845results about How to "Improve traffic efficiency" patented technology

The invention provides a multi-level low-altitude air route network construction method in a complex urban environment, and relates to the technical field of low-altitude air route construction, and the method comprises the steps: building a low-altitude flight rule base; performing three-dimensional discretization on the urban space, and dividing the airspace into three-dimensional grids; designing a risk assessment function R; dividing a plurality of height layers, and distributing the height layers for different types of aircrafts; constructing a multi-stage air route network; constructing an airspace three-dimensional airway model; based on the airspace three-dimensional air route model, adopting a hybrid iterative algorithm to optimize the air route planning of each height layer; performing path smoothing on the discrete path points generated by the hybrid iterative algorithm by adopting a B spline curve, and performing flight performance verification on the smoothed path to obtain a continuous flight path; and generating a low-altitude air route network according to the continuous flight path. According to the method, the defects in the aspects of complex urban environment processing, multi-source data integration, multi-level airspace division, path optimization and the like in the prior art can be overcome, so that safe, efficient and flexible low-altitude flight management is realized.

The invention provides a vehicle collision risk early warning method, system and device and a medium, and belongs to the technical field of vehicle safety control. The method comprises the steps that surrounding environment information of a vehicle is collected in real time through a vehicle built-in sensor and a GPS module; a multi-sensor fusion device technology is used to carry out comprehensive processing on surrounding environment information of a vehicle, and an environment perception model is generated after data integration; based on the environment perception model, target identification is carried out by using a computer vision technology based on a convolutional neural network so as to identify travelers, vehicles, traffic signs and obstacles; performing behavior prediction based on a deep learning model to predict a behavior track of a front vehicle or pedestrian; evaluating a potential collision risk in the current scene in combination with the target recognition result and the behavior prediction result; when a potential collision risk is detected, an early warning signal is sent to a driver, and when the driver does not take corresponding measures, active safety measures are automatically triggered to avoid collision or reduce accident damage.

The invention relates to the technical field of water traffic risk assessment, particularly provides a traffic risk dynamic assessment model construction system, and solves the problem that the intelligent degree and dynamic performance of traffic risk assessment need to be further improved. According to the system, a first modeling assembly constructs a risk propagation model adopting meta-learning; the second modeling component constructs a multi-subject collaborative risk assessment model; an active intervention component; and generating an active intervention strategy. The traffic safety is improved, and the system can effectively reduce the occurrence rate of traffic accidents through real-time risk perception, accurate evaluation and active intervention; a traffic management dynamic risk map and an intervention strategy are optimized, a scientific decision basis is provided for a traffic management department, and the management efficiency is improved; through multi-channel early warning and path induction, safer and more convenient travel experience can be provided for a driver; the development of intelligent traffic is promoted, and technical support is provided for the realization of an intelligent traffic system.

The invention discloses a live-action three-dimensional model construction method, system and device based on a digital city, and the method comprises the steps: obtaining the multi-source sensing data of a city, and carrying out the data preprocessing, and the multi-source sensing data comprises city image data, point cloud data and GIS data; converting the preprocessed multi-source sensing data in different formats into a preset standard format, and performing registration and fusion of the multi-source sensing data to obtain fused multi-source data; performing data optimization on the fused multi-source data, and constructing a city-level three-dimensional real scene model according to the optimized fused multi-source data and a preset three-dimensional space analysis tool; the preset three-dimensional space analysis tool provides space analysis and three-dimensional space visualization functions; according to the invention, efficient and accurate real-scene three-dimensional data of a city is provided, and three-dimensional and visual space information is provided.

The invention discloses a road traffic signal control system and method, and the method comprises the steps: collecting real-time data through a sensor, a camera and a geomagnetic device, combining historical data with emergency analysis, and predicting a flow mode and a change trend; green light time distribution is dynamically optimized, a fixed timing, inductive and adaptive control strategy is adopted, and traffic light duration and priority adjustment is achieved; through regional cooperation and linkage control, green wave bands and branch intersection red light optimization are set, and peak period traffic signals are intelligently dispatched. A machine learning model is used to predict a flow peak value, environment and accident detection data are combined, a signal lamp period is dynamically adjusted, vehicle waiting time, traffic efficiency, carbon emission and traffic satisfaction are evaluated, and a traffic control strategy is comprehensively optimized. According to the road traffic signal control system and method, the traffic flow in different time periods is predicted by adopting the flow prediction model based on big data analysis and machine learning, and the signal period is adjusted in advance.

The invention relates to the field of intelligent traffic management, and discloses a big data intelligent real-time processing analysis method and system, and the method comprises the following steps: obtaining structured, semi-structured and unstructured multi-modal traffic data; preprocessing the data and extracting feature vectors; fusing the feature vectors of different modes through a self-attention mechanism to generate a unified fusion feature; performing time sequence modeling on the fusion features by using a long short-term memory network, and predicting traffic flow; based on a traffic flow prediction result, a deep reinforcement learning algorithm is adopted to dynamically optimize the duration and phase of a traffic signal lamp, and meanwhile, an optimal path is provided for a vehicle in combination with a path recommendation algorithm; and applying the optimized signal control and path recommendation to a real-time traffic management system. According to the invention, intelligent fusion of multi-modal data, accurate traffic flow prediction and intelligent traffic signal control and path recommendation can be realized, and the real-time performance and efficiency of traffic management are improved.

The invention relates to a cloud side-end integrated collaborative digital-intelligent traffic collaborative management and control method, system and device and a medium, and the method comprises the steps: dynamically adjusting the main line speed limit and ramp control rate through driving main line variable speed limit and traffic induction collaborative management and control based on a hierarchical control architecture, so as to enable the performance of a regional road network to be optimal; a collaborative optimization framework is constructed and utilized under a hierarchical control framework, an optimal control scheme of a single ramp is expanded to each partition of a road network in a random environment, and an optimal joint behavior strategy and a behavior selection strategy of each agent are determined, so that adaptive collaborative control of each agent is realized; and a traffic flow optimal equalization strategy of the whole road network is obtained. Therefore, by integrating a main line variable speed limit and traffic induction collaborative management and control technology, a main line and multi-ramp collaborative optimization framework and intelligent agent configuration, optimization of regional road network performance, optimal balance of overall road network traffic flow and improvement of traffic management and control accuracy and response speed are achieved.

The invention relates to the field of construction planning visualization, in particular to a construction planning visualization method and system for a refuse transfer station. The method comprises the following steps: acquiring a satellite positioning map and a refuse transfer station design drawing of a to-be-developed area; image detail reconstruction is carried out on the satellite positioning map, road space distribution fitting is carried out, and a regional road distribution network is constructed; performing surrounding building positioning calculation on the detail optimization reconstruction satellite image based on a regional road distribution network, performing an optimal site selection decision, and extracting an optimal address region image; carrying out transfer station key feature point identification on the refuse transfer station design drawing, then carrying out three-dimensional point cloud modeling, and constructing a transfer station three-dimensional structure model; and according to the optimal address area image, carrying out corresponding foundation accurate matching on the transfer station three-dimensional structure model, carrying out spatial positioning mapping, and constructing a transfer station address twinborn model. According to the method, the operation efficiency of the transfer station is improved, and the rationality of building planning is enhanced.

The invention discloses a road user track and behavior prediction method in a dense heterogeneous traffic environment, and the method comprises the steps: carrying out the target detection and recognition of each road user in a video image sequence frame based on a deep convolutional neural network, and taking a recognition result as a road user type constraint; taking the detected and identified track coordinates and timestamp information of the road user as historical track information of the road user; according to the method, on one hand, a traffic map sequence of a target road user per second within a tf time period after a timestamp t is obtained through prediction, the traffic map sequence is subjected to spectral clustering and then serves as a map regularization constraint in a regularization loss function form to be used for correcting future trajectory coordinates of the road target user output by semi-supervised pipeline prediction; and on the other hand, entering a logistic regression function network after passing through a full-connection network, and outputting the behavior prediction type of the road user. According to the method, the track and behavior of the road user can be accurately predicted in a dense heterogeneous traffic environment.

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 an urban traffic optimization and planning system based on reinforcement learning, and relates to the technical field of computer information, and the system comprises a traffic data acquisition unit, a reinforcement learning processing unit and a traffic planning unit. The traffic data acquisition unit is used for regarding target urban traffic as a network and regarding each intersection as a node, and constructing a nonlinear traffic flow dynamic model to update a multi-dimensional tensor at each time step; the reinforcement learning processing unit is used for taking a signal timing matrix of a signal lamp of a road section between every two nodes of target urban traffic as a state space and calculating a corresponding action space when a target reward function is a maximum value; and the traffic planning unit is used for finishing the traffic planning of the target city according to the action space of each road section. According to the method, the global optimization capability, the multi-objective optimization capability, the adaptive learning capability, the data processing capability, the safety and the interpretability of traffic signal control are remarkably improved.

The invention relates to the technical field of intelligent traffic, in particular to a vehicle driving path control system and method based on intelligent traffic big data, and the method comprises the following steps: a, collecting the traffic data of the real-time position, the road traffic flow and the congestion condition of a vehicle through a real-time traffic data collection module; b, analyzing the historical traffic data through a big data analysis and prediction module, and predicting peak periods, accident risk areas and traffic bottleneck points; according to the invention, through dynamic acquisition and big data analysis of real-time traffic data, the peak period and the congestion area can be accurately predicted, so that the driving path of the vehicle is optimized, congestion is reduced, in addition, through intelligent regulation and control of a signal lamp and a freight vehicle shunting mechanism, the system avoids interference of freight vehicles to public transportation in the peak period, and the traffic safety is improved. The road traffic efficiency is improved, the system also provides real-time path adjustment suggestion and early warning service, and more convenient and safer travel experience is provided for drivers.

The invention provides an intelligent traffic management system and method based on cloud computing, and relates to the technical field of traffic management. The intelligent traffic management system based on cloud computing comprises a multi-source data acquisition module, the multi-source data acquisition module is connected with a data fusion and preprocessing module, and the data fusion and preprocessing module is connected with a behavior prediction module. Real-time data acquisition and a behavior prediction model are utilized, signal lamp time and shunting strategies can be flexibly adjusted, traffic condition changes can be coped with in real time, management efficiency is improved, interaction among all traffic participants is optimized through group behavior analysis, balance and high efficiency of the whole traffic flow are ensured, and the traffic safety is improved. Through reinforcement learning and an adaptive algorithm, the method can continuously learn and optimize a strategy from feedback, dynamically adapts to different cities and traffic scenes, provides personalized suggestions and guidance for each traffic participant through multiple channels, and improves travel experience and safety.

The invention discloses a road right sharing configuration method and a traffic control system, and relates to the technical field of special vehicle road right sharing, and the method comprises the steps: obtaining the real-time position and speed information of a common vehicle and a special vehicle at an intersection; determining the traffic flow condition of the current intersection by using a traffic flow prediction model; according to the real-time position and speed of the special vehicle and the traffic flow condition of the current intersection, the period and phase duration of the signal lamp are dynamically adjusted through a bidirectional dynamic green wave algorithm, a signal lamp control strategy is obtained, and it is ensured that the special vehicle can pass through the intersection without stopping; configuring intersection signal lamps according to the signal lamp control strategy; according to the invention, through combination of prediction of the traffic flow condition of the intersection and the real-time position of the special vehicle, the period duration and the phase duration of the signal lamp at the intersection are automatically adjusted, the influence on common vehicles at other phases is reduced as much as possible while the special vehicle is ensured to pass through the intersection without stopping, thereby realizing dynamic distribution and sharing of the road right of the special vehicle, and improving the traffic safety. And the utilization rate of special lane resources is furthest improved.

The invention relates to the technical field of highway management and control, in particular to a highway hard shoulder opening management and control method based on artificial intelligence. The scheme comprises the steps of collecting traffic flow and vehicle speed in real time; transmitting the preprocessed data to a central control system through a high-speed communication network; testing and optimizing the intelligent decision algorithm by using traffic simulation software, evaluating the performance of the algorithm by simulating different traffic scenes, and adjusting parameters according to a test result; testing and optimizing the optimal strategy by using traffic simulation software, and adjusting parameters according to a test result; integrating the optimized intelligent decision algorithm into a control system of the expressway; the performance of the expressway control system is continuously monitored, the performance comprises the passing efficiency and the accident rate, and the optimal strategy for opening the hard road shoulder is optimized online. According to the scheme, road side sensing is combined with automatic road shoulder development control dynamic adjustment, and safety and passing efficiency of a road system are achieved.

The embodiment of the invention discloses a multi-lane shunting management and control method, equipment and a medium, is applied to the technical field of intelligent traffic, and is used for solving the problem that an existing shunting mode is low in efficiency and low in accuracy. The method comprises the following steps: dividing monitoring sub-regions based on road network data, and determining topological connection data of each sub-region by using historical vehicle driving data; and identifying target traffic characteristics of each sub-region through clustering analysis, and determining a corresponding shunting traffic region according to the target traffic characteristics. And identifying the current congested road section and the congested direction according to the real-time vehicle driving data, and formulating a shunting strategy in combination with the condition of the shunting traffic area. And inputting the passing speed of the current congested road section, the driving data of the shunting traffic area and the shunting strategy into a preset flow prediction model, and outputting and adjusting the shunting strategy according to the model to realize dynamic shunting management and control.

The invention relates to the technical field of intelligent high-speed vehicle passing control, and provides a method for predicting a vehicle driving track under highway passing control based on digital twin tracking simulation, and the method comprises the steps: collecting highway digital twin system, target vehicle and environment data in real time, and carrying out the integration to form an input data set; a future driving track of a vehicle is predicted by using a pre-trained LSTM deep neural network model, and an actual track is collected through devices such as a GPS and the like for verification. And when the prediction deviation exceeds the preset range, the model is dynamically adjusted to improve the accuracy. Prediction results and deviation analysis are transmitted to a background control terminal and a driver mobile terminal in real time, and traffic control, accident early warning and driving aid decision making such as front road condition prediction and suggested driving speed providing are supported. The highway traffic efficiency and safety are effectively improved, the driver experience is enhanced, the development of an intelligent traffic system is promoted, the operation cost is reduced, and remarkable social and economic benefits are achieved.

The invention provides a vehicle speed adjusting method fusing road surface evenness and wheel data and related equipment, and relates to the field of intelligent control. The method comprises the following steps: acquiring a pavement image and point cloud data through monitoring equipment, determining pavement concave-convex height information and state parameters by combining the pavement image and the point cloud data, and inputting the pavement concave-convex height information and the state parameters into a pre-trained flatness evaluation model to obtain a pavement flatness grade. And when the road surface is in the uneven range, acquiring wheel data of the target vehicle, and inputting the road surface flatness grade and the wheel data into the jolting influence evaluation model to obtain jolting data. The vehicle bumping influence level is determined by combining vehicle-mounted sensor data, and then the standard vehicle speed value is determined, so that the current vehicle speed of the target vehicle is controlled, the vehicle speed is accurately adjusted according to the road surface and vehicle conditions, and the driving safety and comfort are improved.

The invention relates to a vehicle obstacle avoidance trajectory planning method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the steps of obtaining information of obstacles around a current vehicle; predicting driving tracks of all obstacles in a future preset time period according to the information of the obstacles around the current vehicle; under the condition that the condition that no fast approaching obstacle exists behind the current vehicle is obtained based on the driving track analysis, target obstacles needing to be subjected to in-lane obstacle avoidance are screened and recognized; calculating a lane occupation space of the target obstacle based on the driving data of the target obstacle; calculating the lane driving space of the current vehicle according to the lane occupation space of the target obstacle; and performing path planning and speed planning solution based on the lane driving space of the current vehicle, and generating an obstacle avoidance track of the current vehicle. By adopting the method, vehicles in the same direction of the adjacent lanes can be effectively avoided during driving on the expressway, and the driving safety is improved.

The invention relates to an intersection lane division and signal timing cooperative control method in an automatic driving environment, and belongs to the field of intelligent traffic. According to the method, lane function division schemes in corresponding directions are published by using lane function adjustment marks, signal timing schemes in corresponding directions are published by using lane-level signal lamps, and personalized suggestion schemes are pushed to network-connected automatic driving vehicles by using a network-connected communication technology, so that lane-level and vehicle-level refined intelligent management and control are realized, and the intelligent management and control efficiency is improved. The intersection traffic efficiency is improved, and intersection road traffic resources are utilized to the maximum extent. According to the invention, the problem of insufficient management refinement of the plane intersection can be overcome, the traffic capacity of the intersection is improved, traffic congestion is reduced, and the utilization rate of road traffic resources is improved.

The invention provides an automatic driving freight formation collaborative decision-making method, device and equipment and a storage medium. Specifically, the invention provides a customized partially observable Markov decision-making process (POMDP) model, and the model aims at freight formation collaborative decision-making in an expressway scene, and enhances perception information of freight formation in a training stage by using a formation control method of a staggered structure. And the effect of the agent decision network in the test stage is further improved. Furthermore, a multi-agent reinforcement learning framework based on a centralized training and decentralized execution (CTDE) training normal form is constructed in combination with a QMIX algorithm, and the safety, the passing efficiency and the energy consumption target of the freight formation are optimized, so that the freight formation is promoted to generate spontaneous cooperative behaviors. Compared with a traditional freight formation control method, the provided model and framework show excellent performance in the aspect of improving freight formation collaborative decision-making.

The invention relates to the technical field of traffic engineering, and discloses an urban air traffic vertical take-off and landing field site selection optimization method based on demand maximization, and the method comprises the following steps: S1, obtaining and preprocessing online car-hailing original data; s2, dividing grids for the research area range, and taking the grid center as an alternative address of a vertical take-off and landing field; s3, screening out online car-hailing travels which are impossible to turn to urban air traffic; s4, constructing a vertical take-off and landing field site selection model based on potential demand coverage maximization; s5, outputting the selected vertical take-off and landing field address and the corresponding urban air traffic travel demand; and S6, outputting the current optimal site selection and demand as a final solution of the model. According to the method, under the condition of the specific number of vertical takeoff and landing fields, the vertical takeoff and landing field addresses which enable potential requirements of urban air traffic to achieve maximum coverage are selected, an effective solution is provided for development of the urban air traffic, and construction of a more efficient, convenient and environment-friendly urban traffic system is facilitated.

The invention relates to a non-signalized intersection cooperative passing method based on IVCPS under passenger and freight mixed traffic, and belongs to the field of intelligent network connection vehicles. The method comprises the step of judging the vehicle density of a non-signalized intersection in an information space. When the vehicle density is low, a real vehicle model is constructed based on a rectangular model, modeling is performed on a non-signalized intersection through a three-vehicle model, meanwhile, modeling is performed on a vehicle track, and a track model is solved to obtain a vehicle passing sequence and a speed plan; when the vehicle density is high, vehicles in a non-signalized intersection area are formed, and cooperative control is carried out through a queue-based double-layer control planning method. According to the double-layer control planning method, the upper layer plans the passing sequence of the motorcade through an improved MCTS algorithm, and the lower layer plans the vehicle track through an MPC algorithm. According to the invention, the control scheme can be adaptively adjusted according to the vehicle density, the traffic efficiency of the non-signalized intersection is optimized, and the fuel consumption is reduced.

The invention relates to a traffic signal control method based on multi-agent deep reinforcement learning. The method comprises the following steps: setting an intelligent agent for an intersection, deploying a fitting network at a cloud end or a server, and performing prediction according to a traffic road network structure and historical traffic flow data to obtain a traffic flow prediction result Y; training each intelligent agent and a fitting network by using the traffic flow prediction result and randomly acquired real-time traffic information of each intersection to obtain each optimized intelligent agent and an optimized fitting network; each optimized intelligent agent performs independent observation according to a traffic flow prediction result Y and respective current real-time traffic information, transmits the result to the optimized fitting network, calculates a global reward and feeds back the global reward to each intelligent agent, and each intelligent agent executes the next action according to the respective current observation result and reward. And forming a current optimal traffic signal control strategy of the target area. According to the invention, the traffic signal coordination control of multiple intersections is realized, and the travel efficiency is obviously improved.

The invention provides a confluence area vehicle control method and system in a man-machine mixed driving environment. The method comprises the following steps: collecting traffic data of a main road and an acceleration ramp of a highway confluence area in real time; taking a position at a certain distance from the convergence point at the upstream of the main road in the convergence area as a first control point, and taking a position at a certain distance from the convergence point at the upstream of the acceleration ramp as a second control point; the type combinations of two vehicles passing through the first control point of the main road of the confluence area are judged, and the optimal speed from the second control point of the acceleration ramp to the confluence point is calculated according to the different type combinations; selecting an optimal driving lane for the vehicle according to the traffic data of each lane of the main road; the driving conditions of the HV, the CAV and the CAV fleet are analyzed according to the traffic data collected in real time, the vehicle speeds of the CAV and the CAV fleet are controlled and adjusted, the optimal driving speed of the HV is prompted, and it is ensured that all vehicles can safely and rapidly pass through the confluence area.

The invention discloses a smart garage gate control system and method applying an image analysis technology, and belongs to the technical field of image analysis, and the method comprises the following steps: collecting an image, obtaining a grayscale image, and analyzing comprehensive grayscale values corresponding to different colors; extracting the contour of the vehicle in the grayscale image, and training a deep learning model; collecting an image when a vehicle to be put in storage arrives at the position of the gate, and obtaining a gray level image; vehicle information of the vehicle is analyzed and recorded, a gate is controlled to be opened, and the vehicle enters a garage; cameras in the garage collect images of passing vehicles, and whether the passing vehicles in the images collected by the cameras are the same vehicle or not is judged; judging whether the passing vehicle leaves the garage or not, predicting the time when the vehicle arrives at the gate, and controlling the gate to open in advance, so that the vehicle leaves the garage; according to the invention, the identification of the vehicle is further limited, the identification accuracy is improved, the out-of-garage time of the vehicle is predicted, and the passing efficiency of the vehicle is improved.

The invention relates to the technical field of traffic monitoring, and particularly discloses an intelligent traffic information processing platform, which comprises a data acquisition module, a data preprocessing module, a traffic mode recognition module, a traffic road flow control module, a traffic road flow prediction module and an information issuing module, and is characterized in that the data acquisition module acquires congested road data in real time; the data preprocessing module performs cleaning, noise reduction and formatting processing on original data acquired by the data preprocessing module; the traffic mode recognition module establishes a congestion solving model by using real-time traffic data to reduce the current road congestion situation; the traffic road flow prediction module analyzes real-time data and historical data, establishes a traffic jam prediction model, and guides a vehicle to select a route in advance. The information issuing module informs a driver of the traffic flow and the prediction result; according to the invention, the road traffic efficiency is improved, the traffic jam is effectively relieved and dealt with in advance, and the road traffic is more intelligent and efficient.

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 discloses an unsteady-state communication environment-oriented intelligent network connection vehicle dynamic control method, relates to the technical field of vehicle networking and intelligent network connection vehicle control, and aims to solve the problem of real-time control of an intelligent network connection vehicle team in an unsteady-state communication environment. The method specifically comprises the following steps: acquiring communication link data and vehicle state data in real time, accurately monitoring unsteady communication parameters such as communication transmission delay and packet loss rate, acquiring vehicle state data such as speed, acceleration and longitudinal position of each vehicle in a motorcade, analyzing communication stability in real time through a multi-dimensional communication environment evaluation index, and determining the state of the vehicle in the motorcade. And finally, according to unsteady state communication environment monitoring and vehicle safety risk assessment results, a dynamic control strategy is adopted to adjust a vehicle control mode, and it is ensured that a motorcade keeps efficient and stable operation in the unsteady state communication environment. By means of the innovative vehicle dynamic control method, the safety and the operation efficiency of the intelligent network connection vehicle team in the unsteady state communication environment are effectively improved.

The invention discloses a dual-mode barrier gate control system and method based on LoRa and Wi-Fi. The method comprises the following steps: S1, system initialization; s2, dynamically selecting a communication mode by adopting a Markov decision process algorithm; s3, analyzing the historical behavior data of the visitor by combining a hypergeometric learning algorithm and a manifold learning algorithm, and generating a dynamic two-dimensional code; s4, encrypting the dynamic two-dimensional code, and transmitting the dynamic two-dimensional code to the visitor terminal through LoRa or Wi-Fi; s5, the visitor scans the two-dimensional code to request for passing, and the edge computing device locally verifies the validity of the two-dimensional code and device binding information; s6, when an abnormal behavior is detected, the system triggers a multi-factor authentication mechanism; s7, after the authentication is passed, the system controls a barrier gate execution mechanism to open a gate, and traffic information is recorded and uploaded to the cloud; and S8, the system collects visitor data for a long time, generates a behavior model by using big data, and optimizes an approval process and two-dimensional code management. According to the invention, the dynamic two-dimensional code is generated by combining LoRa and Wi-Fi dual-mode communication and an intelligent algorithm, so that the dynamic management of visitors is realized.