffic signal lamp state and the historical movement track; and determining a to-be-moved track of the vehicle according to each spatio-temporal feature and each candidate moving action probability. Due to the fact that the directed graph is constructed for the interaction relation among all the motion subjects in the road scene, candidate motion prediction and the like are carried out, various remarkably different moving modes can be distinguished and predicted more accurately, and the problem that high accuracy is difficult to achieve when behaviors of the motion subjects in a traffic intersection are predicted in the prior art is effectively solved.

The invention discloses an intelligent traffic scheduling method for an electric power signal lamp, and the method employs a management platform module, a data storage module, a data collection module, a monitoring equipment module, an electric power signal module, and an early warning equipment module. The monitoring equipment module is used for acquiring power equipment information, the power signal module is used for being connected with a power equipment host, and the early warning equipment module is used for warning vehicles and pedestrians at a traffic intersection. In the changing process of the power signal lamp, information of power equipment is retrieved, a comparison signal is transmitted to the voice warning indicator and the LED warning lamp, the voice warning indicator and the LED warning lamp warn power maintenance personnel, an alarm signal is transmitted to the maintenance personnel, the maintenance personnel can overhaul abnormal equipment in time, and the maintenance efficiency is improved. And the operation safety of the power equipment is improved.

The present invention discloses a pneumatic lifting speed bump, which comprises a lifting plate, a power assembly and a load-bearing bracket. The lifting plate is composed of longitudinal guide rails and side plates. The side plates are connected to both sides of the longitudinal guide rails. The power assembly is composed of a lifting cylinder, a damping cylinder, a high-pressure gas storage tank and a low-pressure gas storage tank. The high-pressure gas storage tank and the low-pressure gas storage tank are welded by a cross beam frame to form a groove rail with an integral structure. A number of lifting cylinders and damping cylinders are arranged alternately and evenly installed on the cross beam frame inside the groove rail. The width of the longitudinal guide rail corresponds to the groove width of the groove rail. The piston rod end of the lifting cylinder is connected to the longitudinal guide rail, so that the lifting plate and the power assembly form a mechanical body of the pneumatic lifting speed bump. An edge bracket is provided on the outer edge of the groove rail to be located on the load-bearing bracket, and the load-bearing bracket is embedded and installed in a preset ground groove pit at a traffic intersection. The present invention adopts a pneumatic lifting method with multiple cylinders and double gas storage tanks for voltage stabilization, achieving the purpose of a lifting speed bump that can pass smoothly when decelerating, give a warning when speeding, and impose a penalty through jolting.

The invention discloses a traffic signal real-time monitoring control system, which comprises monitoring equipment arranged at each traffic intersection in an area, signal lamp controllers in wireless communication connection with the monitoring equipment, and a central control system connected with all signal lamp controllers in the area, and is characterized in that the monitoring equipment is used for shooting the state of each intersection; the central control system is used for shooting traffic lights, preprocessing and feature extraction are carried out on shot images, state data of all intersections are extracted, the information is transmitted to the traffic light controller, and the traffic light controller adopts a reinforcement learning algorithm to adjust the signal conditions of the traffic lights and sends an adjustment scheme to the central control system. The central control system collects the adjustment scheme of each intersection, readjusts the scheme by adopting Nash equilibrium, and sends the adjusted scheme to the signal lamp controller again, and the signal lamp controller performs signal adjustment according to the adjustment scheme sent by the central control system. According to the invention, the traffic flow at the traffic port is improved, and traffic congestion is reduced.

The invention is applicable to the technical field of node deployment, and particularly relates to an edge sensing node deployment method and system in a deterministic network, and the method comprises the steps: drawing a traffic plane graph in a region, marking intersections, reading the duration of traffic light signals of each intersection, and enabling the intersections with the duration greater than a preset threshold value to serve as traffic light signals of the intersections; the intersection is defined as a high-flow intersection; the method comprises the following steps: acquiring pedestrian crossing monitoring videos at high-flow intersections in a single traffic light period, configuring a corresponding relation between pedestrian intervals and traffic light passing time, selecting standard time from the traffic light passing time, sorting all the high-flow intersections according to a sequence of the pedestrian intervals from small to large under the standard time, and storing the sorted high-flow intersections in the traffic light passing time; and obtaining an intersection people flow list. According to the invention, by determining to read the public service signal, network resources can be shared, the data traffic transmission burden can be reduced, the deployment position of the edge sensing node can be offset, the deployment cost is greatly reduced, and the deployment flexibility is improved.

The present invention relates to a vehicle-road collaborative detection method based on V2X communication and multi-sensor fusion, which belongs to the field of intelligent vehicle target detection. The method comprises the following steps: obtaining a vehicle-perspective road image, point cloud data, and a BEV road image at a traffic intersection, and obtaining a target detection result in the vehicle-perspective image; obtaining vehicle and pedestrian target detection results in a laser radar point cloud, and projecting the result onto a vehicle acquisition image plane; obtaining vehicle and pedestrian target detection results in a BEV image at a traffic intersection by using V2X communication technology; performing a perspective conversion on the BEV image at the traffic intersection by using a weight conversion method based on perspective transformation, and obtaining converted target information; correcting vehicle and pedestrian targets at the vehicle-perspective according to the converted target; and cross-modally fusing the corrected vehicle-perspective target with the projected point cloud target by constructing a convolutional neural network, and obtaining an accurate road target detection result.

The invention relates to the technical field of automatic driving and radar signal processing, in particular to an unmanned vehicle identification method based on radar perception. According to the technical scheme of the unmanned vehicle identification method based on radar perception, radar signals emitted by passing vehicles are collected in real time through a receiver installed at an urban traffic intersection or a key position. Secondly, preprocessing and time-frequency analysis are carried out on the collected signals through wavelet transform, and time-domain features and frequency-domain information of the signals are extracted; next, the processed signal image is identified and classified through an improved YOLOv10 target detection algorithm, whether the image is an LFMCW signal of a vehicle-mounted radar is determined, signal information is analyzed, accurate identification and tracking of an unmanned vehicle in urban traffic are realized, and through combination of radar signal processing and a deep learning technology, accurate identification and tracking of the unmanned vehicle in urban traffic are realized. And the limitation of a traditional visual identification method in a complex environment is effectively overcome.

The invention discloses a congestion traffic road dredging method and system, and the method comprises the steps: synchronously collecting traffic road intersection congestion data through an image collection device and a GPS positioning device which are disposed at a traffic road intersection, and uploading the data to a cloud server; the cloud server issues the congestion data to a corresponding monitoring terminal, and the monitoring terminal obtains related data of a congested road section according to the congestion data; the congestion data and the related data of the congested road section are input into a traffic road dredging virtual machine, the current traffic road congestion condition is simulated, and a plurality of dredging methods are adopted to simulate the dredging process in a virtual environment; acquiring a fastest dredging method for simulating the dredging process in the virtual environment; and the simulated dredging process of the fastest dredging method is sent to traffic road intersection signal lamp control equipment, and related commands of the dredging process are implemented through the traffic road intersection signal lamp control equipment, so that the congestion condition of a plurality of road intersections can be quickly dredged.

The embodiment of the invention provides a vehicle-road cooperation blind area early warning method and device, a storage medium and electronic equipment, and the method comprises the steps: obtaining vehicle-road cooperation data; the vehicle-road cooperation data comprises state information of a plurality of traffic participants respectively sensed by the roadside equipment and the plurality of vehicle terminals at the traffic intersection; the plurality of traffic participants comprise motor vehicles and non-motor traffic participants; under the condition that the motor vehicle is in a moving state, predicting a first moving track of the motor vehicle in a future preset time period and predicting a second moving track of a non-motor traffic person in the future preset time period based on the vehicle-road cooperation data; and according to the distance between the predicted position points of the first moving track and the second moving track at the same moment, controlling roadside alarm equipment to give an alarm. According to the invention, the problem that the dead zone of the large vehicle cannot be fundamentally solved in the prior art is solved.

The invention relates to the technical field of traffic monitoring. The traffic flow dynamic monitoring method and device, the equipment and the medium are provided, and the method comprises the steps that independent game nodes are arranged at key traffic intersections of a target road network in a distributed node deployment mode, and a remote game control framework is constructed; collecting real-time traffic flow data and associated environment parameters, and generating an initial signal control strategy; performing cross-regional information exchange on the initial signal control strategy to generate a traffic resource allocation game model; performing iterative optimization on the traffic resource allocation game model by adopting a distributed Nash equilibrium algorithm to generate a global coordination signal timing scheme; performing scenarized strategy customization on the global coordination signal timing scheme according to the road function partition features to obtain a scenarized signal control instruction; and issuing the scenarized signal control instruction to an execution terminal of a corresponding intersection so as to improve the multi-intersection collaborative decision-making capability, enhance the scenarized strategy adaptability and optimize the dynamic optimization real-time performance.

The invention discloses an intelligent traffic control method and system based on the Internet of Things and a storage medium, the method is applied to an Internet of Things server, and the method comprises the steps: obtaining the traffic flow data of a road traffic intersection and the adjacent traffic flow data of an adjacent intersection corresponding to the road traffic intersection, and according to the traffic flow data and the adjacent traffic flow data, obtaining the traffic flow data of the road traffic intersection; generating a traffic light control scheme of the road traffic intersection; obtaining the residual electric quantity of the electric vehicle, and determining a plurality of target parking lots when the residual electric quantity is smaller than a preset electric quantity threshold value; generating a plurality of charging schemes of the electric vehicle based on the residual electric quantity and each target parking lot, and generating guiding information for guiding a driving path of the electric vehicle based on each charging scheme; and performing intelligent traffic control based on the traffic light control scheme and the guidance information. According to the invention, through refined management and control of intelligent traffic on traffic lights and electric vehicle charging at the intersection, effective support is provided for solving the problems of traffic jam and difficult charging of electric vehicles.

The present invention belongs to the technical field of intelligent remote monitoring and dispatching of smart transportation, and relates to an intelligent remote monitoring and dispatching system for smart transportation. The present invention reduces vehicle waiting time, improves road traffic efficiency, and reduces traffic conflicts and congestion by obtaining traffic signal light dispatching information of each time period at each traffic intersection in the target area. By dynamically adjusting the traffic signal light dispatching information of each traffic signal light in the target area to change the traffic signal light dispatching information of the traffic intersection, it helps to adapt to the traffic demand of the current time period and current road conditions, and reduces the risk of traffic conflicts and collisions caused by the inconsistency between the traffic data of the current time period and the historical data. By obtaining the traffic signal light dispatching information of each traffic signal light guiding the intersection in the target area, it helps to achieve coordinated control between signal lights, forming a "green wave" effect, so that vehicles can pass through multiple intersections continuously, reduce the number of stops and starts, and improve traffic fluency.