1525 results about "Traffic conditions" patented technology

The invention provides a procurement supply chain collaborative intelligent management method and a procurement supply chain collaborative intelligent management system. Belongs to the technical field of supply chain management. Constructing a purchase demand prediction model; dynamically adjusting the purchasing strategy according to the prediction result; the record and the evaluation result are stored on the block chain; the system automatically sends order information to a supplier; the supplier confirms the order through the block chain platform and updates the production progress and the logistics information; the system adjusts an inventory strategy in real time; the logistics state is monitored in real time; the system analyzes data of each link of the supply chain in real time and identifies potential risks. Through dynamic path planning and real-time transportation optimization, the logistics path can be adjusted in real time according to traffic conditions, environmental factors and emergencies, the transportation time is shortened, the transportation cost is reduced, and therefore the overall efficiency of a supply chain is remarkably improved.

The invention discloses a driving situation prediction and adaptive strategy generation system based on a cloud multi-mode large model. Comprising a multi-modal data acquisition and situation prediction module, a self-adaptive strategy generation and remote optimization module, a collaborative driving and multi-vehicle strategy coordination module, a cloud driving intelligent monitoring and feedback module and a dynamic risk assessment and path optimization module. Various input data from the vehicle and the environment are processed through the cloud, risk factors such as sudden obstacles, weather deterioration and complex traffic conditions in the automatic driving situation are predicted in advance, cloud computing provides powerful data processing capacity and storage resources, a large model deployed at the cloud can train data collected by the vehicle, and the risk factors can be predicted in advance. Therefore, the perception and prediction capabilities of the automatic driving system are improved, various coping strategies aiming at the risks are generated, and the vehicle is helped to make a safer and more intelligent driving decision in a complex scene through dynamic generation and remote optimization of an adaptive strategy.

The invention discloses a perception intelligent driving complex traffic scene dynamic risk prediction method, and relates to the technical field of risk prediction, and the method comprises the steps: obtaining the multi-source traffic dynamic data of a target region in real time; performing space-time alignment processing on the multi-source traffic dynamic data; inputting the space-time coupling feature matrix into a pre-trained depth space-time prediction model to generate a risk thermodynamic map; calculating a dynamic risk index of each traffic sub-region, and generating a risk level distribution sequence; and triggering a self-adaptive early warning response mechanism according to the risk level distribution sequence, and dynamically adjusting operation parameters of the variable information sign and the traffic signal controller. The technical problems of frequent traffic congestion and high accident risk caused by inaccurate traffic risk prediction and difficulty in dynamic adjustment according to the real-time traffic condition in the prior art are solved, and the technical effects of accurately predicting the traffic risk and improving the safety and traffic efficiency in a complex traffic scene are achieved.

The invention discloses a control strategy model construction method based on big data, and relates to the technical field of intelligent control, a traffic situation deduction and emergency strategy generation module uses a multi-agent traffic simulation technology, simulates a traffic situation in combination with real-time traffic data, evaluates risks and early warns potential crisis by integrating multiple factors, and provides a control strategy model for the traffic situation deduction and emergency strategy generation module. And after an early warning is received, an emergency strategy is generated by using an intelligent algorithm, the strategy is evaluated and optimized by using virtual rehearsal, a result is fed back to a management department, and a synergistic effect with other modules is achieved. Traditional traffic detection equipment data is integrated with multi-source heterogeneous data such as mobile phone signaling, shared bicycle and online hailed bicycle GPS, intelligent vehicle-mounted equipment and public traffic operation, more comprehensive and accurate information such as traffic flow, flow direction, travel behavior track and the like is provided, and rich and accurate basis is provided for traffic management decisions.

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 provides a multi-modal remote sensing and knowledge graph-based urban planning decision-making method and system, and the method comprises the steps: integrating multi-source heterogeneous data, achieving the feature alignment and fusion of an optical image and SAR data in a satellite remote sensing image through a deep learning technology, and generating an urban ground feature feature vector; associating the urban ground feature feature vector with an urban planning policy database, outputting a structured early warning report of an illegal construction early warning event set and a policy compliance label, and forming a dynamic policy constraint condition for subsequent multi-objective optimization; processing historical traffic flow data based on the dynamic graph model, and outputting a time-space distribution prediction result of future traffic conditions; and generating a Pareto optimal city planning scheme by combining multi-objective optimization with a spatial-temporal distribution prediction result of a future traffic condition. According to the method, high-precision urban surface feature classification, real-time violation extension early warning and traffic flow accurate prediction are realized through multi-modal remote sensing data fusion and a space-time knowledge graph technology, and multi-target optimization and digital twinborn verification are combined, so that the planning efficiency is improved, and extension applications such as carbon neutralization are supported.

According to an embodiment, it is a system comprising, a sensor, a communication module, and a processor, wherein the processor storing instructions in a non-transitory memory that, when executed, cause the processor to scan, via the sensor of a host vehicle at a first frequency to observe surroundings for an erratic behavior, determine an erratic vehicle by analyzing for the erratic behavior, scan via the sensor at a second frequency to determine an identity of the erratic vehicle and analyze a traffic condition around the erratic vehicle, determine a possibility of a collision, alert via the communication module a nearby vehicle by sending a message, and determine an evasive action to avoid the collision.

A method to optimize human resource management within emergency medical services (EMS) according to one approach may have the steps of: receiving inputs from at least one or more the data sources such as traffic conditions, weather, incident location of emergency or non emergency call, call type, dispatch type, latitude and longitude of incident location, age, sex, chief complaint, incident date and time, holiday, day of the week, call classification, emergency department population status, incoming EMS service requests, and the like and combinations thereof; providing a scheduling module for building one or more predictive assessment values; the scheduling module outputting automatically a suggested scheduling template; employing one or more machine learning models to generate the one or more predictive assessment values that relate to the comparison of the inputs of the evolution of data over a time interval using machine learning; employing a global positioning systems (GPS) device to provide geo-location information of EMS equipment.

The invention relates to an urban road carbon emission prediction and optimization control method and system, and belongs to the technical field of road traffic carbon emission monitoring, and the method comprises the steps: obtaining real-time monitoring data of a to-be-detected road, and carrying out the preprocessing; performing space-time alignment on the preprocessed real-time monitoring data, extracting dynamic traffic features and static road features, and generating a space-time feature matrix; inputting the spatial-temporal characteristic matrix into a pre-trained carbon emission prediction model to obtain a prediction result matrix of the to-be-detected road; on the basis of the prediction result matrix, identifying a road grid of which the carbon emission intensity prediction value exceeds a preset threshold value as a high emission area; acquiring real-time traffic state data of the high emission area; and based on the real-time traffic state data and the prediction result matrix of the high emission area, constructing a multi-objective optimization function and carrying out solving to obtain an optimization control strategy of the high emission area. According to the invention, traffic carbon emission optimization control and real-time traffic condition synchronization can be realized.

The invention discloses a vehicle and road cloud integrated traffic control method for automatic driving lane changing, and the method comprises the following steps: S1, carrying out the multi-mode fusion of preprocessed multi-source sensor data, and generating environment state parameters; s2, on the basis of the environment state parameters, predicting a future traffic scene by using a traffic flow prediction model, performing risk assessment on a traffic flow prediction result, and generating a risk map containing spatio-temporal information; s3, according to the current traffic condition, the vehicle characteristics and the task requirements, self-adaptive role allocation is carried out on the vehicles running on the expressway, and a local cooperation network is established based on the roles allocated to the vehicles; s4, under the framework of the optimized traffic control strategy, processing the current traffic condition data and the traffic flow prediction result by using a distributed negotiation algorithm based on an auction mechanism, generating a plurality of candidate lane changing schemes, evaluating the candidate lane changing schemes, and determining an optimal lane changing strategy; and S5, according to the optimal lane changing strategy, coordinating each driving vehicle on the expressway to execute lane changing operation.

The invention relates to the technical field of intelligent logistics distribution, in particular to an automobile market logistics distribution scheduling system based on AI optimization, which comprises a data acquisition module, an order management module, a traffic flow analysis module and a scheduling decision module. Wherein the data acquisition module is used for acquiring current position data and traffic flow data of a delivery vehicle and order information of a customer in real time; the order management module is used for carrying out priority ranking on the orders; the traffic flow analysis module is used for predicting traffic conditions in a period of time in the future and identifying a road section and a time period which are about to be congested; and the scheduling decision module is used for optimizing the scheduling and distribution paths of the vehicles to generate an optimal scheduling scheme. According to the invention, by combining real-time traffic prediction, order priority ranking and a path optimization algorithm, the logistics distribution efficiency is effectively improved, distribution delay and resource waste are reduced, and timely distribution of high-priority orders is ensured.

The invention relates to the technical field of smart city traffic management, in particular to an urban traffic jam intelligent prediction and dispersion system and a method thereof, and the system integrates a data acquisition module, a traffic prediction module, an event influence evaluation module and an intelligent dispersion module. The data acquisition module updates multi-source traffic data in real time, and a solid foundation is provided for prediction; and the traffic prediction module accurately predicts the traffic flow by using a deep learning model combining a graph neural network and a space-time attention mechanism based on the received data. The event influence evaluation module analyzes the influence of emergencies on traffic and ensures the pertinence of a dispersion strategy; the intelligent dispersion module optimizes a traffic signal lamp control scheme in real time by using deep reinforcement learning in combination with the prediction result and the event influence; the system comprehensively senses the traffic condition through multi-source heterogeneous data fusion, the traffic flow prediction accuracy is remarkably improved, and intelligent and efficient traffic management is achieved.

The invention relates to the technical field of path planning, in particular to an intelligent transportation path planning method based on deep learning, which comprises the following steps: constructing a joint network model based on a convolutional neural network and a long short-term memory network, and training the joint network model by using historical traffic data; in the current transportation task, the real-time traffic data of the non-driving road sections in the current path are analyzed based on the joint network model, and the traffic change trend of each non-driving road section at the next moment is predicted; if the undriven road section of the current path has the sudden traffic event, or one or more undriven road sections in the current path are predicted to have traffic jam at the next moment, re-planning the path; and when the path is re-planned, dynamically optimizing the current path through the reinforcement learning model under the constraint of the multi-objective optimization function. According to the method, the accuracy of the future traffic condition can be improved, and the adaptive optimization of the transportation path can be realized.

The invention discloses a traffic signal controller adaptive control method and device, a storage medium and an electronic device. The method comprises the following steps: performing multi-source data fusion and spatial-temporal position sensing graph convolution initialization on real-time traffic monitoring, historical traffic data, meteorological data and urban activity information data; a space-time position sensing graph convolutional network is adopted, geographical and time information is converted into mathematical vectors to construct a dynamic traffic flow prediction model, space-time relevance among traffic nodes is analyzed through the graph convolutional network, and the future traffic flow state is predicted; performing deep reinforcement learning and signal optimization; self-adaptive feedback and system iteration updating are carried out; monitoring and evaluating system performance; the problems that an existing traffic signal controller control method lacks real-time data comprehensive utilization capacity, dynamic traffic condition prediction is insufficient, and signal control strategy optimization is insufficient are solved.

The invention relates to the technical field of online car-hailing order dispatching, in particular to an online car-hailing intelligent order dispatching method and system based on a multi-dimensional rule, and the method comprises the steps: building a standardized multi-dimensional feature set through collecting passenger orders, driver states, traffic conditions, environmental weather and regional events, and carrying out the combined modeling of regional order demands and driver online conditions based on a recurrent neural network, thereby achieving the intelligent order dispatching of the online car-hailing. Outputting expected passenger and driver thermodynamic distribution, constructing expected difference thermodynamic distribution according to the expected passenger and driver thermodynamic distribution, training a scheduling strategy through reinforcement learning, enabling an unloaded vehicle to actively migrate to a supply and demand gap area before an order is generated, and further combining an order generation condition and driver expected off-duty information to obtain an order generation result; and calculating the accumulated driving time required by the driver to return to the expected off-duty place after the driver completes the service, and selecting the work order for distribution when the time difference between the accumulated driving time and the expected off-duty place is minimum, thereby realizing accurate matching between the order distribution strategy and the driver work cycle. The empty driving rate is effectively reduced, and the quick response capability and the resource configuration efficiency of the system in a dynamic supply and demand environment are improved.

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 relates to the technical field of traffic management, in particular to a road traffic dispersion and emergency command system construction method based on multi-source data fusion, and the method comprises the steps: collecting traffic flow sensor data, video monitoring data, meteorological data, social media user feedback data and the like through a multi-source data collection module; a data fusion and processing module is used, and data-level, information-level and decision-level fusion algorithms are adopted to carry out standardization processing, key information extraction and comprehensive evaluation and prediction on multi-source data. And constructing a traffic flow prediction model based on machine learning, such as a long and short-term memory network model, and accurately predicting the traffic flow. And according to the prediction result and the real-time traffic condition, a traffic dispersion strategy making module is used for adjusting signal lamp timing, issuing traffic guidance information and allocating traffic police resources. In the aspect of emergency command, multi-source data are integrated to evaluate event severity, deploy rescue resources and determine an optimal rescue route, and multi-department information sharing and cooperative work are achieved.

The invention provides a pre-training enhanced space-time Transform traffic flow prediction method, which belongs to the field of intelligent traffic, aims to improve the prediction performance in a complex traffic environment by designing a space-time Transform model and a pre-training strategy, and comprises the following steps of: firstly, pre-training an encoder through a mask automatic encoding strategy by utilizing long-term historical traffic flow data, and then pre-training the encoder through a mask automatic encoding strategy; extracting long sequence features by adopting time perception attention embedded with time period information; constructing a pre-training space-time sparse graph generated by a data-driven graph learning layer to capture a dynamic spatial dependency relationship; in the prediction stage, a time feature extraction module and a spatial feature extraction module are designed, an improved multi-head self-attention mechanism is utilized to model correlation between time and space, and long sequence features and spatial-temporal features are adaptively fused through a gating mechanism to obtain a prediction result. According to the method, long-term historical traffic flow sequence information can be effectively extracted, and the traffic flow can be predicted more accurately under a complex traffic condition.

The invention relates to the technical field of information, and discloses an intelligent scheduling method and system for a vehicle-mounted mobile charging system, and the method comprises the steps: obtaining the emergency degree values, geographic positions and charging demand grade values of all charging demands in a current region, and building a demand priority list; acquiring real-time electric quantity states, position information and electric quantity consumption rates of all charging vehicles, and establishing a vehicle availability list; according to the two tables, combined with the target position distance, the road condition and the traffic condition, calculating the estimated time and the residual electric quantity of each charging vehicle arriving at the target position; when the estimated time is smaller than a preset time threshold value and the residual electric quantity meets the requirement, the vehicle is marked as an available vehicle; when the available vehicles are insufficient, judging whether cross-regional scheduling is carried out or not; and optimizing the global scheduling instruction by adopting a scheduling algorithm and dynamically scheduling the charging vehicles. The method can achieve the reasonable scheduling of the charging vehicles, and improves the overall efficiency.

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 provides a multi-modal feature extraction network enhanced negotiation decision-making method based on a space-time directed graph, and aims to enhance a negotiation decision-making algorithm of an automatic driving vehicle. According to the method, three graph structures including a view angle graph, a distance graph and a collision time graph are constructed to comprehensively capture and describe the complex dynamic and asymmetric interaction relation between vehicles. Each graph structure is subjected to feature extraction through a graph attention network (GAT), and deep features of different interactive prior information are fully mined. Then, the extracted multi-modal features are fused through a multi-layer perceptron (MLP), and unified feature representation is formed; the comprehensive representation is further input into a reinforcement learning module based on an Actor-Critic framework to be used for optimizing a driving strategy. Through the innovative architecture design, the decision-making ability and the safety performance of the automatic driving system can be remarkably improved in a complex and dynamic traffic environment, and it is ensured that efficient and stable autonomous driving of the vehicle is achieved under the high-density traffic condition.

The invention, which relates to the technical field of intelligent traffic and illumination control, discloses a street lamp vehicle-road cooperative control system comprising a data acquisition module, a data processing module, a control execution module and a decision management module. The data acquisition module is used for acquiring traffic signal lamp phase, vehicle position and speed, street lamp working state and environment illumination intensity data; according to the invention, through a cross-system collaborative algorithm, the traffic flow direction is predicted in combination with the phase of the traffic signal lamp, the brightness of the street lamp illumination area is adjusted in advance, the energy distribution according to needs is realized, the energy consumption is effectively reduced, and through a vehicle-lamp-cloud three-level decision-making mechanism, the computing power priority is dynamically distributed according to the traffic condition. Traffic signal lamp control and vehicle driving path planning are optimized, the road passing efficiency is improved, traffic congestion is relieved, by pushing the lighting optimization path to the vehicle, the vehicle is driven under the proper lighting condition, the emergency brake risk is reduced, and the road traffic safety level is remarkably improved.

In order to solve the defects in the prior art, the invention discloses a mixed traffic flow cooperative control method based on double-layer parameterization deep reinforcement learning. According to the method, traffic high-dimensional spatial features are extracted from an intersection environment through a convolutional neural network, a first-layer model CNN-MPDQN is constructed in combination with a multi-channel parameterization depth Q network algorithm, and the main task of the method is to select a passing priority lane and determine passing duration. And then, on the basis of the first-layer algorithm, a second-layer DQN algorithm is adopted to optimize and coordinate the passing sequence of the vehicles on the lane, and the problem of vehicle track conflict is solved. Through continuous loop training and iteration of a two-layer deep reinforcement learning algorithm model, model parameters are optimized step by step, and finally an optimal convergence model is obtained; the model can effectively cope with various traffic conditions and vehicle behaviors, improves the traffic efficiency and safety of the intersection, and is especially suitable for a mixed flow intersection without signal control.

The invention relates to a road state monitoring system based on video image analysis, which belongs to the technical field of road monitoring and comprises the following steps of: measuring the speed of a target truck vehicle on a key road section of an express lane; when the vehicle speed is lower than a preset speed threshold value, capturing a scene to obtain a captured video image; analyzing the traffic condition of the scene through the snapshot video image; and judging whether the target truck has traffic violation according to the traffic condition. According to the invention, the problem that traffic violation may be misjudged due to the fact that the influence caused by lane occupation cannot be judged according to actual traffic conditions in the prior art is solved. And identifying vehicle distribution information behind the target truck vehicle in the expressway, and predicting the probability of traffic congestion at present by adopting a Bayesian network model according to the vehicle distribution information. According to the method, the accuracy of traffic condition analysis is improved by using the Bayesian network model, and reference is provided for traffic decision making.

The invention relates to an environmental perception traffic signal control method based on reinforcement learning, and belongs to the technical field of computers. According to the method, traffic signals are intelligently regulated and controlled by sensing environment information and traffic states and combining road states. Firstly, a traffic signal regulation and control strategy is learned in a simulation environment by using a reinforcement learning algorithm, and then the obtained regulation and control strategy is applied to a real scene. The specific implementation comprises the steps of collecting environmental perception data, constructing a simulated traffic environment, configuring a control strategy, setting a typical traffic scene, collecting environmental information, fusing features, setting performance indexes, reinforcing learning optimization, applying a real scene, establishing a feedback mechanism and the like. Through combination of reinforcement learning and environment perception technologies, the method can dynamically adapt to traffic demands in different time and environments, effectively alleviates traffic congestion, improves traffic operation efficiency, and provides an effective scheme for improving urban traffic conditions.

The invention discloses an intelligent route distribution system based on vehicle owner behavior prediction, and relates to the technical field of route distribution, and the distribution system comprises a data collection module which is used for obtaining the historical driving data of a vehicle owner; the behavior prediction module is used for analyzing the driving habit of the vehicle owner and predicting the behavior of the vehicle owner; the real-time traffic analysis module is used for acquiring real-time traffic data and analyzing real-time traffic conditions, weather information and road condition changes; the route optimization module is used for calculating an optimal route based on the prediction result and the real-time data and recommending the optimal route; and the user interaction module is used for displaying the recommended route and receiving user feedback and adjustment suggestions. According to the invention, by analyzing the historical driving data and driving habits of the vehicle owner, the system can provide route recommendation conforming to personal preferences, the user experience is improved, real-time traffic data and weather information are integrated, it is ensured that the recommended route adapts to the current traffic condition and road condition change, and a more accurate and efficient driving route can be provided.

The invention belongs to the technical field of intelligent transportation, and particularly relates to a track representation pre-training system based on deep learning. The invention discloses a track representation pre-training system based on deep learning. The method comprises the following steps: constructing a deep learning model oriented to a multi-scale track structure; the model fully combines static and dynamic features of a road network, and learns feature representation of a moving track from fine granularity to coarse granularity by mixing attention mechanisms of different scales and a self-supervised pre-training normal form, so that richer track space-time semantic information is obtained. The method specifically comprises a road network static feature construction module, a road network dynamic feature construction module, a multi-scale trajectory attention encoder and a travel trajectory self-supervised learning module. The whole pre-training system can enable track features to perceive more spatio-temporal information on different scales, and track vector representation obtained through self-supervised learning in massive tracks can be widely applied to various intelligent traffic system tasks such as travel time estimation and traffic condition prediction.

The invention discloses a traffic signal lamp intelligent control method and system, and relates to the technical field of intelligent traffic systems, and the method comprises the steps: collecting traffic operation and environment data through deploying a sensor group in a target traffic area signal lamp; a self-adaptive analysis model including anomaly detection and congestion prediction is obtained through a traffic scheduling platform, traffic conditions are analyzed, and congestion parameters are obtained. And starting an emergency response mechanism according to the parameters, analyzing and determining signal lamp control strategy parameters, and finally realizing adaptive regulation and control of the traffic signal lamp. The technical problems that the real-time response capability of an existing traffic signal lamp control system is insufficient, and traffic abnormity and congestion cannot be effectively predicted are solved, and through real-time traffic data analysis and intelligent prediction, the real-time response capability of the traffic signal lamp system is enhanced, and the traffic safety is improved. And traffic abnormity and congestion can be effectively predicted and dealt with.