794 results about "Traffic conditions" patented technology

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 monitoring and early warning method, device and equipment based on a multi-modal large model and a medium, and relates to the technical field of monitoring and early warning, and the early warning method comprises the following specific steps: S100, multi-modal data collection and fusion: through sensors and cameras deployed in shopping malls, elevators and surrounding road areas, carrying out multi-modal data collection and fusion; the method comprises the following steps: comprehensively collecting multi-modal data of personnel flow, elevator operation and traffic conditions, preprocessing original data, and integrating different modal data into a unified feature vector by using a modal entropy weight fusion function; through a multi-modal data acquisition and fusion technology, the system can comprehensively capture complex environment information of shopping malls, elevators and surrounding road areas, data of different modals are efficiently integrated into a unified feature vector by using a modal entropy weight fusion function, the problem of multi-source heterogeneous data processing is solved, and the system can be widely applied to the field of multi-source heterogeneous data processing. And the accuracy and efficiency of data fusion are greatly improved.

The invention discloses a decision-making model optimization method and device based on a world model, a medium and a product, relates to the technical field of automatic driving, and aims to improve the ability of the world model to understand complex traffic scenes by training the world model in the first stage to understand structured traffic conditions. A world model is trained to predict a future driving scene according to structured traffic conditions and driving actions, and environment prediction and generation quality is improved; and based on the trained world model and the decision model, cooperatively constructing a closed-loop optimization framework based on the world model, inputting the driving action output by the decision model and the corresponding structured traffic condition into the world model, calculating the reward value of the driving action according to the obtained future driving scene sequence, and updating the decision model according to the reward value. According to the method, efficient closed-loop optimization of the decision-making model is realized, the perception capability and decision-making capability of the model can be remarkably improved, the safety and reliability of automatic driving decision-making are improved, and the automatic driving performance of the vehicle in a complex traffic environment is improved.

The invention relates to the technical field of traffic scheduling, in particular to a road traffic analysis scheduling method and system based on event driving. The method comprises the following steps: collecting traffic state data in real time, and generating an original traffic data flow; feature extraction is carried out on the original traffic data flow, a space-time traffic flow graph is constructed, and a dynamic traffic network graph is generated; performing analysis and space-time correlation analysis on the dynamic traffic network diagram to generate a traffic event list; triggering an intelligent scheduling response based on the traffic event list, calling a corresponding scheduling strategy template according to the event type, and generating a candidate scheme set; a multi-objective optimization model is constructed for optimization, and an optimal scheduling scheme is generated; issuing the optimal scheduling scheme to road control equipment, and executing traffic scheduling; and collecting traffic feedback data, evaluating a scheduling effect, and if an expected target is not reached, adjusting scheduling parameters and regenerating an optimization scheme until the traffic state is improved. According to the invention, accurate analysis and efficient scheduling of road traffic can be realized.

The invention discloses a tunnel ventilation control system and method based on artificial intelligence, and relates to the technical field of ventilation control, and the method comprises the steps: in a tunnel design stage, building an unsteady-state computational fluid dynamics model based on tunnel three-dimensional linear parameters, calculating turbulence structures under different traffic conditions through a large eddy simulation method, and calculating an unsteady-state computational fluid dynamics model; determining an optimal space configuration scheme of the fan group according to the distribution of the velocity field and the pressure field; in the tunnel construction stage, multiple types of sensor arrays are arranged along the vault and the side wall of a tunnel in a layered mode; in the tunnel operation stage, real-time vehicle tracks and speed distribution information of a traffic monitoring system are obtained; establishing a ventilation demand dynamic prediction model based on space-time correlation analysis, constructing a fan cooperative control model considering airflow organization optimization, and solving an optimal operation strategy by adopting a multi-target adaptive weight distribution algorithm; when a fire characteristic signal is monitored, the multiple sets of fans are coordinated to form a relay type smoke exhaust airflow organization.

The invention discloses an intelligent parking method and device based on multi-source data fusion and a medium, and relates to the technical field of big data technologies. The method comprises the following steps: collecting multi-source parking data, and analyzing the multi-source parking data to generate a parking lot label library; generating a user tag based on historical search records and residence time preferences of the user, and constructing a user information database; when it is detected that a user triggers a parking request, performing multi-dimensional matching calculation on a user tag and a parking lot tag library to obtain a recommended parking space sequence; and optimizing the recommended parking space sequence based on the real-time traffic state and the user credit score to generate a final navigation path. According to the method, a dynamic parking recommendation mechanism is constructed by collecting and analyzing the multi-source parking data and the user preference data.

The invention provides a highway situation awareness method and system, and the method comprises the following steps: collecting camera video stream data to extract traffic flow data and parking data, and inputting the traffic flow data into a graph neural network to construct a traffic flow propagation model; constructing an abnormal event influence evaluation model based on the recurrent neural network and the long-short-term memory network; and through an abnormal event influence evaluation model, outputting influence range data including an affected road segment set and predicted abnormal recovery time, integrating the data and outputting the data to a visual interface. According to the method, the traffic flow state of the expressway is accurately evaluated by collecting, processing and analyzing the video stream data of the roadside camera, and a reliable situation awareness model is constructed in combination with toll station entrance and exit data, portal snapshot data and the like, so that real-time and accurate monitoring and prediction of the traffic condition of the expressway are realized, powerful decision support is provided for traffic management, and the traffic flow state of the expressway is accurately evaluated. And the operation efficiency of the expressway is improved.

The invention provides a 5G communication-based vehicle state sensing and intelligent traffic cooperative scheduling method, and relates to the technical field of intelligent traffic, and the method comprises the steps: receiving high-frequency vehicle state sensing data transmitted by a vehicle-mounted unit in a target road section in real time through a 5G base station cluster; collecting traffic environment sensing data of a target road section through a roadside sensing unit, fusing the high-frequency vehicle state sensing data with the traffic environment sensing data, and constructing a multi-dimensional real-time traffic situation map; performing collaborative analysis based on the multi-dimensional real-time traffic situation map to generate a traffic control instruction set; and issuing to a road side execution unit and a vehicle-mounted unit for cooperative scheduling control of the traffic flow. The technical problems that a traffic system in the prior art often depends on periodic data acquisition and a fixed signal control scheme, traffic control adjustment cannot be carried out in real time according to changing traffic conditions, traffic flow fluctuation or emergencies cannot be rapidly coped with, and then the traffic system is low in efficiency and unstable are solved.

The invention relates to the field of intelligent logistics transportation carbon emission real-time tracking, in particular to an intelligent logistics transportation carbon emission real-time tracking system and method. The method comprises the following steps: firstly, dividing a logistics transportation path into path sections, and generating a candidate path section set at the starting point of each path section to obtain candidate path sections; then, on the basis of the obtained traffic data, vehicle parameters and scheduling plans of the candidate path segments, the predicted carbon emission of the candidate path segments is calculated through a path segment carbon emission prediction algorithm; constructing an optimal path set based on the predicted carbon emissions of the candidate path segments; and finally, on the basis of the path segments in the optimal path set, obtaining actual operation data of the vehicle, and on the basis of the actual operation data of the vehicle, calculating the actual carbon emission. The technical problems that carbon emission prediction and optimization lack pertinence, obvious influences of speed change behaviors such as acceleration, deceleration and idling on energy consumption in actual driving are ignored, and comprehensive influences of vehicle load weight, driving speed and traffic conditions cannot be dynamically reflected are solved.

The invention relates to the technical field of traffic data analysis, and provides a cold-chain transportation route optimization method and system based on deep learning, and the method comprises the steps: firstly collecting a target data set containing traffic state information, cargo characteristic data and vehicle scheduling information in a cold-chain transportation scene; then carrying out association mapping processing based on the cargo characteristic data and the vehicle scheduling information, and generating a cold chain transportation line network comprising a plurality of transportation nodes and line sides; identifying the traffic condition of the transportation node by using the traffic state information, and inferring and analyzing by combining the influence relationship between the nodes to obtain the congestion distribution characteristics of the line nodes and the cold chain transportation delay label; and finally, in response to an instant transportation demand instruction, processing the original traffic area node network in combination with the above characteristics and a message passing mechanism of the pre-training graph neural network, updating node state information and connection weight, and generating a cold chain transportation optimization line matching the instant transportation demand, and the method can improve the cold chain transportation efficiency and reliability.

The invention provides a traffic data analysis method and device based on multi-channel video fusion, and relates to the technical field of traffic condition analysis, and the method comprises the steps: collecting a multi-channel traffic video stream set; standardization processing and down-sampling processing are carried out, and background identification removal is carried out on the multi-path traffic video frame set; arranging the multi-path traffic foreground image frame set according to time sequence information, carrying out traffic data analysis and abnormal target identification, and determining multi-path traffic operation parameters and abnormal traffic target information; space coding is carried out, and alignment fusion is carried out on the multiple traffic foreground sequence frame sets; and performing parallel tracking processing to obtain a target traffic data analysis result. Through the method and the device, the technical problem of low data processing efficiency caused by lack of an efficient multi-source data fusion mechanism in the prior art can be solved, and the accuracy and comprehensiveness of traffic data analysis are improved through a multi-channel video fusion technology and selection and deployment of monitoring nodes.

The invention discloses a non-contact monitoring device for pavement settlement. The non-contact monitoring device comprises monitoring equipment and a plurality of light reflecting units, the plurality of light reflecting units are arranged on monitoring points located in a monitoring area and datum points located in a stable area; the monitoring equipment comprises an image acquisition device which is used for shooting a reference point and each shooting position before monitoring and during monitoring to obtain an initial reference image and a target image; and the controller is used for acquiring the absolute angle of each shooting position when the initial reference image is shot, judging whether the monitoring point in the target image is shielded or not according to the initial reference image, and calculating the settlement amount of the monitoring point according to the initial reference image and the effective target image after the effective target image without shielding at the current shooting position is obtained. And switching to the next shooting position according to the absolute angle until the settlement amount of all the monitoring points is calculated. According to the invention, real-time monitoring of non-contact, submillimeter-level and full-road-section coverage of the pavement settlement condition under the normal traffic condition of vehicles is realized.

The invention discloses a traffic jam prediction management method and system based on artificial intelligence, and a medium, and relates to the technical field of intelligent traffic systems, and the method comprises the steps: collecting multi-source traffic data to construct a space-time traffic data set, carrying out the traffic state classification based on the space-time traffic data set, and generating a multistage jam probability distribution diagram; performing congestion prediction in combination with the multi-stage congestion probability distribution diagram and the real-time traffic event data, and determining a congestion evolution path; and carrying out traffic management according to the congestion evolution path, generating a dynamic dispersion instruction set, and sending the dynamic dispersion instruction set to traffic control equipment of the target road section to carry out traffic congestion management. The technical problems that a traditional traffic management means cannot adapt to complex and changeable traffic conditions, and accurate congestion prediction and efficient management are difficult to achieve are solved, accurate prediction of traffic congestion is achieved, a dynamic dispersion strategy is efficiently generated and executed according to the real-time traffic conditions, and the traffic congestion prediction efficiency is improved. Therefore, the traffic management efficiency and the road traffic capacity are improved.

The invention relates to the technical field of intelligent traffic, and discloses an intelligent servo barrier gate management system. According to the system, vehicle identification information, detection information, environmental parameters and a barrier gate equipment state are acquired in real time through the data acquisition unit, comprehensive monitoring of traffic flow is ensured, the processing and analysis unit processes the acquired data by using a deep learning model, a control instruction is quickly generated, and the vehicle passing efficiency is evaluated, so that the traffic flow is ensured. The servo control unit is combined with an adaptive control algorithm to dynamically adjust the switching speed, the torque and the acceleration of the barrier gate, the vehicle passing experience is optimized, the queuing time is reduced, the bidirectional communication capability of the communication unit realizes remote monitoring and adjustment of the state of the barrier gate, and the traffic management decision making efficiency is improved. A manager can timely respond to sudden traffic conditions, the response speed and accuracy of the system are improved, the intelligent system effectively relieves the bottleneck of traditional barrier gate management, and the intelligent level of urban traffic is improved.

The invention discloses an online car-hailing order allocation method and system based on an artificial intelligence algorithm, and relates to the technical field of intelligent scheduling, and the method comprises the steps: obtaining to-be-allocated order data and driver data; determining a candidate driver set by adopting a dynamic search radius and a multi-state driver screening strategy, and calculating an estimated arrival time in combination with a real-time traffic condition; establishing a multi-factor order receiving willingness evaluation model, and calculating the order receiving probability of each order-driver combination; constructing a comprehensive matching degree evaluation model, calculating the matching degree of all order-driver combinations, and generating a matching degree matrix; processing the matching degree matrix by adopting a global optimization algorithm, and outputting an order-driver allocation result; and pushing an order to a driver in the order-driver allocation result, monitoring a response state, and performing reallocation processing on a non-response condition. Through the multi-factor order receiving willingness evaluation and the global optimization algorithm, the success rate and matching precision of order allocation are improved, the order waiting time is shortened, and the overall allocation efficiency is improved.

The invention discloses an intelligent traffic regulation and control method and system for cities, relates to the technical field of intelligent traffic, and realizes high-precision real-time sensing of complex traffic states of urban intersections by constructing a multi-source traffic data acquisition system. A multi-modal feature fusion and semantic tag matching mechanism is adopted to recognize overlapping and dynamic changes of traffic states, a multi-strategy linkage signal timing scheme is generated in time, and the problems that a traditional signal control system is lagged in response and single in state recognition are solved. According to the method, commuting and logistics interference indexes are introduced, a release sequence adjustment and channel optimization strategy is intelligently generated according to a people flow peak and logistics concurrence situation, and the method aims at solving the problem of intersection signal timing regulation and control under the condition of complex multi-mode traffic flow superposition. And meanwhile, an execution result is collected in a closed-loop manner, a strategy deviation value is calculated in real time, error tolerance is set, adaptive iterative optimization is supported, and stable convergence of the strategy and achievement of a regulation target are ensured.

The invention discloses a traffic drainage signal management method and system based on a Beidou system, and relates to the technical field of Beidou satellite navigation, and the method comprises the steps: obtaining the real-time traffic data of the position and speed of a vehicle at each intersection based on the Beidou system, and transmitting the real-time traffic data to a traffic management platform; carrying out feature matching on the accident identification model trained based on historical emergency data and a deep learning algorithm and real-time traffic data, and identifying an emergency; and constructing a real-time traffic situation map in combination with the identified emergency information and the traffic condition information of the intersections. The vehicle position and speed data are obtained in real time through the Beidou system, emergencies are rapidly recognized and signal lamp timing is dynamically adjusted in combination with a deep learning algorithm, and compared with a traditional fixed timing scheme, traffic flow changes can be responded in real time, frequent start and stop of vehicles are reduced, the road passing efficiency is improved, and the traffic safety is improved. And particularly, in a sudden traffic accident or construction scene, a signal strategy can be quickly optimized, and traffic congestion aggravation is avoided.

The invention discloses a double-level decision-making method of an urban expressway autonomous lane changing system, which comprises an upper-level decision-making method and a lower-level decision-making method, the upper-level decision-making method is used for calculating lane passing efficiency, and the lower-level decision-making method is used for analyzing lane changing safety. According to the method, a closed-loop behavior decision-making system of lane keeping-lane changing preparation-lane changing execution is constructed, the driving behavior of the intelligent vehicle can be described more accurately, and a clearer framework is provided for subsequent decision-making control. According to the method, the lane passing efficiency driving strategy is adopted for the autonomous lane changing intention, quantification and modeling are carried out by referring to the psychological mechanism that a human driver generates the lane changing intention when encountering a low-speed front vehicle, the decision logic under the actual driving situation is better met, and the intelligent vehicle lane changing decision is more reasonable and humanized. The invention provides a depreciation function calculation method, the influence of the traffic condition on the vehicle driving efficiency can be meticulously measured from the lane level, and a more scientific and more targeted basis is provided for intelligent vehicle lane changing decision making.

The invention discloses a traffic planning method and device, a storage medium and a program product, and relates to the technical field of automatic driving. The method comprises the following steps: acquiring vehicle driving data and a real-time traffic condition; according to the vehicle driving data and the real-time traffic condition, a plurality of particles are initialized, and each particle represents an acceleration sequence of the vehicle in a future preset time period; iteratively updating the particles until convergence, and obtaining a preliminarily optimized acceleration sequence; and a quadratic programming algorithm is applied to smooth the preliminarily optimized acceleration sequence, a target acceleration sequence is obtained, and the target acceleration sequence is used for controlling the vehicle to run. The technical problem that a rule-based speed planning method in the prior art lacks flexibility and is difficult to deal with a complex traffic environment is solved.

A system includes a processor that is configured to acquire traffic video, analyze the acquired traffic video to determine traffic conditions, and optimize traffic signal timing based on the traffic conditions, and apply the optimized signal timing to a traffic signal.

The invention discloses a structure health monitoring method based on a multi-parameter integrated sensor, relates to the technical field of bridge structure health monitoring, and solves the problems that in the prior art, bridge structure health detection is not comprehensive, and bridge structure response is separated from the actual use state. The method comprises the steps that a comparison bridge similar to a target bridge is matched according to bridge type data, bridge index data of the comparison bridge are analyzed, and a priority monitoring sequence of the target bridge is obtained through analysis; obtaining the maximum allowable load of the target bridge, and detecting the structural health condition of the target bridge according to the first priority monitoring target; analyzing the use condition of the target bridge according to the real-time monitoring data and the second priority monitoring target; the damage condition of the corresponding bridge floor of the target bridge is analyzed according to the bridge index data, whether the target bridge has the passing condition or not is judged, mutual hooking of the bridge structure and the use state is achieved, and then comprehensive detection of the bridge structure is achieved.

The invention discloses a vehicle transport capacity scheduling system based on a network order, which comprises a data acquisition module, a data processing unit, a core scheduling unit, a data processing unit, a core scheduling unit, a data processing module, a data storage module, a data processing module, a data processing module and a data processing module, and is characterized in that the data acquisition module is used for acquiring network order data, vehicle real-time state data, real-time traffic data, weather data and driver biological characteristic data; the data processing unit is connected with the data acquisition module and used for cleaning, fusing and analyzing acquired data, the core scheduling unit is connected with the data processing unit, and the core scheduling unit comprises a dynamic adaptive scheduling module, an order allocation module, a path planning module, a dynamic adjustment module and a driver state adaptation module; by continuously receiving the environment state and optimizing the scheduling strategy based on the reward mechanism, the system can adapt to complex and changeable orders and traffic conditions, dynamic optimization of vehicle transport capacity scheduling is achieved, and the overall performance of the system is improved.

The invention discloses a long-distance maneuvering path planning method and system fused with DOP information, and belongs to the technical field of path planning and navigation. The method comprises the following steps: collecting special vehicle information, GNSS DOP information, geographic information and traffic information; based on the DOP value and the traffic condition, the roads are preliminarily screened, and the gradient, the slope direction, the height limit, the load bearing, the turning radius and DOP information are integrated; road condition information is converted into constraint conditions, and DOP information serves as an optimization target and is fused into a heuristic cost function of an improved algorithm; a path with the best navigation effect is obtained through path searching, visual display is carried out on a geographic information system map, and DOP information changes of all key points on the path are displayed in real time. According to the method, a GNSS signal blind area can be effectively avoided, the feasibility and navigation precision of path planning are improved in combination with vehicle dynamics constraints, and the method is suitable for long-distance maneuvering tasks of special vehicles.

The hierarchical optimization control method in the vehicle following scene of the fuel cell hybrid electric vehicle comprises the following steps: acquiring vehicle driving data in real time, and inputting the vehicle driving data into a pre-trained working condition recognition model for recognition to obtain a real-time traffic working condition; inputting the real-time traffic condition into a pre-constructed model prediction vehicle-following controller for prediction, and performing optimization guidance on the prediction process of the model prediction vehicle-following controller based on a particle swarm algorithm to obtain an optimal vehicle control parameter; demand power of the fuel cell hybrid electric vehicle is obtained based on the optimal vehicle control parameters, and weighting Soc is calculated according to the demand power; inputting the required power and the weighted Soc as input variables into a fuzzy logic system for fuzzy processing to obtain an adjustment frequency; and inputting the required power and the adjusting frequency into a self-adaptive low-pass filter for frequency division processing. According to the method, the required power of the fuel cell hybrid electric vehicle can be reasonably distributed in the vehicle following scene.

The invention discloses a highway condition intelligent monitoring and early warning system based on multi-source sensor fusion, and belongs to the technical field of highway monitoring and early warning, and the system comprises a multi-source data obtaining module which is used for obtaining parameter information obtained by a plurality of sensor ends; the processing fusion module is used for carrying out preprocessing operation on the obtained parameter information and constructing a road condition state model; the analysis module is used for judging whether the road condition of the expressway is abnormal or not based on the constructed road condition state model so as to generate a corresponding early warning instruction; and the early warning decision module performs corresponding alarm response based on the early warning instruction. According to the invention, various parameter information of the highway can be fused to generate the vehicle condition state coefficient and the environment information coefficient, and whether the corresponding section of the highway is abnormal or not can be more accurately judged by combining the vehicle condition and the environment, so that early warning response can be performed in advance, and the occurrence of highway traffic accidents and traffic congestion can be reduced.

The invention relates to the technical field of intelligent traffic and smart city waste management, and discloses a path planning method and system for a garbage transport vehicle based on multi-source data, and the method comprises the steps: generating a reference path based on historical collection and transportation data, the method comprises the following steps: performing data cleaning on pre-acquired garbage quantity data, vehicle state data, real-time traffic data, hydroelectric data and fuel consumption data, performing multi-source time sequence feature fusion anomaly detection on a cleaned data set, performing trajectory inversion on the cleaned data set, performing target constraint through a mixed integer programming model, and performing multi-source time sequence feature fusion anomaly detection on the cleaned data set. Then gradient descent parameter optimization is carried out on the initial optimization model, elastic resource scheduling is carried out on an optimized path scheme based on a large event plan library, a path instruction set is generated, dynamic re-optimization evaluation is carried out on the path instruction set based on garbage amount change and traffic condition data fed back in real time, and an updated path is generated; according to the invention, the path planning efficiency of the garbage transport vehicle based on the multi-source data can be improved.

The invention relates to the technical field of computers, in particular to a municipal environmental sanitation cleaning task scheduling method and system based on big data analysis, and the method comprises the steps: obtaining the historical garbage amount data of an urban traffic station, and carrying out the garbage amount partitioning through a K-means + + clustering algorithm according to the historical garbage amount data; acquiring urban meteorological data, and predicting an urban weather condition through a wavelet transform and LSTM neural network hybrid model according to the urban meteorological data; the state of the sanitation vehicle is monitored in real time through a vehicle-mounted sensor, and the position and the driving track of the sanitation vehicle are tracked in real time through the GPS positioning technology; according to the garbage quantity partition, the predicted urban weather condition, the environmental sanitation vehicle state and the position and the driving track of the environmental sanitation vehicle, a cleaning task is distributed to the environmental sanitation vehicle; and real-time traffic conditions are obtained, and the sanitation vehicles are scheduled to go to the garbage quantity subareas to execute cleaning tasks in combination with the real-time traffic conditions. The environmental sanitation management efficiency can be improved.

The invention provides a traffic simulation analysis method for an expressway construction area based on VISSIM. The method comprises the following steps: S1, collecting point cloud data, vehicle data and traffic video data of the landform of the construction area in real time; s2, preprocessing the collected multi-source data; s3, constructing a dynamic simulation model of the construction area; s4, simulating the traffic condition of the construction area; s5, dynamically planning an optimal driving path of the vehicle according to the simulated traffic condition and construction information; and analyzing the vehicle overlapping probability of the intersection in real time, and performing early warning according to an analysis result. Through the technical means of multi-source data fusion, high-precision dynamic modeling, real-time analogue simulation, intelligent path planning and the like, accurate simulation and optimal management of the traffic influence during the blasting demolition construction of the expressway overline overbridge group are realized, and remarkable technical advantages and application values are achieved; and a scientific and efficient solution is provided for urban traffic management.

The application discloses a highway traffic monitoring system based on unmanned aerial vehicle aerial photography, which comprises a camera arranged on a holder, the holder being arranged on an unmanned aerial vehicle, the unmanned aerial vehicle flying at a low speed above a designated highway lane, and the unmanned aerial vehicle driving towards vehicles on the highway lane; the camera is used for photographing a road surface image of the designated highway lane based on the unmanned aerial vehicle and sending the road surface image to an image recognition unit; the image recognition unit recognizes the vehicles in the road surface image; and a road surface monitoring unit determines whether traffic congestion occurs on the designated highway lane based on the positions of the vehicles in adjacent frames.

Disclosed in the present invention are a routing model-based network slice recommendation method and system, a device, and a medium. The method comprises: collecting statistics about real-time traffic to analyze network load and demand, obtaining traffic distribution characteristics of the whole network, and pre-constructing basic network slices on the basis of the traffic distribution characteristics; deploying the constructed basic network slices on a switching node in the form of OpenFlow protocol flow tables; on the basis of bandwidth and time delay classification thresholds, classifying the network slices deployed on the switching node; virtualizing physical resources by means of NFV to obtain associations between various network slices and the physical resources; an SDN controller generating a routing policy on the basis of network load and traffic conditions; deploying the routing policy and obtaining an optimal link from the existing routing policy; selecting, from the various network slices, network slices to be allocated to a user, sorting the network slices to be allocated for recommendation, and sequentially recommending same to the user by means of the optimal link. In the present invention, requirements of users of the same type can be met by means of recommendation priority-based sorting.