312 results about "Road congestion" patented technology

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.

Provided are a method and an apparatus for constructing a road congestion prediction model, a device, a medium, and a product. A road traffic network is defined as a directed weighted graph. Historical dynamic traffic features of each road segment in the road traffic network are obtained as sample data, including recent dynamic traffic features and periodic dynamic traffic features. The sample data is input into a mixture of adaptive graph learners (MAGL) model for learning, and a probability prediction vector is output. The sample data is input into a trend expert model, and a trend distribution vector of a predicted probability of future traffic conditions is output. The periodic dynamic traffic features are fused to determine a periodicity prediction vector. An aggregated logit vector is obtained. An objective function is determined based on the aggregated logit vector. Congestion prediction training is performed to obtain a road congestion prediction model.

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

The invention discloses a smart city public safety management system based on video monitoring, and relates to the technical field of public safety management, original video data streams and a real-time road data set D are collected in real time through an API interface, a time sequence feature matrix TSM and a road congestion coefficient Ck are generated, and the data integration efficiency is improved. The behavior feature extraction module identifies group behavior features through target detection and an optical flow algorithm, and encryption transmission guarantees privacy. The risk prediction module calculates a cross-regional risk index RRI based on federated learning, triggers a response mechanism and accurately early warns risks, and the dynamic decision module combines real-time data to generate a resource scheduling scheme and selects a proper path and callable resources. Through the combination of the execution feedback module and the driving iterative optimization module, the algorithm is dynamically adjusted, closed-loop management is formed, the problems of data splitting, response lag and model stiffness of a traditional system are solved, and the real-time performance, the accuracy and the scene adaptability of public safety management are remarkably improved.

The invention provides an energy recovery control method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining the driving habit data and road condition data of a user, the driving habit of the user being the habit of the user controlling the vehicle speed and the rotating speed, and the road condition data being used for representing the road congestion condition; calculating a target energy recovery torque according to the driving habit data and the road condition data; and controlling the motor controller to output the target energy recovery torque. In the embodiment of the invention, the target energy recovery torque is calculated according to the driving habit and the road condition data of the user. It can be understood that the self-adaptive energy recovery mode in the embodiment of the invention is more in line with the driving habits of users, and even if different users drive vehicles, different target energy recovery torques can be calculated respectively, so that the requirements of different users for energy recovery intensity are met, and the user experience is improved.

The invention belongs to the technical field of data processing, and particularly relates to a vehicle brake reminding method and system based on big data, and the method comprises the steps: collecting environment data around a vehicle through a sensor in the vehicle, and collecting historical driving data, including driving habits of a driver, a vehicle driving track and a road congestion condition; formats of data from different sources are unified, subsequent processing and analysis are facilitated, and data from different sensors and external data sources are fused; based on the driving state of the current vehicle and the surrounding environment data, the road congestion condition and the accident occurrence probability are analyzed in real time, a machine learning or deep learning model is utilized, historical data and real-time data are combined, and the necessity and the emergency degree of braking under the current road condition are evaluated; according to the real-time analysis result and the brake risk assessment, whether the driver needs to be reminded to brake is determined, and the effects of predicting possible brake risks in the future, reminding the driver to pay attention to the front road condition and taking necessary precautionary measures are achieved.

The invention discloses a traffic signal adaptive control method based on a thunder-vision integrated vehicle detector, and relates to the technical field of intelligent traffic, and the method comprises the steps: obtaining the time headway of vehicles in an entrance road section, the number of vehicles entering a target intersection, and the total number of vehicles entering the entrance road section; detecting the number of detained vehicles in each traffic flow direction of the entrance road section, and determining the saturation degree of each traffic flow direction; according to the saturation degree, the effective green light duration of the corresponding phase of each passing flow direction of the target intersection is adjusted, and when it is detected that a congestion flow direction exists, the additionally prolonged effective green light duration is added to the corresponding phase of the congestion flow direction, so that an updated signal timing scheme is obtained; and controlling the traffic lights of the target intersection according to the updated signal timing scheme in the next control period. The signal timing scheme is adjusted in real time according to the change condition of the road traffic flow state, adaptive control of the traffic signal lamp is achieved, and the road congestion problem is effectively relieved.

The invention discloses a charging station load uncertainty quantification method based on space-time traffic flow prediction, and the method comprises the steps: collecting traffic flow data from sensor nodes at two sides of a road, including traffic flow data and traffic flow average speed data; constructing a traffic flow uncertainty prediction model based on the quantile space-time network, and predicting the traffic flow uncertainty by using the model and the traffic flow data; considering the charging electricity price, traffic flow and road congestion factors of the charging station, an improved charging station queuing model is constructed, and the uncertainty prediction result of the traffic flow is converted into the uncertainty result of the load of the charging station. The method fully excavates the space-time coupling characteristics of the traffic flow data, effectively improves the accuracy of traffic flow uncertainty prediction, comprehensively considers the influence of the traffic flow, the charging electricity price and other factors on the charging station load, and further improves the precision of charging station load uncertainty prediction.

The invention discloses a multi-agent-based disaster prevention and risk avoiding greenbelt system layout optimization method and system, and relates to the technical field of urban planning. The method comprises the steps that original geographic data in a to-be-optimized area are received, the original geographic data are preprocessed to form effective geographic data, and the original geographic data comprise original residential area data, original road network data and original disaster prevention and risk avoiding greenbelt data; according to the invention, a multi-agent evacuation simulation model is introduced into the planning design of the disaster prevention and risk avoidance greenbelt, and multiple targets such as road congestion, greenbelt overload and the like are optimized during simulation, so that the real-time escape condition can be accurately simulated, and the real-time disaster risk of the road, the congestion risk of escape crowds and the capacity overload risk of the disaster prevention and risk avoidance greenbelt during disaster can be avoided; and an optimal escape scheme is formulated, the evacuation path and strategy can be dynamically adjusted according to different emergency requirements, and the optimal scheme under the multi-target condition is formulated.

The invention relates to an intelligent bus combined scheduling method and device and a storage medium, and relates to the field of scheduling optimization. Bus card swiping records are called to establish a card swiping station feature set, navigation station triggering is established to carry out navigation record matching analysis, and a space-time popularity prediction map is established; constructing a multi-source traffic flow data set to perform road congestion state prediction and construct a region-level road congestion state diagram; establishing a station passenger flow demand, route travel time and a congestion state according to the space-time popularity prediction map and the region-level road congestion state map; a multi-target scheduling channel is configured to take station passenger flow demands, route travel time and congestion states as input data, and route bus scheduling optimization is carried out through a passenger waiting time target, a scheduling cost target, a vehicle utilization rate target and a congestion influence target. The technical problems of insufficient data utilization, limited prediction precision and lack of flexibility of a scheduling strategy in bus scheduling are solved.

The invention discloses an intelligent logistics scheduling method based on Beidou positioning, and the method comprises the steps: obtaining the real-time position information of a vehicle, and constructing a traffic state matrix in combination with road congestion data; judging a weather influence area based on the weather change trend, and obtaining a weather risk area in combination with the traffic state matrix; obtaining inventory state data, and generating a resource demand priority list in combination with the weather risk area; adopting a multi-objective optimization algorithm, taking the resource demand priority list, the traffic state matrix and the weather risk area as input, obtaining an optimal path set, and generating a preliminary scheduling scheme; if a dynamic task insertion request is received, the task insertion feasibility is judged, the resource demand priority list is adjusted, the multi-objective optimization algorithm is operated again, and a corrected scheduling scheme is generated. Through multi-dimensional data fusion and intelligent algorithm optimization, the real-time performance, flexibility and reliability of logistics scheduling are improved, and an effective solution is provided for resource allocation in a complex environment.

This invention discloses a method, system, and storage medium for electric vehicle charging scheduling based on dynamic data fusion, relating to the field of data processing technology. The method includes the following steps: acquiring the status monitoring parameters of all standby charging interfaces and filtering out available charging piles that meet the requirements; classifying vehicles into charging-priority vehicles and cost-priority vehicles based on the real-time status data and environmental parameters of the electric vehicles requesting charging; for charging-priority vehicles, adjusting the charging pile search range, introducing output power to determine the target charging pile and performing a locking operation; for cost-priority vehicles, calculating the cost optimization coefficient based on arrival time prediction and electricity price fluctuations within the time period, and generating a preferred charging pile sequence. The user finally selects and locks the charging pile, while dynamically adjusting the locking time according to road congestion. This method achieves efficient allocation of charging pile resources, optimizing user experience and economic costs.

The invention provides a method, a device and equipment for predicting road congestion, and a computer storage medium, relates to the technical field of traffic, and is used for improving the speed of discovering congestion of a sensing blind area road section. The method comprises the steps that vehicle driving information corresponding to an upstream area and vehicle driving information corresponding to a downstream area of a target road section are continuously obtained, the upstream area is a vehicle driving-in area relative to the target road section, and the downstream area is a vehicle driving-in area relative to the target road section; wherein the following operations are executed every time the vehicle driving information of the downstream area at a first historical moment and the vehicle driving information of the upstream area at a second historical moment meet a preset flow condition, and it is determined that the target road section is congested, when the vehicle driving information of the downstream area at the first historical moment and the vehicle driving information of the upstream area at the second historical moment meet the preset flow condition; wherein the difference value between the first historical moment and the second historical moment is not greater than a preset time range.

The embodiment of the invention provides a road congestion identification method and device, electronic equipment and a readable storage medium, and the method comprises the steps: determining a vehicle number change value based on a target number corresponding to target vehicles in a target road image and a first number corresponding to first vehicles in a first road image; determining the vehicle similarity between each target vehicle and each first vehicle based on the target vehicle feature corresponding to each target vehicle and the first vehicle feature corresponding to each first vehicle; and determining a road congestion index based on the vehicle similarity, the vehicle number change value and the congestion evaluation coefficient corresponding to each vehicle similarity. In this way, the road congestion degree is accurately evaluated based on the vehicle number change value and the vehicle similarity, and the recognition dimensions of road congestion recognition are enriched. And meanwhile, on the basis of judging whether the road is congested or not, the content of congestion identification is refined, important reference information is provided for traffic management and planning, and the traffic management efficiency is improved.

The invention belongs to the field of urban traffic management, and provides a hierarchical dynamic routing system and method for a computing power-energy fusion network, and the method comprises the steps: dividing a fusion network graph into a plurality of region sub-graphs, and dividing a path table and a connection point path table based on each region sub-graph; monitoring the running states of the road, the edge computing node and the charging station in real time, and predicting the road congestion intensity, the edge computing node queuing estimation information and the charging constraint; and determining a candidate path set in the partition path table and the connection point path table according to an online request of the vehicle, screening each candidate path in the candidate path set based on the road congestion intensity, the edge computing node queuing estimation information and the charging constraint, and determining an optimal path. According to the method, online retrieval is converted into rapid combination and evaluation of small candidate sets from full-graph traversal; traffic, energy and computing power are incorporated into the same decision framework, and systematicness deviation caused by evaluation of the future at present is avoided.

The invention relates to the technical field of traffic management, in particular to a school entrance and exit pedestrian traffic management system and method based on the Internet of Things, and the method comprises the steps: obtaining the road traffic monitoring data of a school entrance and exit in a school leaving period; the road congestion degree is evaluated by extracting vehicle distribution data; evaluating the traffic interference degree by identifying the vehicle driving direction and the mixed driving behavior of the vehicle and the pedestrian; and evaluating the school leaving speed of the students according to the road congestion degree and the traffic interference degree, and adjusting the school leaving strategy according to the school leaving speed of the students. The school leaving speed of the students is evaluated by quantifying the road congestion degree and the traffic interference degree of the school entrances and exits, the school leaving efficiency of the students is improved, and meanwhile the road traffic safety risk of the school entrances and exits is reduced.

The invention relates to the technical field of traffic planning, in particular to a road congestion shunting system suitable for urban design, which comprises a node congestion identification module, a signal time sequence adjustment module, a path interference screening module, a signal priority ordering module and a linkage time period correction module. According to the method, a multi-direction traffic pressure concentration area is formed through path overlapping structure identification, an association boundary of a path group and a node state is opened, a green light starting time sequence is adjusted in combination with a column head static continuous behavior, a vehicle response and period synchronization relation is reconstructed, and structural proximity judgment is introduced in path conflict screening. High-interference paragraphs are identified by utilizing the correlation degree of guidance, speed and lane parameters, a node sorting mechanism dynamically generates priorities through the consistency of a frequent path and a queuing trend, real-time refreshing of a signal response sequence is realized, linkage rhythm correction is bidirectionally verified according to release delay and an adjacent node blocking state, a starting segment is dynamically adjusted, and the linkage rhythm is corrected. Chained dislocation is avoided.

The invention relates to a real-time reminding method and system based on vehicle driving behaviors and electronic equipment, and belongs to the field of intelligent driving. Comprising the steps of collecting monitoring data of vehicle driving in real time, inputting a pre-established violation behavior recognition model, and pre-judging violation behaviors in vehicle driving; if it is judged that the violation behavior exists or the non-standard driving action causes the safety risk, scanning a manifest file of an application program based on the vehicle-mounted voice service to verify the application capability; after verification is completed, a first reminding message is sent to the driving side so as to correct the driving behavior; and responding to the road congestion message displayed by the navigation equipment, generating a navigation plan in combination with the vehicle position and the environmental road information around the vehicle position, and sending a second reminding message to the driving side to re-plan the route. According to the scheme, the problems of lagging and unsafety caused when violation behaviors in driving cannot be judged in time or safety risks exist are solved, the optimal route can be planned again after correction, and the driving experience of a user is further improved.

The invention belongs to the technical field of logistics distribution, and particularly relates to an unmanned aerial vehicle and truck collaborative distribution method based on multi-target reinforcement learning, and the method comprises the steps: order clustering: a logistics service provider carries out the clustering of orders according to a K-means method, enables the orders with the similar distribution time and address to be divided into one order cluster, and carries out the clustering of the orders according to a K-means method; the unmanned aerial vehicles are delivered by the truck and the unmanned aerial vehicles carried by the truck; according to the NSGA-II algorithm, multiple targets such as path cost, distribution time and carbon emission are considered, and optimal paths of multiple schemes are provided; and planning an optimal path by adopting a reinforcement learning method. According to the cooperative logistics distribution method based on the unmanned aerial vehicle and the truck, the three targets of logistics distribution time, cost and carbon emission are considered, the dynamic conditions of low-altitude traffic restriction of the unmanned aerial vehicle, the road congestion condition of the truck, load constraint and the like are also considered, the optimal path is optimized by adopting a reinforcement learning mode, and the logistics distribution efficiency is improved. And parameter setting of reinforcement learning is automatically updated by using a non-dominated sorting algorithm, so that multi-dimensional optimal path selection can be provided for logistics service providers.

The invention discloses an intelligent highway abnormal event identification method based on multi-mode perception, and relates to the field of congestion identification, and the method comprises the steps: collecting the dynamic data of a highway in real time through a sensor network; feature extraction is carried out based on historical and real-time traffic flow data of each road section of the expressway, the road congestion condition is judged through feature fusion, and a congestion index is generated; based on the congestion event, triggering traceability analysis, and generating a feature vector of the source area; carrying out model training through a deep reinforcement learning model, obtaining a trained congestion reason identification and classification model, and obtaining a congestion reason label; and generating early warning information based on an identification result, and updating a navigation route through the linkage navigation platform. The method has the advantages that abnormal events on the expressway are accurately recognized by fusing video monitoring, radar, weather and toll station data, real-time traffic early warning is provided through the deep learning and prediction model, and road safety and traffic efficiency are improved.

The invention discloses a road congestion evaluation method based on a multi-portal road intersection between adjacent portals, and relates to the technical field of traffic data processing, and the method comprises the steps: obtaining each portal interval road section, analyzing the feature index of each portal interval road section, carrying out the adaptive adjustment of a traffic monitoring device, and judging the congestion level of each portal interval road section. And obtaining optical fiber link state information applied to each portal interval road section, and dynamically adjusting the optical fiber link. According to the invention, by analyzing the data of the intersection of the adjacent portal sections and the data of each portal interval section, the complex traffic condition at the intersection of the multi-portal road is comprehensively considered, the congestion level of each portal interval section can be accurately judged, the adaptive adjustment of the traffic monitoring device and the dynamic optimization of the optical fiber link are realized, and the traffic monitoring efficiency is improved. The method provides a numerical basis for traffic management, better adapts to a complex traffic environment, ensures smooth operation of a traffic system, and promotes reasonable allocation of traffic resources.

The invention provides an urban road network traffic congestion state prediction method and system based on deep learning, and relates to the technical field of intelligent traffic control, and the method comprises the steps: obtaining congestion state image data of an urban road network, and carrying out the preprocessing to extract structured road congestion information; constructing a road network topological graph based on the road congestion information, and performing structure sensing state coding on the road network topological graph to generate a structure sensing state vector fusing local congestion features and global topological position information; and inputting the structure perception state vector into an improved deep learning prediction model, performing feature deepening and enhancement through a graph neural network which dynamically adjusts a graph structure and aggregates node information, and finally outputting road network congestion state prediction results of a plurality of time scales in the future. According to the method, accurate prediction of the urban road network congestion state can be realized, and reliable technical support is provided for intelligent traffic management, resource scheduling optimization, travel path planning and the like.

The embodiment of the invention provides a vehicle-mounted instrument road condition information bar drawing method and device, electronic equipment and a vehicle, and relates to the technical field of vehicle-mounted display, and the method comprises the steps: obtaining current road congestion state data and current road congestion distance data; the current road congestion distance data are converted into OPENGL vertex coordinate data, and the current road congestion state data are converted into OPENGL vertex coloring data; using the OPENGL vertex coordinate data and the OPENGL vertex coloring data to draw a road condition information strip thick line; according to the drawn road condition information strip thick line, current road condition information strip texture data is generated; and rendering the texture data of the current road condition information bar, and displaying the current road condition information bar. According to the method, the road condition information bar is drawn by using the line segment drawing function of the OPENGL, so that the computing power consumed by drawing and rendering the road condition information bar of the vehicle-mounted instrument is reduced, and a relatively low resource consumption state is gained for a CPU and a GPU.

The invention provides a precision perception and decision-making integrated traffic control method, system and device, and relates to the technical field of traffic control, and the method comprises the steps: collecting high-precision traffic flow information and pedestrian and non-motor vehicle information of a preset lane range, and constructing a target traffic signal lamp preset range scene model; collecting a high-precision lane image set; vehicle type identification and vehicle distance identification are carried out to generate a road congestion coefficient; positioning the type and the position of the sudden traffic event; performing influence range prediction to obtain a traffic event prediction influence range; and performing traffic signal lamp control decision analysis to obtain a target control parameter, and performing a control decision of a target traffic signal lamp. The technical problems that an existing traffic control system is usually set as timing period signal lamp control, traffic demand differences in different time periods and different road conditions are neglected, flexible adjustment cannot be carried out when sudden traffic accidents occur, and congestion or traffic accidents are caused are solved.

The invention provides a vehicle cruise speed control method and device, a vehicle-mounted electronic control unit and a vehicle, and relates to the technical field of intelligent driving. The method comprises the following steps: acquiring the current speed and navigation map information of a vehicle in an intelligent driving mode; the navigation map information comprises road congestion information and vehicle positions of vehicles; determining a first variable speed of the vehicle according to the road congestion information and the vehicle position; and determining a first variable speed position of the vehicle based on the first variable speed and the current vehicle speed, and controlling the vehicle to perform variable speed cruise driving at the first variable speed position according to the first variable speed. The device is used for executing the method. The variable speed of the vehicle is determined according to the navigation map information of the vehicle, and the variable speed position is determined according to the variable speed and the current vehicle speed of the vehicle, so that the vehicle is controlled to perform variable-speed cruise driving at the variable speed position according to the variable speed. By adjusting the vehicle speed in advance, the probability of occurrence of accidents that the front vehicle collides with the rear vehicle and the rear vehicle collides with the rear vehicle is reduced, and therefore the road passing efficiency and safety are improved.

The invention belongs to the technical field of computers, and particularly discloses a road congestion prediction method and device based on a training model, and the method comprises the steps: obtaining the road information of a to-be-predicted region; based on the road information, performing prediction processing through a pre-trained congestion prediction model to obtain a road congestion result; wherein the road information comprises at least one of the following items: a road image in the prediction area, road event information in the prediction area, time information and weather condition information. According to the invention, the road information in the region is predicted and calculated through the pre-trained congestion prediction model, and the road congestion result of whether the road in the current region is congested within a period of time is obtained. Therefore, the command center can realize congestion prediction at intersections with high traffic flow or intersections with crowded vehicles caused by accidental accidents in a non-fixed time period, so that the vehicle passing efficiency of urban roads in each region is improved.

The invention discloses a flood avoidance transfer multi-objective optimization analysis method considering road congestion constraints. The method comprises the following steps: constructing a road network undirected graph; calculating the shortest distance from each node to the placement area; initializing ant colony and road network parameters, and retaining pheromone concentration and road network congestion degree of the last round of training; traversing all ant colonies, if traversing is not completed, letting k = k + 1, and traversing all ants in the ant colony k; for each ant, selecting a next node according to a heuristic function, pheromone concentration and crowding degree until the ant reaches a resettlement area or an empty node; and judging whether a training termination condition is met or not, if so, outputting a pheromone concentration and placement area matching result, and performing result diagnosis. According to the invention, based on the ant colony algorithm framework, transfer time shortening and transfer batch reduction are taken as optimization objectives, and the problem of multi-objective optimization of transfer area and placement area matching and path selection under the constraint of road congestion is solved.

The invention provides a road congestion evaluation method based on new energy automobile network connection big data, and the method comprises the steps: obtaining the real-time operation data of a new energy automobile, carrying out the preprocessing of the real-time operation data of the new energy automobile, and obtaining a vehicle basic information table; acquiring urban road network data, and analyzing the urban road network data to obtain a road network information table; obtaining a vehicle-road matching relation information table according to the vehicle operation table and the road network information table; obtaining road congestion evaluation information according to the vehicle-road matching relation information table; and obtaining a congestion analysis result according to the road congestion evaluation information. According to the invention, the Internet of Vehicles data and the urban road network data can be obtained, the diversity and real-time performance of the data are ensured, the road congestion condition can be evaluated and predicted more accurately, and more accurate traffic flow prediction and congestion early warning are provided. The evaluation and prediction precision is improved, and the intelligent and informatization level of urban traffic management can be improved.

The invention discloses an urban traffic road path planning method based on big data, and relates to the technical field of intelligent traffic. The method comprises the following steps: loading a city map, decomposing the city map into a plurality of sub-maps, and selecting a center node of each sub-map to replace all nodes in the center node; constructing a mathematical model taking the minimum driving path planning cost as a target function; collecting real-time multi-dimensional traffic data, inputting current position information of a to-be-planned vehicle and position information of a destination, and calculating the minimum distance to the destination; and the optimal path is optimized by combining four influence factors of the driving vehicle in the mathematical model. According to the method, the mathematical model with the minimum driving path planning cost as a target function is constructed, the current position information and the destination position information of the vehicle to be planned are input, the minimum distance to the destination is calculated, the optimal path is optimized in combination with the four influence factors, the travel efficiency is improved, and the traffic road congestion probability is reduced.