142 results about "Traffic dynamics" patented technology

The invention provides an unmanned aerial vehicle low-altitude intelligent traffic dynamic airspace management and control method and system, and relates to the technical field of intelligent control, and the method comprises the steps: taking an electronic fence geographic coordinate set as a monitoring reference boundary, fusing ADS-B data, meteorological information and an unmanned aerial vehicle equipment state, generating a real-time risk thermodynamic diagram, and outputting a grading alarm instruction; receiving a real-time risk thermodynamic diagram and a grading alarm instruction, and combining wind speed prediction and dynamic airspace occupation data; when the grading alarm instruction is triggered, executing the following operations: constructing a route feasible solution space by taking a no-fly zone and a high-risk zone in the risk thermodynamic diagram as constraint conditions; and iterating an evolutionary path population through selection, intersection and mutation operations of a genetic algorithm by taking the lowest energy consumption as an optimization target, so as to output a global final obstacle avoidance bypassing path, and issuing a route updating instruction to an unmanned aerial vehicle flight control system. According to the invention, the utilization of airspace resources is maximized on the premise of ensuring safety.

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 provides an intelligent traffic dynamic green wave control method and system, a storage medium and a program product, and relates to the field of traffic control systems.The method comprises the steps that real-time traffic data of all lanes and emergency data published by a traffic management department are obtained; obtaining traffic flow prediction results of different directions of each road section in a future preset time based on a neural network model; outputting the congestion level and the congestion duration of each road section in combination with the emergency situation data and a traffic flow theoretical model, and further determining an adjustment sequence; and on the basis of the traffic flow prediction result, calculating the optimal green wave bandwidth of the signal lamp at each intersection in each target road section by using a particle swarm optimization algorithm to obtain an optimal timing combination scheme of the signal lamp in each target road section, and performing real-time timing control. By implementing the method, the traffic flow can be dredged more efficiently, the parking frequency and delay time of vehicles on the road are reduced, the overall traffic capacity of the urban road is improved, and energy consumption and environmental pollution are reduced.

The invention discloses a P4 switch processor core-oriented multi-service traffic packet scheduling method, which mainly comprises the following steps of: proposing a feature extraction method based on a statistical interval and Sketch, integrating data packet time correlation and traffic dynamic change, optimizing a Bloom filter conflict detection mechanism, and improving the adaptability and accuracy of an algorithm in a limited resource environment; a decision tree model is combined with scheduling, a decision tree is reconstructed to adapt to an operation mode and a data processing requirement of a P4 switch, and high-precision service flow classification under linear speed operation is ensured; a packet scheduling method AD-PIFO is designed based on a variable priority, the forwarding priority of a data packet is dynamically adjusted, the QoS requirements of different services are met, and the overall performance and efficiency of the switch are improved. The method is outstanding in high-priority traffic throughput and low-delay performance, is superior to similar algorithms in medium-low priority traffic throughput, is stable and effective under different load conditions, is reasonable in extra time overhead of data packet transmission, and is widely applied to SDN network optimization data packet scheduling scenes.

The invention relates to the technical field of network communication, and discloses an intelligent control method and system of a switch. The method comprises the following steps: receiving a heterogeneous data stream in a target network domain; performing data processing by using a preset flow feature aggregation model, and generating a flow dynamic pressure field distribution diagram and a protocol priority mapping matrix; constructing an adaptive scheduling threshold model, and generating a multi-target flow control instruction set; a cascade effect is simulated based on a network congestion chained conduction model, and collaborative routing parameters are optimized; and iteratively optimizing through a mixed integer programming framework, and outputting a network scheduling action sequence to a switch control engine. The system correspondingly comprises a data receiving module, a data processing module, an instruction generation module, a simulation optimization module and an instruction output module. According to the method, heterogeneous data can be effectively processed, congestion can be accurately predicted, flow control can be optimized, the overall performance of the network can be improved, and communication requirements in a complex network environment can be met.

The invention relates to the technical field of smart city traffic, and discloses a smart city traffic dynamic optimization system and method based on digital twinning. The system obtains urban traffic network multi-dimensional data from a plurality of heterogeneous data sources through a traffic multi-dimensional data acquisition module and integrates the urban traffic network multi-dimensional data into a traffic related data warehouse; a traffic digital twinning model construction module extracts features from the data warehouse to generate a traffic related feature matrix, and a digital twinning traffic dynamic model is constructed according to the traffic related feature matrix to output a theoretical traffic state value; the traffic flow map construction module determines a dimension link map of each dimension and constructs a traffic flow link map; the traffic core feature screening module screens a traffic core feature sequence based on the map; the traffic multi-dimensional optimization analysis module performs multi-dimensional difference analysis on the theoretical traffic state value and real-time actually measured traffic data, and generates a region-level difference coefficient matrix in combination with the core feature sequence; and the traffic event association positioning module can realize accurate management and dynamic optimization of urban traffic.

The invention relates to a virtual power plant load prediction and dynamic response method and system integrated with vehicle network interaction. The method comprises the following steps: acquiring and processing historical charging data to generate a user data set; extracting and analyzing the user data set to generate a charging behavior prediction result; collecting traffic flow data in real time and matching to generate a traffic influence coefficient; constructing a regional charging demand prediction model, and generating regional charging demand distribution; acquiring external environment event data and processing the data to generate a sudden load correction value; a comprehensive load prediction value is generated by fusing multi-level factors, the power distribution and the charging priority of the charging pile are adjusted, and a scheduling instruction is output to complete closed-loop operation, so that the problem of load prediction deviation caused by user behavior randomness, traffic dynamic coupling and unpredictability of emergencies is solved; accurate modeling and collaborative optimization of multi-level dynamic factors are realized, and the scheduling efficiency of the virtual power plant to the vehicle network interaction resources is improved.

The invention provides a regional boundary traffic signal control method and system based on model predictive control, and belongs to the technical field of intelligent traffic. The method specifically comprises the following steps: partitioning a road network region; collecting traffic parameters of each region of the road network, and fitting an MFD curve of each region based on the collected data; establishing an optimization model by taking minimization of the number of vehicles in a road network as a target; an optimal boundary control value is obtained through optimization solution of an MPC controller with a built-in SLSQP algorithm, so that boundary traffic flow and boundary traffic signal lamp timing of each region of the road network are controlled; a traffic flow is simulated based on SUMO, and from a current time step to a next time step, an MPC controller with a built-in SLSQP algorithm is used for continuous iterative optimization solution to realize rolling optimization. And modeling the traffic state of each region by using MFD to obtain the flow dynamic state of the region. By establishing a network structure to simulate traffic dynamics, the MPC method is realized to control transfer of boundary traffic flow and dynamically control timing of traffic signal lamps, so that traffic congestion is relieved.

The invention provides a construction site monitoring method and system based on Beidou positioning and digital twinning, and relates to the technical field of highway construction supervision. According to the invention, real-time position information of equipment, personnel and vehicles on a construction site is accurately obtained through a Beidou positioning technology, and an equipment and personnel dynamic model and a traffic dynamic model are constructed in combination with vehicle passing data collected by road test equipment. And fusing the dynamic model with a pre-constructed construction site environment model by using a digital twinning technology to generate a high-precision construction site digital twinning model. According to the model, real-time three-dimensional visual monitoring of a construction site is realized, external intrusion and internal collision risks are effectively prevented, illegal behaviors of not wearing a safety helmet and the like are identified in combination with AI, and accident tracing is realized through the Internet of Things. The construction site can be comprehensively monitored without manual inspection, and the safety and inspection efficiency of highway construction are remarkably improved.

The invention provides a flow anomaly analysis and detection method, system and device based on an industrial control network, and a medium, and the method comprises the steps: carrying out the collection and analysis, data preprocessing, data calculation and analysis, and persistent storage of the flow data of a process business network of an industrial control system; a network flow behavior baseline model is automatically generated according to a process service communication condition, a flow threshold sensitivity dynamic adjustment mechanism can be utilized, learning and self-optimization can be performed from new flow data, and the network flow behavior baseline model becomes more intelligent and accurate as time goes on, and can better adapt to continuous evolution of network threats; the network traffic behavior baseline model is used for carrying out real-time network traffic dynamic detection, the traffic abnormal condition of each device in the network is found in time, an alarm is given, and the problems that in the prior art, a statistical analysis means is not established for a specific process service communication link, threshold setting is lack of accuracy, and threshold adjustment is difficult are solved.

The invention relates to the technical field of automatic driving, and discloses a vehicle system path adjustment method and device based on a graph neural network, and the method comprises the steps: dividing a target traffic network based on geographic position data and traffic flow characteristic data, and obtaining a plurality of independent regions; constructing a region graph neural network model, training the region graph neural network model to obtain road feature embedded representation, determining node feature information and edge feature information of boundaries of adjacent independent regions, and sharing the node feature information and the edge feature information to the region graph neural network model of the adjacent independent regions. Shared information is obtained; and updating the area graph neural network model by using real-time traffic dynamic data, determining an optimal path in the independent area, and determining a global optimal vehicle system path based on the shared information and the optimal path in the independent area. According to the invention, real-time path adjustment of the vehicle system in a complex traffic environment is realized.

The invention belongs to the technical field of dynamic traffic, and particularly discloses a multi-source data fusion traffic dynamic induction method, which comprises the following steps: S1, traffic information is acquired from multi-source heterogeneous data, and the data comprises real-time traffic information, navigation map data, a road network topological structure and a public traffic timetable; s2, carrying out data fusion processing on the traffic information obtained in the step S1, and generating fused traffic state estimation; s3, on the basis of travel historical records and preferences of the user, constructing a personalized travel guide model, and providing an optimal travel suggestion; and S4, monitoring the traffic condition in real time, performing dynamic adjustment based on user feedback and emergencies, and optimizing the travel path. By integrating real-time information from various data sources, comprehensive perception of road traffic conditions is ensured, traffic changes in space and time can be processed at the same time, the weight is dynamically adjusted according to the reliability of the data sources, and the influence of data islands and information loss is reduced.

The invention relates to the technical field of industrial control, in particular to a TSN-based industrial equipment cooperative control method and system and a readable storage medium. The method comprises the following steps that: a switch acquires the flow of a TSN network from a preset detection starting moment t1 to a current moment tK; constructing an optimization objective function of the static scheduling strategy; obtaining a flow priority, a distribution time slot and a bandwidth resource which enable the optimization objective function to reach the minimum; and constructing a dynamic scheduling model based on an intelligent scheduling algorithm of an ant colony optimization algorithm so as to obtain adjusted flow priority, allocation time slot and bandwidth resources. According to the invention, a static traffic scheduling strategy is adopted to ensure that key control traffic can be transmitted preferentially; the network state is analyzed in real time by adopting an intelligent flow dynamic scheduling algorithm in combination with an ant colony optimization algorithm, and network resource allocation is optimized; the problem that traditional flow scheduling cannot adapt to network changes in real time is effectively solved.

The invention discloses an intelligent network connection automobile collaborative decision-making method based on double interactive perception, and the method comprises the following steps: designing a double interactive perception collaborative control strategy DIACC through employing a centralized training and distributed execution multi-agent reinforcement learning framework; using a distributed interactive adaptive decision module D-IADM to learn interactive characteristics with surrounding vehicles and traffic environment information; a centralized interaction enhancement evaluator (C-IEC) module is utilized to capture a relationship between global vehicle interaction and global traffic dynamics. According to the intelligent connected automobile collaborative decision-making method based on double interactive perception, the behaviors of the intelligent connected automobile are actively adjusted in advance, transverse conflicts between a pre-confluence area and other vehicles are reduced, on the other hand, the behaviors of the intelligent connected automobile actively guide and coordinate manual driving of the automobile, and the driving safety of the automobile is improved. Therefore, the overall traffic performance is improved.

The invention discloses a multi-modal trajectory prediction method based on anchor points. The method belongs to the technical field of automatic driving, and comprises the following operation steps: firstly, preprocessing a pre-collected track data set, and sampling on a track line to obtain candidate anchor points; and then, an MGT network architecture is constructed, interaction between track features and fusion between lane features are realized after a graph neural network technology is fused, the MGT adopts an embedded hierarchical design, the hierarchical structure firstly preliminarily optimizes anchor point positions through an internal dependency relationship between tracks, and further, track surrounding environment information obtained by an API is combined, so that the track surrounding environment information is obtained, and the track surrounding environment information is obtained. Environment features and potential influences of surrounding traffic dynamics are fused to anchor point position features; and finally, decoding the optimized anchor point by using a decoder, and designing a fitting function so as to obtain a complete predicted trajectory coordinate. Multiple factors in the MGT network are fully considered, the anchor point coordinates are optimized step by step, and the credibility and accuracy of prediction are remarkably improved.

The invention discloses a distributed denial of service attack defense method and device and electronic equipment, and relates to the technical field of network security. The method comprises the following steps: based on a connection relationship between nodes in a current network topology structure, obtaining a flow characteristic parameter of each link in the network topology structure in a current detection period; according to the traffic characteristic parameters, respectively obtaining a traffic dynamic weight of each link in the network topology structure; respectively obtaining a dynamic ranking value of each node in the network topology structure according to the flow dynamic weight; according to the dynamic ranking values, respectively obtaining node feature parameters of each node in the network topology structure; and judging whether the node characteristic parameter of each node exceeds a node characteristic parameter threshold value corresponding to the node, and when the node characteristic parameter of any node exceeds the node characteristic parameter threshold value corresponding to the node, starting a defense strategy for the node. The timeliness and accuracy of distributed denial of service attack defense are improved, and the network security is ensured.

The invention relates to the technical field of soil detection, and particularly discloses a soil detection method based on a freeze thawing-unsaturated dynamic load hollow cylinder torsional shear apparatus, which comprises the following steps: respectively applying a vertical dynamic load, a torsional shear dynamic load and confining pressure through a vertical servo motor, a torsional shear servo motor and a triaxial pressure chamber, and simulating the stress state of a roadbed under the action of a dynamic wheel load; the unsaturated state of the sample is controlled through an axis translation method for controlling pore air pressure and pore water pressure, and the hydraulic state of the roadbed in the actual situation is simulated; the temperature of the sample is further controlled through a low-temperature water bath connected with the upper bottom surface and the lower bottom surface of the sample, and the temperature gradient in the roadbed freezing and thawing process is simulated; therefore, the instrument can introduce the influence of freezing and thawing and unsaturation while simulating the traffic dynamic load, can more accurately reflect the mechanical response of the cold region roadbed under the coupling action of freezing and thawing-unsaturation-traffic load, and provides a more accurate and more scientific basis for the performance evaluation of the cold region roadbed.

The invention discloses an interface request flow dynamic control method and device, a terminal and a medium, and relates to the technical field of the Internet, and the scheme provided by the invention enables a flow control strategy to reflect the actual processing capability of a server in real time by establishing the dynamic association between a flow limiting threshold and a resource state. By analyzing the relationship between the resource utilization rate and the request processing capacity, it is found that higher request processing capacity is allowed in the idle state of the resources, and the processing threshold should be reduced when the resources are nearly fully loaded. A linkage mechanism of a flow pressure index and resource capacity scheduling is further considered, when the flow pressure exceeds a preset interval, an elastic capacity expansion and contraction operation is triggered, cooperative closed-loop control of flow limiting and capacity expansion is achieved, and a flow limiting threshold value and the resource capacity form dynamic matching.

The invention belongs to the technical field of computer information calculation. The invention provides a sparse trajectory recovery method based on a graph neural network. According to the embodiment of the invention, the STREAM model based on the graph neural network is constructed, and for a given sparse trajectory, the graph neural network constructs a sub-graph for each point to represent a surrounding road network area. Convolution operation is applied to a speed matrix of a time period to obtain global traffic representation, and local traffic dynamics are learned through traffic information of an adjacent road section. Global traffic representation is transmitted into an encoder and fused with local traffic conditions, and a complex space-time dependency relationship is captured. The network combines hierarchical motion dynamics, and can extract global and local traffic dynamics in an encoder, so that the decoder can accurately reconstruct missing track points.

The invention belongs to the technical field of traffic management, and particularly relates to a dynamic and static traffic cooperative governance method and system, and the method comprises the steps: collecting traffic dynamic and static data, and uploading the data to a traffic basic platform for data processing, network transmission, safety protection, privacy protection and data storage; the traffic dynamic and static data are input into the data platform for data arrangement, the artificial intelligence platform processes the data from different ports, the traffic data are deeply analyzed and predicted by using an intelligent algorithm, and then different traffic problems are optimized and adjusted by using the integrated algorithm platform; finally, the algorithm is applied to various traffic scenes, real-time data and analysis results of urban traffic are visually displayed through an intelligent large screen, and efficient management and optimization of an urban traffic system are achieved through the functions of multi-source data integration, intelligent algorithm support, large-scale data processing and simulation reasoning. And technical support and collaborative optimization are provided for constructing a more intelligent, efficient and safe intelligent traffic system.

The invention provides an urban traffic dynamic deduction and real-time planning method and system based on multi-source position data fusion. Time-space references of signaling, MDT and MR data are unified through a dynamic rasterization mechanism, and the problem of data isomerism is solved by adopting anchor point selection of signal weighting and a triple criterion time expansion algorithm; generating a minute-level updated dynamic OD matrix based on the hierarchical road network constraint model; a METANET macroscopic traffic flow model and EKF extended Kalman filtering are fused to realize traffic state deduction, and parameter online calibration is supported; signal timing optimization, path induction and emergency control schemes are generated in combination with real-time deduction results, and a sensing-deduction-decision-evaluation closed loop is formed. The method can improve road network traffic efficiency, and is especially suitable for response of sudden traffic events.

The invention relates to the technical field of safety testing, and particularly provides a method, device and equipment for generating an expected function safety testing scene library, and the method comprises the steps: obtaining a parameter combination of key parameters in a pre-constructed basic scene; wherein the key parameters comprise environment parameters, traffic dynamic parameters and vehicle system parameters; generating a plurality of test scenes based on the parameter combination; after screening the plurality of test scenes, constructing a test scene library; wherein the test scene library is used for an expected function safety test of the intelligent driving vehicle. According to the embodiment of the invention, a rich expected function security test scene library can be constructed, and support is provided for expected function security test.

The invention discloses a real-time road condition-oriented AGV collaborative congestion avoidance path planning method, and relates to the technical field of AGV path planning, and the method comprises the steps: building a global road condition map based on AGV state data, and carrying out the dynamic updating of the global road condition map, and the dynamic updating comprises the weight updating based on an AGV density value and an average speed; based on the updated global road condition map, adopting a path planning algorithm to generate an AGV global optimization cooperative path set; the cooperative path set is converted into a control instruction, dynamic compensation is carried out on the control instruction through load sensing, and a corrected control instruction is obtained and issued to the AGV; according to the method, global road condition dynamic updating is combined with dynamic control of collaborative path optimization and load perception, multi-AGV global flow balance and real-time congestion prevention are achieved, and the problems that under the high load, path conflicts are frequent, local congestion cannot be actively adjusted, and the overall efficiency is reduced due to single AGV optimization are solved.

The invention discloses an electric vehicle charging pile planning method, system and device and a medium, and the method comprises the steps: obtaining urban road data, and building a dynamic traffic network matrix; the influence of different building types on traffic is considered, the dynamic traffic network matrix is corrected, and driving parameters are calculated; through a path search and optimization algorithm and a multi-dimensional traffic demand analysis technology, the travel path of the electric vehicle is simulated, and the charging demand of the electric vehicle is predicted; and establishing an optimization model by taking charging economic benefit maximization as a target, and generating a charging station capacity configuration scheme. The method breaks through the limitation of traditional experience planning, and achieves the efficient configuration of charging resources through the coupling analysis of building features and traffic dynamics.

The invention discloses a multi-lane heterogeneous traffic joint modeling method and system based on a GNN-PINN coupling architecture. The method comprises the following steps: constructing a space-time dynamic graph coding lane changing rule through a GNN, embedding a traffic flow physical constraint through a PINN, and solving a source item; according to the method, CAV behaviors are described by using improved IDM in the mid-microscopic view, an LWR equation subjected to GNN lane change flow correction is solved by using PINN in the macro view, GNN and PINN closed-loop optimization is realized by combining a double-path back propagation mechanism, and a model is optimized based on a residual loss function fusing physical loss, data driving items and boundary constraints. The system comprises a dynamic graph topology module, a GNN coding module, a PINN solving module and the like, the multi-lane traffic dynamic description precision can be improved, the multi-scale characteristic is considered, real-time simulation is supported, and the system is suitable for heterogeneous traffic modeling.

The invention discloses a regional traffic dynamic optimization control method based on vehicle-road cloud coordination, relates to the technical field of traffic control, and designs a self-adaptive space-time dynamic compensation algorithm and a three-level communication fault-tolerant architecture aiming at space-time asynchronization and communication interruption risks existing in multi-source data of a vehicle end, a road side and a cloud end. According to the method, accurate alignment and stable transmission of multi-dimensional heterogeneous data are realized, an attention mechanism enhanced multi-agent deep reinforcement learning architecture is adopted, online element reinforcement learning is combined, global and local optimization control of cloud-side double-loop cooperation is realized, communication is performed by adopting a block chain-based security consensus protocol, the system security and stability are effectively guaranteed, and the system reliability is improved. According to the method, the operation efficiency, the emergency response capability and the environmental friendliness of urban smart traffic are remarkably improved, and the method is suitable for a smart city global traffic optimization scene.

The invention discloses a traffic signal scheduling system and method based on road section congestion information, and relates to the technical field of traffic management and control. Comprising the following steps: S1, data acquisition and preprocessing: acquiring original traffic data of a road section in real time through a multi-source data acquisition unit, the original traffic data comprising traffic flow, vehicle speed, queuing length, occupancy and travel time; meanwhile, external environment data are obtained, wherein the external environment data comprise weather information, holiday and festival information and traffic event information; traffic data and external environment data are acquired in real time through the multi-source data acquisition unit, data quality is guaranteed through cleaning, denoising and standardized preprocessing, and the problem that data fusion of an existing system is insufficient is solved; by means of an adaptive weight learning model, historical traffic data, real-time data and environmental data are combined to dynamically adjust a congestion evaluation index weight, and the defect that a fixed or empirical weight is difficult to adapt to traffic dynamic changes is overcome.

The invention discloses a multi-target flow dynamic allocation method based on deep learning, which comprises the following steps: collecting network topology data, link operation data and service flow request data to form a network state sequence; a multi-target constraint set is obtained and written into a target constraint description table; constructing a state vector, and dividing the state vector into multiple groups of input sub-vectors; carrying out conjoint analysis on the service flow, and constructing a candidate allocation action set; constructing an improved CMAC network model, and outputting an action value vector; constructing an anti-multi-arm bandit decision-making module, and outputting action selection distribution; and executing multi-target feasible region projection processing to generate a feasible traffic distribution decision. According to the method, the improved CMAC network model is introduced, and the anti-multi-arm bandit decision-making method is combined, so that stable and self-adaptive dynamic distribution of the network traffic under the multi-target constraint and complex network state is realized.

The invention is suitable for the technical field of traffic management, and provides an urban traffic dynamic command method and system based on digital twinning. According to the method, the high-quality data set is generated through direct multi-source data acquisition, restoration and integration; hierarchical dynamic storage is carried out, and block chain security management is carried out; carrying out stored multi-source data acquisition and processing, and constructing a real-time dynamic traffic twin model; based on the real-time dynamic traffic twinborn model, decision optimization of traffic control is carried out, evaluation feedback is carried out through edge calculation, and model parameters of the real-time dynamic traffic twinborn model are corrected; a three-level data storage system is adopted, a data management center is constructed, and data life cycle management is carried out. According to the method, the real-time dynamic traffic twin model can be constructed by fusing multi-source data, traffic flow simulation, congestion prediction and dynamic optimization are realized, a scientific basis is provided for smart city traffic management, traffic congestion is effectively relieved, and the urban traffic efficiency is improved.

The invention provides a traffic dynamic cooperative control method and system based on multi-source heterogeneous data fusion, and relates to the technical field of traffic control, and the method comprises the steps: obtaining a multi-source heterogeneous data flow of a management and control region through a ubiquitous perception terminal cluster, carrying out the fine-grained data fusion, and outputting a space-time fusion state matrix; performing blind area state completion to generate a panoramic traffic state map; performing dynamic demand prediction based on the space-time deep learning model, and outputting a pressure distribution matrix; dynamically delimiting a traffic control boundary; performing multi-agent collaborative decision, and outputting a collaborative strategy packet; and according to the real-time dissipation rate deviation, performing closed-loop updating of the traffic coordination strategy. The technical problems that traffic data in the prior art mainly depends on a single data source, data missing or coverage blind areas are prone to occurring, real traffic conditions cannot be comprehensively reflected, traffic flow prediction errors and bottleneck prediction errors are caused, and finally traffic congestion is aggravated are solved.