1591 results about "Traffic management" patented technology

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

The invention relates to the field of artificial intelligence, particularly discloses an intelligent optimal management system for urban traffic congestion based on artificial intelligence, and aims to solve the problems of congestion and low efficiency caused by response delay, local optimization and low data utilization rate of an existing traffic management system. The system comprises a data acquisition and fusion module, a traffic state perception and prediction module, a decision optimization module, an instruction issuing and execution module and a man-machine interaction and visualization module. Through multi-source data fusion, graph neural network prediction and multi-agent reinforcement learning, traffic flow real-time perception, accurate prediction and adaptive control are realized, congestion is effectively relieved, and the overall operation efficiency and toughness of a road network are improved.

The invention relates to a large-scale road network traffic control method based on a deep reinforcement learning large model, and belongs to the technical field of intelligent traffic control. The method comprises the following steps: sensing real-time multi-modal road network information including urban road intersections, highway entrance ramps and emergency lanes, and generating a space-time fusion representation vector representing a current traffic network state by fusing a space diagram construction method and a time sequence embedding method; the space-time fusion representation vector and historical state memory are spliced to serve as input, a backbone network of a pre-training large language model is used for state feature distillation so as to enhance state representation, and a traffic control decision is output through a strategy network with a layered action space; through cross-modal knowledge migration and a progressive course learning strategy, a training process of a deep reinforcement learning algorithm is guided and optimized so as to improve model training efficiency and generalization ability. According to the method, the generalization performance and the accuracy of the control strategy are improved while the real-time response speed is ensured.

Disclosed embodiments generally relate to edge-based multi-Radio Access Technology (RAT) traffic management (TM) solutions to support delay-sensitive traffic over heterogeneous networks. Embodiments include delay-aware TM implementations that split and / or steer network traffic across different RATs for the edge network control plane. Embodiments also include utilization threshold-based implementations to achieve delay-aware multi-path TM at the network's edge. The multi-path TM includes multi-RAT, multi-access, or multi-connectivity traffic routes. Embodiments include strategies to sort users (or devices) for making multi-RAT traffic distribution decisions and to determinate the utilization thresholds. Embodiments also include message exchange mechanisms or learning utilization thresholds and other useful system properties. Other embodiments may be described and / or claimed.

The invention discloses an urban road traffic entrance and exit influence evaluation method based on big data, and relates to the technical field of urban traffic management. Constructing an urban road traffic network topological graph based on the traffic feature vectors, proposing a dynamic weight graph embedding algorithm, and establishing a road network association mapping model; applying a graph neural network algorithm based on an attention mechanism to the road network association mapping model, performing road node influence factor evaluation, and quantitatively analyzing the road entrance and exit influence degree; fusing influence factor evaluation results, and constructing a multi-dimensional traffic influence evaluation model by adopting a cross-domain ensemble learning method; and according to a performance evaluation result of the multi-dimensional traffic influence evaluation model, generating urban road traffic entrance and exit optimization decision suggestions through an intelligent recommendation algorithm, and completing accurate scheduling of traffic network nodes. The intelligent recommendation algorithm is developed based on reinforcement learning, and reliable optimization suggestions are provided for traffic management decisions.

The invention relates to a city-level path guidance method and system based on dynamic clustering in a vehicle-road cloud cooperation scene, and the method comprises the steps: constructing a vehicle-road cloud cooperation architecture, and collecting traffic data; the traffic management center updates the road weight and initializes a penalty matrix, and performs congestion detection at the same time; when a congested road section is detected, vehicles possibly affected by congestion are screened out, and a to-be-planned vehicle set is formed; performing spatial clustering on the to-be-planned vehicle set to obtain a plurality of vehicle clusters, and constructing an independent path planning sub-graph for each vehicle cluster; distributing priorities for the vehicles in each vehicle cluster; planning an alternative path for the vehicle; performing batch dynamic adjustment on the road weight and the penalty value on the path planning subgraph; the traffic management center issues the planned alternative path to the corresponding vehicle through the road side unit, and the vehicle runs according to the new running path to realize path induction; finally, reasonable distribution of traffic flow, improvement of road network traffic efficiency and balanced allocation of time-space resources are realized.

The invention discloses a low-altitude unmanned aerial vehicle trajectory tracking and monitoring method based on a 5G-A communication sensing integrated base station, and relates to the technical field of low-altitude traffic management and communication sensing fusion, and the method comprises the steps: firstly collecting multi-source data such as a communication sensing fusion signal, environment interference and unmanned aerial vehicle attributes, and carrying out the alignment and packaging of a unified timestamp and a coordinate system into a synchronous data frame; then, deep fusion and anti-interference processing are carried out on the data frames, noise is filtered out, and pure fusion data is generated; and furthermore, real-time track calculation and motion trend prediction are carried out on pure data by utilizing multi-base-station cooperative calculation and prediction. Based on this, through a multi-target feature recognition and clustering separation mechanism, independent individual trajectories are accurately stripped from a complex mixed data stream, and compliance verification and anomaly judgment are performed on the trajectories in combination with an airspace rule base. In this way, the problems of signal interference and multi-target aliasing in a complex environment can be effectively solved, and therefore high-precision global tracking of the low-altitude unmanned aerial vehicle and real-time monitoring of abnormal behaviors are achieved.

The invention discloses an unmanned aerial vehicle non-visual flight management method based on an unmanned area three-dimensional model, and belongs to the technical field of unmanned aerial vehicle air traffic management and autonomous flight control, and the method comprises the steps: fusing multi-source heterogeneous data to generate a probability map of a data confidence field; calculating a space-time risk potential field based on the map and the kinematics state of the unmanned aerial vehicle; performing adaptive perception and route optimization planning according to the risk potential field; and a closed-loop feedback signal is generated according to the flight execution deviation so as to regulate and control the data fusion and risk calculation process, and data assimilation is carried out. According to the technical scheme, environment modeling fusing multi-source data and confidence evaluation is adopted, dynamic risk calculation of kinematics and global closed-loop feedback regulation are combined, and the safety, autonomy and environment adaptability of non-visual flight of an unmanned aerial vehicle in a complex unmanned area can be remarkably improved.

The invention provides a low-altitude economic area traffic control optimization method and system, and relates to the technical field of traffic control, and the method comprises the steps: carrying out the spatial division of a low-altitude economic area, generating a multi-layer air corridor topological structure, and mapping the traffic flow data to each layer of air corridor; according to the air corridor capacity constraint of each layer, drawing up a first flight control condition; according to the same-layer air corridor safety distance constraint, a second flight control condition is drawn up; and performing traffic scheduling control in the low-altitude economic area by using the first flight control condition and the second flight control condition. The technical problem that the traffic management and control efficiency of the low-altitude economic area is low due to the fact that the traffic density of the low-altitude economic airspace is increased and airspace resources are difficult to distribute effectively in the prior art is solved, and the traffic management and control efficiency of the low-altitude economic area is improved by comprehensively considering the capacity constraint of the airspace corridors and the safety distance constraint between the airspace corridors on the same layer. And the traffic control efficiency of the low-altitude economic area is improved.

The invention discloses an urban traffic flow pre-judgment system, and the system comprises a multi-source sensing module which is disposed at a traffic node, and collects traffic flow time series data in real time; the multi-source sensing module is connected with the dynamic graph engine, and the dynamic graph engine comprises a time sequence correlation calculation unit which is used for generating a dynamic correlation weight between nodes based on a dynamic time warping algorithm; and a topology fusion unit. According to the urban traffic flow pre-judgment system provided by the embodiment of the invention, the urban traffic flow pre-judgment system effectively captures the time-space dependency relationship of the traffic flow through multi-source data fusion and dynamic graph modeling, and provides real-time data support for congestion early warning and signal control; and the decision support terminal assists a traffic management department to efficiently formulate a scheduling strategy through visualization and interpretability analysis, so that the urban traffic monitoring and regulation efficiency is improved.

The invention relates to the technical field of air traffic management, and particularly provides an airport scene aircraft taxiing conflict real-time prediction and dynamic scheduling method, which comprises the following steps: collecting the motion state of an aircraft and airport road network data, and generating the original trajectory of the aircraft by adopting a hidden Markov model; predicting a future taxiing trajectory of the aircraft to obtain a predicted trajectory; screening potential conflict aircrafts by adopting a dynamic R-Tree spatial index technology, judging whether tracks between the potential conflict aircrafts are intersected or not, and generating a dynamic risk value; executing a dynamic scheduling decision based on the dynamic risk value, and generating an aircraft bypass path by adopting a path generation algorithm; rolling time domain control is adopted, and a local path is re-planned based on data obtained in real time. According to the method, prediction of the taxiing trajectory of the aircraft is fully considered, the risk value of the taxiing conflict of the aircraft is pre-judged in advance, and dynamic path adjustment is performed, so that the labor cost is reduced, and the efficiency and safety of airport ground operation are improved.

The invention, which relates to the technical field of traffic management, discloses an intelligent detection system for a vehicle parking space in a passenger and goods mail fusion station, and the system comprises a multi-mode sensing module which is configured to collect vehicle data information, the collection mode comprises the steps that vehicle data information is collected through an enhanced geomagnetic array, an adjustable video monitoring set, a lateral laser radar and a piezoelectric sensor; and the edge computing node module is configured to integrate an AI chip, the AI chip comprises a vehicle classification model, an abnormal parking detection algorithm and a data fusion engine, and the collected data is computed and analyzed. The edge computing node is integrated with an AI chip, and data are analyzed and processed through a built-in algorithm; the hybrid communication network transmits data through an optical fiber backbone network and a specific redundant link, self-adaptive visual adjustment and intelligent algorithm optimization are performed, the problem of complex vehicle type detection in a passenger and goods mail fusion station is solved, and the parking space utilization rate and the operation efficiency are remarkably improved.

The invention discloses a multi-mode cooperative unmanned aerial vehicle highway illegal parking intelligent identification method and system. The system comprises a multi-mode sensing layer and a dynamic focusing control engine. The multi-mode sensing layer is connected with an unmanned aerial vehicle end, and the multi-mode sensing layer comprises a vehicle detection module, a lane semantic segmentation module and a vehicle tracking module; the vehicle detection module positions a vehicle target from an unmanned aerial vehicle cruise acquisition road video stream obtained from the unmanned aerial vehicle end; the lane semantic segmentation module can perform anti-distortion lane semantic perception; the vehicle tracking module establishes a motion model based on the target vehicle positioned by the vehicle detection module; the dynamic focusing control engine obtains a lane mask graph and a vehicle state according to the lane semantic segmentation module and the vehicle tracking module; and the dynamic focusing control engine is also used for performing illegal judgment and evidence generation and sending the illegal judgment and evidence generation to the traffic management law enforcement platform. According to the invention, precise identification and automatic law enforcement evidence collection of illegal parking targets in the highway scene are realized.

The invention discloses an intelligent traffic flow dynamic regulation and control method and system based on multi-source data fusion, and relates to the technical field of data processing. The method comprises the following steps: executing multi-source data acquisition through a data acquisition interface to obtain a multi-source traffic data set; performing spatial fusion on the multi-source traffic data set to obtain a flow marking map; performing current traffic state analysis based on the flow marking map, identifying local and global congestion, and constructing a traffic situation map; and performing traffic flow state prediction in a preset future time zone based on the traffic situation map, performing regulation and control decision based on a prediction result, and generating a traffic flow regulation and control strategy. The technical problem that traffic flow prediction and regulation are not accurate enough in the prior art is solved, and the technical effects that intelligent traffic flow dynamic regulation is achieved through multi-source data fusion and space-time analysis, and the traffic management precision and the response speed are improved are achieved.

The invention discloses a highway intelligent emergency management method and system, and relates to the technical field of intelligent traffic, and the key points of the technical scheme are that multi-source heterogeneous data are fused to construct a three-dimensional situation awareness library, and accurate positioning and prediction of traffic congestion are realized; constructing a virtual emergency scene on a simulation platform based on a Bayesian network, evaluating traffic management efficiency indexes of different management and control plans, and analyzing accident influence and rescue path feasibility; an optimization objective function is established to solve an optimal resource scheduling scheme, and the optimal resource scheduling scheme is issued to the intelligent interaction device in real time; the Bayesian network and the deep reinforcement learning model are dynamically optimized through equipment feedback data, and scheme self-adaptive adjustment is achieved; according to the scheme, factors such as traffic flow dynamic change, road topology and environment are comprehensively considered, quick response is facilitated when an emergency occurs, rescue time and traffic jam loss are reduced, and the overall level of highway emergency management is improved.

The invention relates to the field of meteorological detection and analysis, and discloses a low-altitude complex wind field detection and inversion method based on a vehicle-mounted laser radar, and the method comprises the steps: determining a scanning point location based on the terrain, building distribution and simulation data; optimizing the driving path of the vehicle-mounted radar to cover the high-risk area; acquiring three-dimensional wind field data by adopting an RHI and PPI multi-mode scanning strategy; the influence of vehicle-mounted movement on speed measurement is corrected; calculating turbulence intensity and vortex nucleus position by using spectral width characteristics; and identifying the wind shear position and strength through a slope algorithm. According to the invention, low-cost and high-precision wind field detection is realized through maneuvering deployment and multi-mode cooperation, and reliable technical support is provided for safe flight of the unmanned aerial vehicle, low-altitude traffic management and meteorological early warning.

The invention belongs to the technical field of air traffic management, and relates to an air flight flow prediction method based on a lightweight adaptive network, which comprises the following steps of: firstly, acquiring data and fusing the data to generate a flow time sequence matrix indexed by airport identification and timestamp; secondly, constructing a self-adaptive dynamic graph; performing mixed graph convolution on the output dynamic graph and node features to form a spatial feature tensor, and performing frequency domain enhancement on the spatial feature tensor to obtain an event enhancement sequence; then processing the event strengthening sequence through an hour-level large convolution kernel and a minute-level expansion small convolution kernel, amplifying a congestion peak based on trend-fluctuation gating after time alignment, and outputting a fusion feature sequence; and finally, carrying out recursive decoding and prediction to obtain the predicted air flight flow. According to the method provided by the invention, under the conditions of dynamically changing airspace topology and strong noise and multi-scale coupled time sequence data, a set of lightweight spatial-temporal model is constructed and updated online in a self-adaptive manner, so that high-precision multi-step prediction of the multi-element flight flow can be realized.

The invention discloses an intelligent traffic control method and system in a low-altitude economic environment, and relates to the technical field of intelligent traffic systems, and the method comprises the steps: collecting low-altitude aircraft and ground traffic spatio-temporal data through a multi-modal sensor network, and generating a multi-source heterogeneous data set; constructing a dynamic traffic situation map through spatio-temporal feature fusion; performing three-dimensional path planning to generate a three-dimensional guiding strategy; detecting conflicts and correcting strategies according to a preset rule base, and outputting an instruction set to distribute real-time traffic flow. The technical problem that in the low-altitude economic environment, a traditional traffic management and control method is difficult to meet the cross-domain cooperation requirement of the low-altitude aircraft and the ground traffic is solved, and the technical effects of three-dimensional cooperative management and control of the low-altitude aircraft and the ground traffic and further guaranteeing safe and efficient operation of the traffic in the low-altitude economic environment are achieved.

The invention discloses a traffic flow prediction method and system based on multi-scale dynamic decomposition and a space-time Transform. According to the method, firstly, an original traffic flow sequence is decomposed into trend components and seasonal components; then, modeling the trend components by adopting a multi-layer perceptron to capture global changes; and meanwhile, a space-time Transform is used for modeling seasonal components, and the architecture effectively extracts dynamic space-time dependence characteristics by integrating space-time adaptive embedding and an adaptive Switch GLU gating mechanism. And finally, fusing trend and seasonal feature representation to generate a prediction result. According to the method, noise is effectively separated through decomposition, linear enhancement space-time self-adaptive embedding, a self-adaptive Switch GLU gating mechanism and a unified space-time self-attention Transform architecture are integrated, the modeling capacity for complex space-time dependence is enhanced, prediction precision and robustness are remarkably improved, and the method can be widely applied to the field of intelligent traffic management and control.

The embodiment of the invention discloses a flight path data analysis method based on a depth auto-encoder and a generative adversarial network, relates to the technical field of air traffic management, and adopts a cubic spline interpolation method to enable each flight path to have the same length; a deep auto-encoder (DAE) and a generative adversarial network (GAN) are used as a basic framework to establish a flight trajectory data analysis framework, and two tasks of trajectory anomaly detection and flow pattern recognition are synchronously executed; in the pre-training stage, on the basis of DAE, a discriminator is integrated to establish an abnormal trajectory detection task, and an abnormal score function is formulated in combination with reconstruction loss and discriminator loss; in the fine tuning stage, a Gaussian mixture model (GMM) is adopted for low-dimensional representation of the trajectory in a potential space output by an encoder to establish a flow pattern recognition task, and in order to enhance clustering, a loss function is constructed in combination with clustering distribution enhancement. The method is suitable for civil aviation air traffic management.

The invention aims to provide a large-range urban road network intelligent management and control method based on mobile phone signaling data, and belongs to the technical field of traffic management and control. Real-time traffic flow parameters are obtained by preprocessing the mobile phone signaling data, a high-fidelity microscopic traffic simulation environment is constructed, and on the basis, the real-time traffic flow parameters are obtained; the method comprises the following steps: establishing a multi-dimensional evaluation index system containing operation safety and efficiency, constructing a macro-micro collaborative double-layer planning model, adopting a deep reinforcement learning algorithm, taking continuous-discrete mixed decision variables such as intersection signal timing and a variable lane strategy as optimization objects, carrying out strategy learning and iterative optimization through an Actor-Critic architecture, and carrying out optimization on the optimization objects. And outputting the optimal control strategy combination. According to the invention, dynamic and accurate cooperative management and control of the large-range urban road network are realized, and the traffic efficiency is remarkably improved while the operation safety is guaranteed.

The invention relates to the field of traffic management, and discloses a traffic situation prediction method based on multi-source heterogeneous data fusion, which comprises the following steps of: firstly, acquiring traffic situation related data of a target area from a plurality of data sources, including traffic flow data, vehicle speed data, video image data, meteorological data and historical traffic statistical data; secondly, preprocessing the acquired traffic situation related data, including data cleaning, normalization or standardization processing, and performing time-space synchronization and matching; wherein the data cleaning comprises noise removal, abnormal value processing and missing value filling; and finally, inputting the preprocessed data into a pre-trained traffic situation prediction model, and outputting the predicted traffic jam degree of the target area. According to the invention, comprehensive analysis is carried out through the traffic-related situation data and the emergency data, and finally, the purpose of improving the prediction comprehensiveness through a multi-source cooperation mechanism is achieved.

The invention provides a vehicle-road cloud collaborative mixed traffic flow optimization method, system and device, and relates to the technical field of intelligent traffic, and the method comprises the steps: extracting a mixed traffic flow feature set containing a road environment, a vehicle state and a driver behavior through obtaining and synchronously fusing the multi-source traffic data of a cloud end, a road end and a vehicle end; according to the method, the intention of a driver is predicted by using a double-layer LSTM model, a vehicle trajectory is predicted in combination with an RNN-LSTM model, confidence fusion analysis of the intention and the trajectory is performed on this basis, accurate prediction of traffic conflict events is realized, and global optimization of traffic flow is supported. According to the method, the intention recognition and trajectory prediction precision in a complex mixed traffic environment is effectively improved, the intelligent decision-making capability of a traffic management system is enhanced, and an efficient technical means is provided for relieving traffic congestion, improving the traffic efficiency and guaranteeing traffic safety.

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 discloses an elastic resource allocation and barrier gate linkage control system and method for mixed traffic of parking lots, a medium and equipment, and the system comprises a vehicle type recognition module, a dynamic resource allocation module, a cooperative control decision module, an emergency channel management module and a multi-mode communication module. According to the parking lot mixed traffic management system integrating vehicle-road cooperative communication, double-layer game optimization and elastic resource pool division, an independent resource pool and a dynamic quota adjustment mechanism of automatic driving vehicles and manual driving vehicles are constructed, and a charging pile-barrier gate cooperative optimization unit and a multi-stage emergency response protocol are combined; the global optimization of parking lot resource allocation and traffic control is realized, and the problems of prominent resource competition contradiction, separation of barrier gate control and resource allocation, insufficient intelligence of an emergency response mechanism and short delay of multi-mode communication and decision cooperation in a mixed traffic scene are solved.

The invention relates to the technical field of low-altitude traffic management, and discloses a low-altitude traffic situation analysis method and system based on digital twinning, and the method comprises the steps: constructing a non-uniform adaptive quadtree spatial index structure; generating a space-time risk probability field, and calculating a conflict risk probability by adopting trajectory prediction of multi-model fusion; constructing a scene adaptive classification model, and dynamically adjusting a decision strategy according to traffic scene characteristics; collaborative decision optimization is realized, and a hierarchical decision structure and a passing right distribution algorithm based on an auction mechanism are adopted; establishing a digital twinborn situation analysis platform, mapping a low-altitude traffic state in real time and providing decision support; according to the method, through organic combination of technologies such as non-uniform adaptive quadtree spatial indexing, multi-model fusion trajectory prediction, space-time risk probability field calculation, hierarchical collaborative decision optimization and a digital twinborn situation analysis platform, accurate perception, analysis and prediction of low-altitude traffic are realized.

The invention discloses a road traffic accident cause analysis and responsibility judgment method and system, and relates to the technical field of traffic management data processing, and the method comprises the steps: carrying out the credible collection of multi-source data, and constructing an evidence chain; performing multi-source data preprocessing and cross-modal fusion optimization; performing multi-dimensional cause intelligent analysis; performing responsibility judgment based on a quantification rule; and the responsibility judgment result is subjected to multi-dimensional rechecking and rule iteration adaptation. According to the road traffic accident cause analysis and responsibility judgment method and system, through a full-process closed-loop design of data acquisition, preprocessing, cause analysis, responsibility judgment, re-checking iteration and report evidence storage, technologies such as multi-source perception and an AI algorithm are integrated; the method solves the problems of uncredible evidence chain, one-sided cause traceability, non-uniform judgment standard, low cooperation efficiency and the like in traditional accident processing, realizes intelligence, standardization, compliance and traceability of accident processing, remarkably improves the credibility, processing efficiency and judicial suitability of a judgment result, and provides core technical support for modernization of traffic control.

The invention relates to an AI identification road traffic safety illegal behavior image evidence obtaining method and device based on an unmanned aircraft, equipment and a medium, and the method comprises the steps: calling a no-parking region identification model, so as to determine a no-parking region mask in a to-be-inspected region image, calling a potential violation vehicle detection model to identify potential violation vehicles in the no-parking area mask so as to determine bounding boxes of the potential violation vehicles and vehicle types corresponding to the bounding boxes; and calling a target tracking algorithm to perform target tracking on the potential violation vehicle, and determining the potential violation vehicle as a target violation vehicle when detecting that the intersection-to-union ratio between the bounding box and the no-parking area mask in the plurality of continuous image frames exceeds a preset threshold value and the potential violation vehicle is in a static state. And sending the violation evidence packet corresponding to the target violation vehicle to a traffic management system. According to the invention, the efficiency of the monitoring system is improved, and accurate and verifiable violation evidences are provided for the traffic management system.

The invention belongs to the technical field of air traffic management, and relates to a flight time adjustment management method and system in a special scene. According to the method, the flight release priority sequence is constructed, the airport taxiway network topology and the dynamic limitation information are combined, the taxiing path is planned, the space-time conflict is detected, and the dynamic adjustment of flight ground operation and the coordination verification of the runway access time window are realized. The problems of frequent flight ground taxiing path conflicts, low scheduling efficiency and difficulty in accurate take-off time control in special scenes are solved, the safety and efficiency of airport ground operation are effectively improved, the flight taxiing time and the runway waiting time are reduced, the flight release sorting and the runway use plan are optimized, and the flight taxiing efficiency is improved. The flight scheduling cooperation capability in a complex operation environment is enhanced, and the orderliness and stability of flight operation are guaranteed.

The invention, which relates to the technical field of intelligent traffic and carbon emission monitoring, discloses a highway carbon emission energy consumption abnormity monitoring optimization system comprising a vehicle characteristic acquisition module, an environment dynamic sensing module, a carbon emission dynamic calculation module, an abnormity intelligent diagnosis module and an attribution optimization execution module. Calculating a real-time dynamic carbon emission value by combining environmental factor data such as vehicle characteristics, real-time driving speed and wind speed, road gradient and the like; setting a dynamic carbon emission reference value according to the traffic flow and environmental factor data of the current road section; and identifying an abnormal attribution type by associating data such as vehicle speed abrupt change, environmental factor abrupt change and road section traffic condition in the abnormal carbon emission time period, and generating a corresponding optimization adjustment instruction. The method improves the attribution accuracy of the carbon emission abnormity, supports the real-time optimization decision oriented to traffic management, and provides technical support for the operation of a green intelligent expressway.