482 results about "Traffic network" patented technology

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 relates to the field of real-time visualization of road conditions, in particular to a data simulation visualization rendering method and system for urban traffic road conditions. The method comprises the following steps: extracting a real-time satellite streetscape image based on urban satellite remote sensing scanning, and performing scene pixel-level segmentation to obtain scene texture rendering parameters; scene illumination visual identification is carried out according to the real-time satellite streetscape image, traffic scene background modeling is carried out based on scene texture rendering parameters, and a real-time scene background model is constructed; the method comprises the following steps: acquiring urban-level multi-source traffic monitoring data flow, performing vehicle state sensing, and constructing a multi-dimensional particle feature matrix; road network topological correlation analysis and global traffic network state perception are carried out according to the real-time satellite streetscape images, and a road network state perception model is constructed. According to the invention, a real real-time traffic environment is visualized, scene effects in different traffic states are presented, the current road condition can be rapidly evaluated, and the traffic control decision efficiency is improved.

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 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 traffic network toughness diagnosis method under flood disasters based on a space-time diagram neural network, and relates to the crossing field of traffic engineering and artificial intelligence. The method comprises the following steps: collecting traffic topology, flood monitoring and traffic flow data, and carrying out space-time alignment; a flood coupling dynamic space-time diagram is constructed, a water depth-traffic capacity response mechanism is introduced, and real-time mapping from a disaster physical state to a network topology is realized by utilizing an attenuation function meeting physical monotonicity constraint and dynamically updating an edge weight of a diagram structure according to real-time water depth; inputting the dynamic graph into a pre-trained space-time graph neural network model, extracting space-time evolution characteristics and outputting a toughness diagnosis result; model training adopts a toughness label generated based on an anti-fact baseline to carry out supervised learning, and introduces a physical constraint loss function. According to the method, the problems of decoupling of disaster features and graph structures and unavailability of toughness labels in the prior art are solved, and the physical consistency and accuracy of diagnosis are improved.

The invention relates to the technical field of intelligent traffic, in particular to a dynamic road network collaborative capacity expansion decision-making system based on federated learning-digital twinning, which comprises the following steps of: calibrating pulse type and periodic type data flow weights through a dynamic weight distributor, and generating a normalized feature vector; fusing the normalized feature vectors of all the domains through a privacy protection aggregation engine to obtain a passenger flow pressure distribution prediction matrix; the reconstruction module is used for constructing a digital twinborn body based on the prediction matrix and initializing the digital twinborn body; and based on the initialized twinborn environment, recognizing and sensing a missing region through a blind area data reconstructor, and fusing historical features and real-time data streams of adjacent nodes by adopting a space-time correlation algorithm to reconstruct a complete road network state. According to the method, the dynamic road network collaborative expansion decision system is constructed by fusing federated learning and digital twinning technologies, and the expansion decision efficiency of the traffic road network is improved.

The invention discloses a global traffic signal control method based on a regional context enhanced large language model, and the method comprises the steps: dividing a target region into K traffic signal lamp control regions according to a traffic network structure of the target region; global real-time traffic information of the target area is collected and converted into global traffic text information; and inputting the global traffic text information into the large language model after LoRA fine tuning, and outputting a global traffic signal lamp control strategy of the target area, namely a traffic signal lamp phase of each intersection. In the fine tuning process of the large language model, an optimal phase decision is generated by using a reinforcement learning agent, a decision result is converted into text data, a region-level optimal phase decision data set is constructed according to an adjacent region division strategy, fine tuning is performed on the model by using the data set, region loss is calculated, and the large language model is optimized through the loss. According to the invention, optimization of global traffic signal lamp control can be realized, the overall traffic delay is reduced, and the traffic efficiency is improved.

The invention discloses a public traffic composite traffic network key node identification method based on an improved K-shell algorithm, and the method comprises the steps: firstly extracting the line, geographic position, vehicle arrival interval and rated passenger capacity information of a subway and a bus station from multi-source data, and constructing a weighted composite traffic network considering the intra-layer transportation capacity and inter-layer transfer relation; node stripping is carried out based on a K-shell algorithm, and a shell fine position KP value is calculated in combination with a node removal sequence; further introducing a neighborhood structure overlap ratio, and constructing a node local propagation influence index; meanwhile, a comprehensive correction coefficient is constructed by considering the difference between intra-layer node strength and transfer attributes and the transportation capacity difference between subway and bus layers; and finally, a node comprehensive importance index IKA is formed and is used for sorting and identifying network key nodes. Finally, the recognition effect is verified through network efficiency, connectivity and SIR (susceptibility-infection-recovery) propagation simulation, and the result shows that the method is superior to a traditional centrality algorithm in the aspects of recognition precision and propagation influence measurement.

The invention discloses a global dynamic perception space-time traffic flow prediction method based on graph packet representation learning, and belongs to the technical field of traffic flow prediction, and the method comprises the following steps: S1, traffic data input, S2, traffic graph packet construction, S3, graph packet initial feature extraction, S4, time sequence feature extraction, S5, spatial feature extraction, and S6, traffic flow prediction and output. Through a space-time modeling technology of graph packet representation learning and global dynamic perception, space-time characteristic elements of a traffic road network can be comprehensively covered, traditional traffic indexes such as flow and speed are concerned, elements such as road network topological association and cross-regional multi-hop association are also included, a dynamic dependency relationship between a time sequence and a spatial dimension is deeply mined, and a real-time dynamic perception effect is achieved. Therefore, the prediction result can reflect the real evolution law of the traffic flow more accurately, and a more scientific basis is provided for traffic management and decision making.

The invention discloses a traffic signal global collaborative prediction method based on a quantum entanglement state, and the method comprises the steps: building a quantization model of a dynamic evolution path of a target traffic network in a time window in the future, and constructing a space-time diagram model; mapping the signal phase state of the intersection into a time-space integrated quantum state by adopting a layered quantum coding scheme; on the basis of a preset traffic optimization target, constructing Hamiltonian including spatial coupling, time evolution and a cost item, solving a ground state of the Hamiltonian through a mixed quantum-classical calculation method, and determining an optimal dynamic evolution path; coding the actual state of the current traffic network into an initial state, carrying out sequential measurement through an optimal evolution operator, decoding a cooperative signal control strategy of each time point in the future, and executing the cooperative signal control strategy through a rolling time domain control framework; according to the invention, global optimization is carried out by using quantum parallelism, the optimal cooperation strategy in the whole space-time range can be obtained at one time, and the overall operation efficiency, predictability and robustness of the traffic network are improved.

The invention discloses a multi-network cascading failure propagation prediction method based on a heterogeneous graph neural network under a disaster. The method comprises the following steps: acquiring multi-source data of disaster, electric power, communication and traffic networks; constructing a four-layer heterogeneous graph model, and defining multi-type interlayer edges; establishing a mapping relation between disaster physical quantities and physical node health states, and predicting an initial fault state at a disaster impact moment; defining a multi-scale and multi-mechanism propagation rule based on interlayer edges, and simulating a cascading failure process; a space-time heterogeneous graph neural network model is constructed, a heterogeneous graph neural network module and a gating circulation unit module are stacked in the model, and a double-end prediction layer is adopted to predict the continuous operation state and the discrete function state of nodes in an autoregression mode; and post-processing a state time sequence output by the model, and reconstructing a cross-domain cascading failure propagation path through a causal attribution algorithm. According to the method, the whole evolution process from disaster occurrence to multi-network cascading failure can be accurately simulated, and multi-scale failure prediction and causal analysis are realized.

The invention relates to the technical field of parking resource scheduling, in particular to an intelligent parking resource scheduling system based on multi-source data analysis, which realizes full-scale digital mapping from vehicle individual behaviors and parking lot flow fields to a regional traffic network by constructing microscopic-mesoscopic-macroscopic three-level parking twin bodies and fusing dynamic and static multi-source data. Through scale conversion and data interaction among the three-level model, dynamic characteristics of parking resources in space and time dimensions are accurately captured, a high-fidelity and fine-grained digital twinborn base is provided for scheduling decision making, splitting of local optimization and global collaboration in traditional scheduling is broken through, the accuracy and foresight of resource allocation are supported, and the scheduling efficiency is improved. Based on dynamic construction of an algorithm strategy library and twinborn simulation evaluation, the scheme takes a multi-dimensional scheduling value as a core to realize closed-loop optimization of algorithm combination-effect rehearsal-strategy optimization-dynamic adjustment.

The invention relates to the technical field of intelligent traffic, and discloses a dynamic traffic guidance method based on traffic flow prediction under the influence of navigation information, and the method specifically comprises the steps: constructing a random dynamic traffic network model, and carrying out the quantitative description of OD demands and the time-varying characteristics of road traffic flow; establishing a path travel time perception model under the influence of navigation information, and calculating a path selection probability by adopting a Logit model; constructing a hybrid traffic distribution model based on dynamic system optimization and dynamic user balance, and performing iterative solution by adopting a continuous averaging method with a residual flow updating mechanism; forming a reinforcement learning environment by constructing a state space function, an action space function and a reward function; and training the model by using a DDQN algorithm, and optimizing a path selection strategy. According to the method, the problems of low induction precision and poor adaptability caused by neglecting node delay and lacking information fusion and utilization in the existing method are effectively solved, and the dynamic traffic induction effect is remarkably improved.

The invention relates to a traffic flow prediction method and system based on a dynamic perception expert network, and the method comprises the steps: obtaining traffic observation data, constructing a traffic network diagram, carrying out the feature embedding, and generating an initial spatial-temporal feature vector; inputting the initial spatial-temporal feature vector into a double-path time encoder, and respectively extracting a personalized time sequence feature vector and a time sequence dynamic feature vector through parallel channel independent paths and channel mixed paths; carrying out vector fusion through a gating mechanism to obtain a time context feature vector, inputting the time context feature vector into a multi-scale hybrid expert model, activating a plurality of most relevant time scale experts, and generating a corresponding routing weight; and generating a spatial dependency graph through a scale condition dynamic graph generator, performing graph convolution to extract a plurality of spatio-temporal feature vectors, performing weighted aggregation, sending the spatio-temporal feature vectors into a prediction model, and generating a traffic prediction value. Compared with the prior art, the model constructed by the method is relatively high in prediction precision and relatively high in robustness in a complex traffic scene.

The invention relates to an emergency resource scheduling strategy generation method considering material demand urgency, and the method comprises the steps: representing all equipment on a target system through a GIS technology, obtaining heat supply network information, carrying out the coupling of the heat supply network information and traffic network information corresponding to the target system, and obtaining a geographic information model of a heat supply facility; based on a K-means clustering algorithm, determining an emergency resource scheduling task of the material distribution center according to the position information of the fault point on the geographic information model, the position information of the material distribution center and the load data of the first-aid repair vehicle in the material distribution center; calculating an optimal path of each emergency resource scheduling task by using a grey wolf algorithm to obtain an initial scheduling strategy; according to the method, the demand urgency degree of the fault point is obtained, the initial scheduling strategy is updated according to the demand urgency degree, the emergency resource scheduling strategy is obtained, efficient and accurate emergency resource scheduling can be achieved, and the scheduling efficiency and reliability of the system are remarkably improved.

The invention discloses a multi-dimensional flood risk assessment method and system oriented to personnel, traffic and economy. The method comprises the following steps: acquiring multi-source data, and generating flood spatio-temporal evolution data through hydrodynamic simulation; constructing a time-dependent traffic network driven by flood power, wherein the road traffic capacity is dynamically attenuated along with hydrodynamic parameters; on the basis of a space-time racing mechanism, calculating the time difference between the personnel danger arrival time and the shortest evacuation time consumption, and constructing an evacuation time margin and evacuation success probability model so as to dynamically correct the static personnel exposure risk and obtain a personnel safety risk index; and in combination with physical state attenuation and asset exposure characteristics, traffic operation and economic loss risk indexes are calculated respectively, and multi-dimensional fusion evaluation is carried out. According to the method, the problem that dynamic interaction between flood routing and personnel evacuation is neglected in traditional static assessment is solved, accurate calculation of personnel trapped probability and critical road section rush-through marginal contribution is realized, and scientific decision support is provided for flood control command and emergency rescue.

The invention discloses a power distribution network fault first-aid repair optimization method based on a power grid-traffic network joint simulation system, and aims to improve the efficiency of task allocation, resource scheduling, path optimization and load recovery in a fault first-aid repair process through a simulation technology. According to the method, a power grid-traffic network joint simulation system is utilized, factors such as the power grid operation state, the traffic condition and the emergency degree of the first-aid repair task are combined, high-precision modeling and optimization are carried out on the fault first-aid repair process, an optimization model is constructed through an objective function and constraint conditions, first-aid repair task scheduling is optimal, a first-aid repair path is feasible, and power supply recovery is optimal. The method can be used for simulation and optimization of actual fault first-aid repair of the power distribution network, the scientificity of first-aid repair decision is improved, the power failure time is shortened, and the power supply reliability is improved.

The invention discloses a traffic flow prediction method based on a multi-scale time window adaptive graph bias neural network, and belongs to the field of deep learning and intelligent traffic. The method comprises the following steps: collecting flow, speed and occupancy data of traffic network nodes, and constructing a historical data set; constructing a time embedding module, and extracting intra-day and intra-week time features; constructing a multi-scale time window trend sensing module, and capturing short-term fluctuation and long-term trend of the traffic flow; a time condition adaptive graph bias module is constructed, a graph bias matrix is dynamically generated according to the time context, and time-varying spatial dependence is modeled; a sparse space attention module is constructed, the calculation complexity is reduced, and spatial-temporal features are fused; and a self-adaptive double-end output module is constructed, a gating coefficient is generated based on the volatility and the quartile distance, and linear and nonlinear branches are fused to output a multi-step prediction result. According to the method, multi-scale spatial-temporal feature fusion and adaptive prediction of the traffic flow are realized, and the prediction precision and robustness are improved.

The invention provides an unmanned aerial vehicle communication intensity calculation method and system based on data driving, and belongs to the technical field of low-altitude traffic network communication, and the method comprises the steps: processing obtained communication parameter data through employing a pre-trained graph neural network model, and achieving the prediction of the signal intensity of each grid; wherein the graph neural network model is composed of a plurality of graph attention network modules, a full connection layer and an output layer. According to the method, characteristic parameters related to urban environment and signal attenuation serve as input, signal intensity values serve as output, a graph neural network model is trained, and a high-precision and high-efficiency low-altitude communication signal attenuation estimation model is established. The model can effectively estimate the low-altitude communication signal intensity in different urban scenes, and the estimation result can provide a scientific basis for communication quality partition and system optimization of an urban environment.

The invention discloses a power distribution network toughness improvement method based on an energy internet under mobile energy storage space-time optimization scheduling, and the method comprises the steps: building a double-layer scheduling model which comprises an upper-layer model and a lower-layer model; the upper layer model adopts a distributed robust optimization method, considers the uncertainty of fault nodes in extreme weather and the influence of traffic network passing time, and optimizes the configuration number and position scheme of mobile energy storage; the lower layer model constructs a multi-source cooperative recovery mixed integer quadratic cone programming model based on dynamic scheduling and time sequence output characteristics of mobile energy storage, an electric vehicle and a diesel generator, and solves an optimal load reduction scheme and an important load power recovery state; constructing a fault probability model based on the double-layer scheduling model; the method comprises the following steps: performing simulation analysis by improving an IEEE-33 node power distribution network, and verifying the effectiveness of the method; according to the method, the key problems of dynamic adaptive scheduling and multi-temporal-spatial-scale collaborative optimization are solved, the potential of mobile energy storage and multi-source collaboration is released, and a high-toughness power distribution network system can be constructed.

The invention discloses an electric power-traffic coupling network vulnerability identification method and system. The method comprises the following steps: S1, coupling an electric power network and a traffic network to form a graph model; the graph model comprises a node set and an edge set; constructing an adjacent matrix and a node feature matrix based on the node set and the edge set; s2, inputting the adjacent matrix and the node feature matrix into a graph convolutional neural network model, performing feature extraction model training through a semi-supervised learning mechanism, and outputting an updated node feature matrix; and S3, learning an optimal strategy by using a deep Q network, and identifying a fragile node sequence through interaction with the environment. According to the method, a traditional single network evaluation mode is broken through, accurate quantification of the cross-network coupling effect is achieved, complex topological information can be captured, intelligent dynamic recognition is achieved, dependence on artificial experience is reduced, decision visualization can be supported, the system practicability is high, and the method can be applied to power-traffic coupling network catastrophe scenes.

The invention belongs to the technical field of optimal planning and site selection of electric vehicle charging stations, and particularly relates to an electric vehicle charging station planning and site selection method, which comprises the following steps: S1, constructing an uncertainty set containing road and node traffic capacity loss; constructing a vehicle starting point-terminal point travel scene set in combination with the historical travel data of the vehicle; s2, constructing a network equalization model considering the arrival time fairness of the electric vehicle users according to the passing time cost difference of the electric vehicle users on the traffic road when the electric vehicle users go to the charging station from the starting point so as to solve regional traffic flow distribution; and S3, based on the traffic flow distribution in the S2, constructing a charging station planning model considering the user arrival time fairness, and determining an optimal site selection and capacity configuration scheme of the charging station. According to the method, the time fairness of the electric vehicle user arriving at the charging station can be ensured while the traffic uncertainty of the traffic network road section is effectively dealt with, and the method has a relatively good market application prospect.

The invention discloses an urban traffic flow prediction method based on multiple time-space characteristics of a road network, and the method comprises the following steps: obtaining historical traffic flow information and road topology connection structure information of a related region, and constructing a corresponding data set according to a prediction demand; historical traffic flow information is preprocessed, a spatial-temporal feature tensor is constructed, and a road adjacent matrix is constructed; inputting the spatial-temporal feature tensor into a wavelet decomposition unit capable of being trained and promoted for hierarchical decomposition to obtain a low-frequency component and a high-frequency component; hierarchical decomposition is carried out on original traffic flow time series data through a wavelet decomposition unit, features of different frequency components are respectively extracted and modeled, so that low-frequency components fully learn global trend and periodic change of traffic flow, and high-frequency components emphatically describe sudden and local dynamic change in a traffic network.

The invention discloses an expressway traffic flow prediction method based on a spatial-temporal clustering generative adversarial network under an emergency, belongs to the field of expressway traffic flow prediction, and remarkably improves the precision of expressway traffic flow prediction under the emergency. The method comprises the following steps: step 1, acquiring traffic flow data from a traffic detection system, and constructing a traffic network graph structure; 2, integrating geographical location information, traffic function similarity and delay propagation characteristics to construct a comprehensive clustering algorithm, and realizing the comprehensive clustering algorithm; 3, designing a generator network based on a space-time attention encoder and a discriminator network structure fusing a bidirectional long-short-term memory network and graph convolution based on the clustering result and the feature representation in the step 2, and constructing a complete prediction model; 4, performing prediction model training based on the composite loss function; and step 5, applying the trained prediction model to real-time data prediction to realize traffic flow prediction under emergencies.

The invention discloses a pre-allocation method and system for a pre-disaster distributed power supply of a power grid in a flood scene, and belongs to the technical field of power system planning and disaster defense, and the method comprises the steps: constructing a power distribution network multi-scene model under the influence of a flood disaster, and generating a disaster scene set; establishing an upper-layer optimization model with the goal of minimizing the load loss amount and the distributed power supply configuration cost, and setting the operation constraint of the power distribution network by adopting a Distflow power flow model; performing iterative solution on the upper-layer optimization model by adopting a bald eagle optimization algorithm, establishing a lower-layer scheduling optimization model in combination with a traffic network ponding model, and optimizing a scheduling path of the distributed power supply; and outputting a comprehensive optimal pre-disaster pre-allocation scheme including the optimal layout position, the capacity configuration and the scheduling path of the distributed power supply. According to the method, the power supply continuity, the recovery efficiency and the disaster prevention toughness of multiple types of power grids in the post-disaster stage can be effectively improved, the guarantee capability of key loads is enhanced, and the method has good global search capability and engineering adaptability.

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 relates to the technical field of passenger flow prediction, and discloses a multi-graph convolutional network inbound and outbound passenger flow prediction method based on space-time marking, and the method comprises the steps: obtaining passenger flow data; performing space-time marking coding on the passenger flow data by using a pre-generated self-learning space-time marking vector to obtain a passenger flow sequence after space-time marking coding; constructing a static topological graph, a passenger flow similar graph and a function similar graph of the traffic network, performing multi-graph information fusion by adopting an adaptive weighted summation method, and performing graph convolution operation on a fused graph structure and the passenger flow sequence after space-time marking and coding to obtain passenger flow space dependence characteristics; and capturing a long and short term passenger flow time sequence mode for passenger flow space dependence characteristics by adopting a gating circulation unit, and performing dynamic weight distribution of time nodes in combination with an attention mechanism to obtain an in-and-out passenger flow prediction result. The method can improve the recognition capability of the historical passenger flow data mode, and improves the prediction precision of the future passenger flow change.

The invention discloses a traffic flow prediction method for a double-layer multi-scale dynamic graph convolutional network, and relates to the technical field of traffic, and the method comprises the following steps: constructing a double-layer structure of a traffic network, which comprises a node layer composed of traffic sensor nodes and a region layer formed by clustering nodes in the node layer, node layer traffic flow data and area layer traffic flow data are extracted; respectively mapping the node layer traffic flow data and the region layer traffic flow data to a potential space to obtain a node layer initial hidden state and a region layer initial hidden state; inputting the initial hidden state of the node layer and the initial hidden state of the region layer into a sequence containing at least one space-time module for processing so as to capture a dynamic space-time dependency relationship; the method has the effect of providing more reliable decision support for traffic management and travel planning.

The invention discloses a fault traffic electrification network emergency support strategy and a fault traffic electrification network emergency support system based on a mixed graph attention (Graph Attention Network, GAT)-deep double Q learning (DDQN) architecture, and aims to enhance the anti-interference capability and the recovery capability of a traffic electrification network when the traffic electrification network faces faults and emergency situations. Firstly, coupling characteristics and operation requirements of a traffic network and a power distribution network are comprehensively considered, a traffic electrification network emergency support framework based on a GAT-DDQN architecture is proposed, and a double-layer multi-objective optimization model is established. And secondly, learning fault topological structure information based on the graph attention network, constructing a double-layer finite Markov Decision Process (FMMP) model, modeling a dynamic interaction process of the traffic network and the power distribution network into a state, action and reward decision sequence, and realizing fault emergency support of the power distribution network and the traffic network. And finally, proposing a DDQN solving algorithm based on an attention mechanism and experience playback optimization, and outputting an optimal traffic electrification network emergency support strategy by strengthening key node feature learning and historical experience utilization efficiency. And finally, constructing a traffic electrification network emergency support system which comprises a data acquisition and processing module, a feature extraction module, an algorithm solving module and a visualization module.

The invention discloses a power-traffic network charging load prediction method and system, and a storage medium. The method comprises the following steps: constructing an electric vehicle basic model, a road network basic model, a charging station basic model and a power distribution network basic model; generating dynamic traffic flow data through a generative adversarial network based on the basic model and the historical traffic data; constructing a traffic travel demand matrix based on a start-end point analysis method, and simulating the whole process of charging decision, queuing waiting and charging service of the electric vehicle in the charging station by using a queuing theory algorithm in combination with the dynamic traffic flow data and the basic model; constructing a space-time diagram attention network model, taking charging demand simulation data, a road network basic model and a charging station basic model as input, obtaining a space-time coupling relationship between a traffic network state and a charging demand through a multi-head diagram attention mechanism, and predicting to obtain space-time distribution of an electric vehicle charging load in a certain period of time in the future. And accurate prediction of the time-space part of the charging load of the electric vehicle is realized.