1319 results about "Traffic management" patented technology

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.

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 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 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 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 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 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.

The invention discloses a traffic abnormity processing method and system based on multi-source data space-time fusion, and relates to the technical field of traffic supervision, and the method comprises the steps: collecting traffic multi-modal data in parallel to construct a space-time correlation matrix, and analyzing the space-time correlation matrix according to a space-time correlation analysis model to determine an initial abnormal section; tracking abnormal points of the initial abnormal section according to a grid contraction strategy to determine a target abnormal section; determining an alarm screening strategy according to the abnormal sweep degree of the target abnormal section; matching an exception handling scheme according to the alarm screening strategy in combination with the alarm level of the target exception section; and monitoring the completion degree of the traffic dredging indexes in real time to determine the traffic dredging condition in combination with the abnormality processing scheme, and updating the traffic road condition broadcast in real time. According to the scheme, the problem of insufficient multi-source data fusion is solved, the whole-process intelligent management of traffic abnormity is realized, the abnormity positioning precision and the alarm processing efficiency are improved, and a real-time and accurate solution is provided for traffic management.

The invention discloses a vehicle monitoring method based on frame difference and deep learning fusion, and relates to the technical field of vehicle monitoring, cameras and environment sensors are deployed in a monitoring area, and videos and multi-source data are acquired by means of vehicle-road cooperation; a self-adaptive frame difference method is used, morphology and optical flow estimation are matched, a threshold value is determined according to the environment, and vehicle features are extracted; constructing a deep convolutional neural network with an attention mechanism, and training a model by using various data in combination with migration and reinforcement learning; fusing the two types of features based on a graph attention network to form high-quality fusion features; a space-time diagram convolutional network is combined with an LSTM to track a vehicle and predict a trajectory, a behavior pattern library is constructed to judge abnormity, and classification analysis is performed in combination with an SVM and a knowledge graph. According to the invention, the frame difference and deep learning are fused, the monitoring accuracy is improved, and the vehicle can be accurately identified and detected; the real-time performance is enhanced, the data is quickly processed, and the environmental influence is reduced; traffic management is assisted, and a safe and efficient traffic environment is created.

The invention discloses a county and rural traffic planning decision-making method and system based on a road traffic index. The method comprises the following steps: acquiring navigation API data, social economic data and POI information related to county and rural traffic planning; the method comprises the following steps: acquiring data, processing the data, then calculating a route growth index, a per-hour time index, a holiday influence index, a broken road index and a planning simulation index, carrying out weighted fusion to obtain a road traffic index, when the road traffic index is calculated, distributing an initial weight for each index, and adopting a self-adaptive weight adjustment algorithm to calculate the road traffic index. Dynamically adjusting the weight of each index according to the real-time traffic data and the simulation result; county and rural traffic planning is carried out by taking reduction of the road traffic index as a target, and a road traffic index difference value before and after planning is calculated as a decision basis. According to the invention, the problem of asymmetry of basic traffic management information is effectively solved, and the connection accuracy of the road network is improved.

The invention discloses a road shoulder vehicle exclusion method based on an inspection system, and relates to the technical field of intelligent transportation, and the method comprises the steps: obtaining a parking area image shot by an inspection vehicle, recognizing a vehicle target in the image, and extracting the license plate position information; based on vehicle three-dimensional feature reconstruction and space projection analysis, generating a space distribution thermodynamic diagram of the vehicle in the image coordinate system; constructing a dynamic segmentation line model, and adaptively generating a road shoulder region judgment boundary according to the parking space type and the road geometric parameters; and through a multi-target trajectory association algorithm, the berth vehicle and the road shoulder vehicle are distinguished, and the berth occupation state is output. According to the scheme provided by the invention, the spatial positioning precision and behavior judgment reliability in a complex scene can be remarkably improved, the limitations on environmental adaptability, vehicle type compatibility and time sequence continuity are broken through, and a road shoulder violation management and control solution with high robustness and full-time coverage is provided for urban traffic management.

The invention belongs to the technical field of traffic management and control, and particularly relates to a congestion feed-forward intervention method based on traffic flow phase change critical point recognition, which comprises the following steps: collecting and preprocessing multi-source heterogeneous traffic data; carrying out multi-scale traffic flow feature engineering; identifying a traffic flow phase change critical point based on a space-time dynamic graph neural network and critical moderation effect analysis; generating a multi-objective optimization congestion feedforward intervention strategy; and performing intervention, evaluating the effect and performing adaptive learning. According to the technical scheme, accurate prevention and early intervention can be performed before congestion occurs, and the operation efficiency and reliability of an urban traffic system are remarkably improved.

The invention belongs to the technical field of air traffic management, and discloses a control tower and flight guarantee command room decision instruction transmission method based on edge nodes, priorities are distributed for instructions through an intelligent grading model, the high-priority instructions can reserve resources in advance and are transmitted preferentially, the resources can be preempted in emergency, and the control tower and flight guarantee command room decision instruction transmission efficiency is improved. A multi-path transmission strategy is automatically switched when a certain path fails, data integrity is ensured in combination with fragment verification and block chain recording, instruction loss caused by a single point of failure is avoided through edge-cloud redundant backup, and the problems that instruction transmission is prone to interference and insufficient in safety in a traditional method are solved; through intelligent priority grading, resources are inclined to high-priority instructions, resource waste is avoided, a collaborative scheduling mechanism of an edge node cluster can dynamically adjust resource allocation according to a real-time load, and a transmission quality feedback and adaptive optimization strategy is continuously optimized, so that the system can still efficiently process a large number of instructions in a flight take-off and landing intensive period.

The invention discloses a dynamic airspace gridding management method and system for low-altitude economy, and belongs to the technical field of unmanned aerial vehicle traffic management, and the method comprises the steps: collecting airspace state data in real time through a multi-source sensing device, and constructing a four-dimensional space-time grid model; generating a four-dimensional space-time grid with a block chain hash code by fusing meteorological data, an airspace control rule and a real-time flight demand; receiving a space-time grid use request submitted by the aircraft through the smart contract, and calculating an optimal grid allocation scheme based on a deep reinforcement learning model; and the edge computing node executes local track prediction, issues a navigation instruction to the aircraft through the distributed account book, monitors a grid occupation state in real time, and triggers a dynamic grid recombination mechanism when sudden conflicts are detected. According to the method, the rigid constraint of static airspace division can be broken through, the cooperative conflict of multiple aircrafts is eliminated, and the marketization configuration of airspace resources is realized.

The invention discloses a middleware management method and device, relates to the technical field of middleware, and mainly aims to realize unified management and configuration of middleware, reduce the management and maintenance difficulty of the middleware and improve the flexibility and convenience of flow management. According to the main technical scheme, each middleware instance is divided into one or more logic clusters according to operation characteristics of each service, wherein the operation characteristics are used for representing characteristics related to high-reliability operation of the services; general configuration setting is carried out on the one or more logic clusters to obtain global configuration information containing all the logic clusters, and the general configuration setting at least comprises setting of physical mapping and routing strategies of middleware instances docked by all the services in the running process; and respectively issuing the global configuration information to each middleware instance contained in each logic cluster so as to run according to the global configuration information when a service butted with the middleware instance contained in each logic cluster is started. The middleware management method and device are used for middleware management.

The invention discloses an automatic driving safety operation system integrating environmental perception and decision reasoning. The system generates and dynamically updates a security risk map covering an operation area by fusing real-time environment perception, historical operation data and traffic management information. On the basis, the collaborative safety decision-making module further carries out behavior modeling and intention prediction on other traffic participants, and in combination with map risks and traffic instructions, a driving strategy is actively adjusted under a dynamic game framework. According to the invention, the safety control is improved from passive response to an active mode of behavior pre-judgment and game dominance, and the safety and reliability of the operating vehicle in a complex environment and the cooperative capability of the operating vehicle and traffic management are obviously enhanced.

The invention discloses a road section illegal parking space-time prediction system based on deep learning, and relates to the technical field of intelligent traffic management, and the system comprises a multi-source data fusion collection module, a space-time self-attention feature extraction module, a dynamic granularity parameter generation module, a space-time hybrid model prediction module and an illegal parking early warning visualization module. By collecting traffic camera video streams, vehicle-mounted sensor data, mobile phone signaling data and meteorological data in real time, comprehensive perception of the traffic environment is achieved, the multi-source heterogeneous data are mapped to a unified GIS grid after being subjected to space-time coordinate calibration, a multi-dimensional illegal parking influence factor database is constructed, and on the basis, the real-time monitoring of the illegal parking influence factor database is achieved. The space-time self-attention feature extraction module performs efficient space-time feature extraction on multi-source data by using a space-time diagram self-attention model, generates feature vectors with space-time relevance, and solves the problems of single data and insufficient feature extraction in a traditional method.

The invention discloses a traffic prediction method and device based on multi-modal feature fusion, and a medium, and relates to the technical field of traffic prediction. The method comprises the following steps: performing unmanned aerial vehicle video acquisition and preprocessing on an interleaving area to obtain a target area video; traffic flow parameters are extracted from the target area video based on a multi-target detection and tracking algorithm, wherein the traffic flow parameters comprise a space average speed, a space occupancy rate and a vehicle interleaving conflict index; a deep learning model is adopted to process the image sequence of the target area video, potential semantic features are extracted, and the deep learning model comprises a variational auto-encoder; carrying out weighting processing on the potential semantic features by adopting a double attention mechanism; and splicing the weighted potential semantic features and the traffic flow parameters into a multi-modal feature vector, and carrying out traffic state prediction. According to the method, the accuracy and robustness of traffic state prediction of the interlaced area are improved, and more accurate data support and decision basis are provided for dynamic traffic management of complex road sections.

The invention provides a traffic control decision-making method, device and equipment based on data analysis, and aims to solve the technical problems of high subjectivity, insufficient data value mining, disjunction of strategy and practical application and lack of continuous learning optimization capability in the traditional traffic control decision-making. Through standardized fusion processing of multi-source traffic data and reverse optimization configuration of a traffic feature library, in combination with a progressive effect evaluation and reverse deduction verification mechanism, a multi-time scale effect tracking and strategy evolution trajectory acquisition system is innovatively established, and a decision cycle mechanism with autonomous learning and dynamic adjustment capabilities is constructed. The association relationship between the traffic state evolution rule and the control strategy is systematically analyzed, and finally an intelligent traffic control decision framework based on operation state data driving is formed; scientific and reliable decision support and technical basis are provided for application scenes such as urban traffic fine management, intelligent traffic system optimization control, traffic jam treatment and traffic safety guarantee.

The invention provides a cooperative control method and system based on a port automatic driving mixed driving scene, and relates to the technical field of port traffic management, and the method comprises the steps: firstly obtaining a dynamic interaction data set containing information of multiple aspects such as an automatic driving vehicle and manual driving equipment in a port operation region; and then carrying out association feature extraction on the dynamic interaction data set, generating dynamic association representation data containing traffic participant association relationships and other features, executing collaborative decision analysis based on the dynamic association representation data, and determining a right-of-way priority sequence and a collaborative path planning scheme. And a cooperative control instruction set including speed cooperative parameters and the like is generated according to a cooperative decision analysis result, and finally the cooperative control instruction set is distributed to a corresponding control system, so that dynamic cooperative driving control in a port mixed driving scene is realized, and the port operation efficiency and safety are improved.

The invention relates to the technical field of traffic event identification, and discloses an abnormal traffic event identification method and system based on a traffic large model, and the method comprises the steps: obtaining a traffic data flow, extracting an abnormal feature vector, and obtaining an abnormal signal candidate set; grouping the candidate sets and calculating a deviation degree, and if the deviation degree exceeds a threshold value, taking the deviation degree as a risk signal to form an input subset; environment variables are extracted from the subsets, a mapping relation is established, and anomaly recognition embedding representation is obtained; classifying the embedded representation, judging a congestion precursor and generating an early warning signal to obtain an early warning signal sequence; matching the sequence to obtain an abnormal event chain; if the integrity is higher than a threshold value, analyzing the type to obtain an abnormal event type; extracting a correlation feature vector from the type, pushing the correlation feature vector to a traffic management platform to obtain an instruction, and obtaining an emergency response trigger instruction sequence; and executing the instruction sequence to extract a feedback data stream, inputting the traffic large model to judge the accuracy rate, and if the judgment accuracy rate is met, determining an optimized anomaly recognition framework. The method can solve the problem of insufficient early warning capability.