894 results about "Traffic network" patented technology

The invention relates to the technical field of intelligent traffic prediction, in particular to a time-space correlation traffic flow prediction method based on deep learning, which comprises the following steps: S1, collecting traffic flow data and related external environment data in real time, and establishing an initial traffic network topology structure; s2, calculating the dynamic weight of each node; s3, dynamically adjusting the weight of each edge in the traffic network according to the node weight change in the S2 so as to update the topological structure of the traffic network; s4, performing feature extraction on the spatial dependency relationship among the traffic nodes, and extracting time evolution features of the traffic flow data at the same time; s5, constructing an adaptive space-time fusion model; s6, traffic flow prediction is carried out, and adaptive space-time fusion model parameters are optimized; according to the method, through dynamic fusion of the spatial-temporal characteristics and adaptive adjustment of the model parameters, the precision and real-time performance of traffic flow prediction are effectively improved, and the adaptability and generalization ability of the model in a complex dynamic traffic scene are enhanced.

The invention belongs to the field of intelligent traffic systems, and relates to a multi-mode space-time traffic flow modeling method supporting large-scale road network real-time prediction, and the method comprises the steps: firstly designing a space-time prediction framework facing a dynamic traffic network, and then carrying out the training to obtain a final prediction model; the space-time prediction framework comprises a data embedding layer, a space-time coding module and a deep modeling and output module based on an expert hybrid mechanism; the data embedding layer comprises two parallel channels of time embedding and spectral domain space embedding and a time-space data fusion module; the space-time coding module comprises a block-level sparse time attention module, a space attention-message passing module and a weighted fusion layer which are parallel; the deep modeling and output module based on the expert hybrid mechanism comprises an MoE dynamic expert modeling module, a full-connection mapping module, a jump connection layer and an output layer; according to the design, the response speed, the prediction precision and the cross-regional adaptive capacity of the model in a high-heterogeneity scene are improved.

An intelligent traffic management device for optimizing traffic flow at one or more intersections using reinforcement learning, comprising: a central control unit housed in a weatherproof enclosure and mounted on a structural support near an intersection; a sensor interface operatively connected to a variety of multimodal sensors, including at least radar-based vehicle detectors, inductive loop sensors embedded in the road surface, infrared pedestrian detection detectors, and high-resolution optical imaging modules; a powerful embedded computing unit consisting of a multi-core processor and a GPU configured for on-device inference; a reinforcement learning engine embedded in the computing unit, comprising a deep reinforcement learning model trained to map environmental states defined by traffic-related sensor inputs to optimized action policies for traffic light control; a signal actuation module electrically connected to traffic signal controllers, with action policies outputting signal phase durations and transitions in real time; a communications module supporting wireless connectivity and configured for device-to-device communication and cloud synchronization; a policy synchronization module configured to perform federated learning-based updates of model parameters by exchanging gradient weights or distilled traffic state vectors with neighboring intelligent traffic management devices deployed in a traffic network; where the reinforcement learning engine continuously observes traffic state vectors consisting of vehicle counts, average speeds, queue lengths, and pedestrian activity, and dynamically updates the policy to maximize cumulative traffic throughput and minimize average delay.

The invention provides a non-stationary traffic prediction method based on wave flow decomposition and time delay perception. The method mainly comprises the following steps: constructing a traffic network diagram and inputting historical traffic flow data; constructing a decoupling flow layer, and decoupling the original flow into a wave component and a flow component; constructing a time gating convolution module, and capturing short-time dependency; constructing a space-time causal chain of time-delay perception directed graph attention and processing wave components; constructing an adaptive graph convolutional network, and processing global steady-state features of flow components; constructing a time gating convolution module, and capturing long-term time dependence; constructing an adaptive event fusion module; constructing a full connection layer; and outputting the predicted traffic flow data. The method overcomes the limitation of a traditional method in the aspects of prediction accuracy and coping with a complex traffic network, and effectively solves the problems that a traditional traffic flow prediction method is insufficient in prediction accuracy in an intelligent traffic system, cannot reflect the influence of the traffic network and the like.

The invention provides a multi-type mobile emergency resource optimization scheduling and power distribution network toughness improvement strategy based on a data-model hybrid driving method, and the strategy starts from the dynamic interaction relationship between a mobile energy storage vehicle and an emergency repair team, and coordinates the mobile energy storage vehicle and the emergency repair team to carry out load recovery and road maintenance. Scheduling an emergency repair team to repair the damaged road of the traffic network under the condition of considering that the duration required for repairing the road is uncertain; scheduling the mobile energy storage vehicle to carry out load recovery on the power distribution network nodes under the condition of considering the dynamic change of the road state of the traffic network; the data driving part is supported by a graph neural network multi-agent reinforcement learning algorithm GATD3QN based on a graph attention mechanism so as to solve an optimal multi-type mobile emergency resource routing behavior strategy; and the model driving part constructs the scheduling model of the mobile energy storage vehicle and the emergency repair team, the power distribution network reconstruction model and the power distribution network optimal power flow model into an MISOCP model and solves an optimal multi-type mobile emergency resource scheduling behavior strategy.

The invention discloses an industrial land intelligent site selection method based on spatial big data and an artificial intelligence big model. The method comprises the following steps: 1) obtaining a feature matrix containing spatial topology constraints and economic association rules; 2) extracting key analysis elements in user site selection requirements; 3) inputting the spatial key analysis elements into CNN convolutional neural network branches to obtain spatial features; the policy text type key analysis elements are input into a Transform branch, and policy features are obtained; inputting the industrial chain type key analysis elements into GNN graph neural network branches to obtain industrial chain features; 4) generating an industrial land analysis conclusion; and 5) generating a comparison report, a site selection proposal, a risk avoidance strategy and an emergency plan of each feasible scheme. According to the invention, through fusion of multi-source spatial data (industrial layout, traffic network and regional assessment) and an artificial intelligence large model, dynamic planning constraint matching, full-life-cycle risk assessment and multi-objective optimization site selection are realized.

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

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

The invention discloses an electric vehicle charging station layout optimization configuration method and system, and the method comprises the following steps: obtaining the traffic network information and power distribution network information of a planning target area, and determining the charging demand number of electric vehicles and the charging load demand distribution condition; according to the electric vehicle charging load demand distribution condition, determining the service range of each charging station, and constructing a proposed charging station capacity determination model; an upper-layer charging station site selection model is constructed, and the optimal site of each charging station is determined; establishing a lower-layer mixed integer programming model, and determining the configuration number of various charging piles of each charging station; solving the double-layer locating and sizing planning configuration model by using a hybrid genetic simulated annealing algorithm and an analytic hierarchy process to obtain an optimal locating and sizing scheme of the charging station; according to the method, the attraction of the charging station to the user and different types of charging pile configuration schemes are comprehensively considered, a bilevel planning configuration model is established, an optimal solution set is solved by using a hybrid genetic simulated annealing algorithm, and finally an optimal scheme is determined through an analytic hierarchy process.

The invention relates to the technical field of traffic emergency evaluation, and discloses a comprehensive traffic network toughness evaluation method based on multiple indexes, which comprises the following steps of: in a pre-stage, selecting a plurality of key indexes for evaluating the traffic network toughness; an in-event stage: when an emergency occurs, acquiring real-time traffic data based on the evaluation model; and a post-event stage: after the emergency event is ended, analyzing the influence of the event on the traffic network, and evaluating the recovery capability of the traffic network based on the evaluation model in combination with the restoration time of the facility, the traffic flow recovery condition and the emergency resource allocation record. A topological structure index, a traffic flow index, a facility reliability index and an emergency response capability index are adopted, the robustness of the traffic network under different dimensions can be comprehensively evaluated, through multi-dimensional quantitative analysis, the impact resistance, the recovery capability and the emergency management capability of the network can be comprehensively evaluated, and the evaluation accuracy is improved.

The invention discloses an electric power-traffic collaborative balance scheduling method and system for electric vehicle charging, and belongs to the technical field of electric power systems, and the method comprises the steps: obtaining the travel charging demand information, charging station information and traffic network information of each electric vehicle in real time; constructing a power-traffic coupling operation network model which comprises a power network model, a traffic network model and a coupling network model; using a multi-agent multi-layer graph convolutional learning algorithm to perform power-traffic collaborative balance scheduling, using a preposed graph convolutional network to perform coupling feature representation and environmental state perception on the coupling network model to obtain the coupling feature of each node, and using a charging station as an agent to perform power-traffic collaborative balance scheduling; and inputting the coupling characteristics of the node where the charging station is located into the state space as a part of observed quantity of the intelligent agent, and outputting a charging scheduling decision of each electric vehicle through a target strategy network and a target value network in the intelligent agent. The electric vehicle charging routing behavior can be optimized, and real-time balanced scheduling of the electric power-traffic network is realized.

The invention discloses a livestock epidemic disease early warning method and system based on big data analysis, and the method comprises the steps: generating a preliminary epidemic situation diffusion path through employing the farm distribution and traffic network data of a geographic information system in combination with a space-time propagation model, and carrying out the dynamic adjustment according to the real-time updated data; predicting a high-risk area by adopting a multi-source data fusion early warning model; the strain mutation risk is judged by analyzing the relevance between pathogen genomes and environmental parameters; deploying a deep learning model at an edge computing node, analyzing animal husbandry behaviors and voiceprint data in real time, and evaluating the animal husbandry health state; and integrating mutation risk analysis and animal husbandry health assessment results to generate a comprehensive epidemic situation early warning report. Through multi-dimensional data analysis and edge intelligent calculation, accurate early warning of animal epidemic situations is realized, and a scientific basis is provided for epidemic situation prevention and control.

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 provides an intelligent urban municipal planning method and system based on geographic information, and relates to the technical field of data processing, and the method comprises the steps: obtaining urban spatial data of different data sources, including terrain, land utilization, building distribution, traffic network, population distribution and environment index data; and analyzing the urban spatial data, including spatial overlay analysis, buffer analysis and network analysis, and identifying key problems and potential risks in urban municipal planning to obtain a data analysis result. The urban space data can be comprehensively and accurately analyzed, the optimized municipal planning scheme is automatically generated and updated and adjusted in real time, and the urban traffic fluency, the environment quality and the public facility utilization efficiency are improved.

The invention provides a traffic flow prediction method based on a dual hypergraph neural network. The method comprises the following steps: preprocessing traffic data collected by a sensor in a traffic road network in a certain time period; forming a data set by the preprocessed traffic data, and dividing the data set into a training set, a test set and a verification set; respectively constructing a traffic flow graph and a dual hypergraph according to the topological graph of the traffic network; constructing a traffic flow prediction model based on the space-time dual hypergraph convolution layer, wherein the traffic flow prediction model comprises an input layer, the space-time dual hypergraph convolution layer and an output layer which are connected in sequence; inputting the preprocessed traffic data in the training set into a traffic flow prediction model for training; testing the trained traffic flow prediction model by using the preprocessed traffic data in the test set to obtain a final traffic flow prediction model; and inputting the traffic data preprocessed in the verification set into the final traffic flow prediction model to obtain the predicted traffic flow. According to the method, multi-source data are fused and external knowledge is utilized, so that the spatial-temporal correlation in the data can be better understood and utilized, and the accuracy of a traffic prediction model is improved.

The invention discloses an electric vehicle charging path planning method based on a dynamic energy consumption model and road network information. According to the method, the influence of different temperatures on the battery capacity is analyzed according to the charging and discharging experimental data of the lithium battery, the power consumption generated in the driving process of the electric vehicle is analyzed, and a refined air conditioner energy consumption model and a mileage energy consumption model are established. Based on an improved A * algorithm, a method for searching a reachable path with the minimum energy consumption according to the residual electric quantity of a vehicle-mounted battery, the position of a charging station and the real-time road condition is established. The A * algorithm of the model solving algorithm serves as an optimal priority search algorithm in graph theory search methods, and the optimality requirement can be met by reasonably designing a function; and meanwhile, aiming at an urban road network, a heuristic A * algorithm is adopted to be matched with the path optimization solution of a large-scale traffic network in a road-electricity coupling scene. The effectiveness of the provided model in the charging path decision is verified by respectively comparing three targets of time optimization, path optimization and comprehensive energy consumption optimization provided by the invention.

The invention discloses a highway toll station flow prediction method and system, and relates to the field of intelligent traffic management and big data analysis, and the method comprises the steps: collecting multi-source heterogeneous traffic data, carrying out the preprocessing, carrying out the feature extraction of the preprocessed multi-source heterogeneous traffic data, mapping the extracted features into a common feature space, and carrying out the prediction of the flow of a highway toll station. The method comprises the steps of performing feature fusion, generating a comprehensive feature vector, obtaining a traffic network diagram based on the comprehensive feature vector, constructing an STGATwE model according to the traffic network diagram, training the STGATwE model by using the comprehensive feature vector, adjusting parameters of the STGATwE model, generating a trained STGATwE model, inputting multi-source heterogeneous traffic data collected in real time into the trained STGATwE model, and obtaining a flow prediction result. By collecting multi-source heterogeneous traffic data and introducing an external influence factor mapping function, the data dimension is enriched, and the sensitivity to external factors is enhanced.

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 provides a charging station capacity optimal planning method considering space-time charging scheduling of an electric vehicle. The method comprises the following steps: carrying out functional region division on a city region with a traffic-power coupling system; counting the number of nodes, node loads and branch impedance in the power distribution network, and road types, road lengths and the number of roads in the traffic network; the method comprises the following steps: acquiring data such as a typical daily road travel time index, a power load and electric vehicle behaviors, and performing ordered charging intention evaluation on electric vehicle users in a region to obtain a charging mode of each electric vehicle; and taking the charging facility capacity of the electric vehicle charging station as an optimization variable, taking the minimum comprehensive cost as a target, establishing a target function, combining the operation constraint and the time-space constraint, establishing an optimization model, and solving to obtain a planning scheme. According to the method, the influence of the charging behavior of the electric vehicle on the running state of the traffic and power system can be deeply excavated, meanwhile, the benefit requirements of multiple related subjects are considered, and remarkable economic benefits are achieved.

The invention provides a vehicle trajectory prediction method and device, electronic equipment and a storage medium, and belongs to the technical field of artificial intelligence. Local trajectory features of each vehicle node are extracted from historical moving trajectory data of each vehicle node; performing global feature extraction in combination with the historical moving trajectory data corresponding to each vehicle node to obtain global trajectory features, and performing spatial feature extraction on the motif matrix, the corresponding local trajectory features and the global trajectory features for each vehicle node to obtain target spatial features of the vehicle node; performing time feature extraction on the motif matrix and the global trajectory feature to obtain a target time feature, and combining the historical moving trajectory data, the local trajectory feature, the target spatial feature and the target time feature corresponding to each vehicle node through a preset graph neural network to obtain a target time feature; and predicting the predicted moving trajectory of the target vehicle node in the traffic network graph structure. The accuracy of vehicle trajectory prediction can be improved.

The invention discloses a regional traffic network dynamic risk assessment method under sudden earthquake influence, which belongs to the technical field of earthquake disaster assessment and comprises the steps of collecting data and preprocessing, constructing a traffic network model, constructing an earthquake vulnerability model and constructing a risk dynamic assessment system. According to the method, seismic parameters, social economy, a regional traffic network and real-time detection data after an earthquake are comprehensively considered when the earthquake occurs, the risk of the regional traffic network under the earthquake can be evaluated more comprehensively, a Monte Carlo simulation method is applied, an earthquake vulnerability model is combined, a damage scene of the earthquake to the traffic network is simulated, and the risk of the regional traffic network under the earthquake can be evaluated more comprehensively. Evaluating the damage probability and the function loss of the traffic facilities; calculating the connectivity of the traffic network model, and dynamically calculating the performance change of the traffic network after the earthquake based on the connectivity index and weight of the traffic network; and evaluating the toughness of the traffic network according to a network performance recovery curve under the recovery strategy, so that the performance change of the traffic network in the earthquake can be dynamically reflected in real time.

The invention provides a traffic flow prediction and signal control optimization method based on a space-time diagram convolutional network (STGCN), a long short-term memory network (LSTM) and a near-end strategy optimization (PPO) algorithm, and is suitable for intelligent traffic management in a smart city. According to the method, through combination of deep learning and reinforcement learning, urban traffic flow is accurately predicted, traffic signal lamp timing is optimized, urban traffic congestion is reduced, and road utilization efficiency is improved. The method comprises the following steps: firstly, capturing spatial dependence among different road sections through an STGCN model, and processing time dynamic change of traffic flow by using an LSTM model; and then, optimizing signal lamp timing based on a PPO algorithm, and dynamically adjusting a signal time sequence, thereby reducing the vehicle waiting time to the maximum extent and improving the traffic efficiency of the whole traffic flow. In addition, the system integrates external factors such as weather, holidays and festivals, and the adaptive capacity of the model in a complex traffic environment is enhanced. Experiments show that by using the method of the invention, the accuracy of traffic flow prediction is significantly improved, the average waiting time of vehicles is reduced, the traffic volume is increased, and the efficiency of a traffic system is effectively improved. The system is suitable for a large-scale urban traffic network, has good expansibility and real-time performance, and provides powerful technical support for intelligent urban traffic management.

The invention provides a power distribution network and traffic network cooperative operation method considering multi-subject benefit coupling, and belongs to the technical field of network system control, and the method comprises the following steps: S1, constructing a three-layer power traffic coupling network cooperative operation model, comprising a lower-layer traffic network user balance model, a middle-layer charging network operator optimal pricing model and an upper-layer power distribution network second-order cone relaxation optimal power flow model; s2, converting the lower-layer traffic network user equilibrium model into a group of constraints by applying a KKT condition, thereby converting the three-layer electric power traffic coupling network collaborative operation model into a double-layer model; and S3, solving the double-layer model by using a CCG algorithm. According to the power distribution network and traffic network cooperative operation method considering multi-agent benefit coupling, the system operation efficiency is optimized, the cooperative interaction among the operation agents is ensured, and the overall cooperative effect of the power distribution network and the traffic network is improved.

The invention discloses an urban cross-domain multi-mode network space modeling method, and belongs to the technical field of traffic simulation. In order to solve the problem that urban traffic cross-domain spatial modeling is complex, the method comprises the steps that road, hub and track BIM models are classified, traffic semantics in different BIM objects in the BIM models are recognized, and traffic elements are extracted; geometric element abstraction is carried out, spatial topology connection relations and attribute information of geometric elements are defined, and single-mode traffic network model modeling is carried out; coordinate conversion is carried out, and the relative position coordinates of the single-mode traffic network model are converted into 1984 world geodetic surveying geographic coordinates; a walking traffic network is used as an intermediate network, a walking traffic mode and transfer rules of roads, rails, buses and hubs are used as strategies, network fusion is achieved through a map matching algorithm, and a multi-mode network space is modeled; and carrying out topological structure verification on the obtained multi-mode network space. According to the invention, full-link calculation from design to simulation is 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 provides a bridge group maintenance priority dynamic decision-making method and device based on reinforcement learning, and relates to the technical field of bridge intelligent maintenance. The method comprises the following steps: constructing a topological structure of a bridge network and a road; defining a state space, a maintenance action space and a state transition matrix of the bridge; defining a reliability index corresponding to the state of the bridge, and designing a comprehensive reward function based on maintenance cost, asset risk and traffic network capacity loss risk based on the topological structure; constructing a bridge maintenance decision problem; the method comprises the following steps of: describing a bridge maintenance decision problem as a Markov decision process, establishing a pointer network strategy model by adopting a pointer network, and training the pointer network strategy model by adopting an Actor-Critic algorithm to obtain a maintenance decision model based on reinforcement learning; and training the maintenance decision model based on reinforcement learning until convergence, and outputting a bridge maintenance action sequence under limited constraints. By adopting the method, the limitation problem of traditional single bridge assessment can be solved.

The invention relates to the technical field of intelligent traffic, in particular to a traffic network robustness evaluation method and system based on multi-source data fusion, and the method specifically comprises the following steps: obtaining a plane axis diagram according to an obtained research map and road data, and marking the obtained data information; importing the plane axis diagram into the depthmapX to construct a traffic road network topology model, and performing spatial syntactic analysis; exporting a traffic road network topology model and a space syntactic analysis result, accessing real-time data in the acquired data, and mapping the traffic road network topology model to a road network space coordinate system through a space-time alignment algorithm; constructing a nonlinear coupling model to calculate the robustness weight of each road section, and optimizing parameters in the model through a loss function and a gradient descent method; and combining the robustness weight of each road section after parameter optimization with the disaster probability factor, and outputting a comprehensive evaluation result. The method solves the problems of data isolation and evaluation lag of a traditional method, and is suitable for urban road network optimization and emergency management.

The invention relates to the field of smart city management, and provides a smart city operation data management system and method, which adopts a deep learning-based data processing and analysis technology to understand the time sequence change of the traffic flow of an intersection by performing time sequence analysis on the time queue data of the traffic flow of a target management intersection. And the time configuration of the traffic signal lamp is dynamically adjusted based on the time sequence change of the intersection traffic flow. Therefore, the traffic signal lamp time can be scientifically and reasonably configured according to the actual traffic flow, and the operation effect of an urban traffic network can be optimized.

The invention discloses a traffic flow prediction method based on feature embedding and a space-time multi-head self-attention mechanism, and relates to the technical field of intelligent traffic and flow prediction, and the method comprises the steps: obtaining historical traffic flow sequences of N sensors at the first T moments in a traffic network; taking historical traffic flow sequences of N sensors in a traffic network at the first T moments as input, and generating features including time and space through a feature embedding layer; the generated features including time and space are input into a stacked space-time coding layer, calculation is carried out through a decoupled time multi-head self-attention mechanism and a space multi-head self-attention mechanism, new time features and space features are obtained, the time features and the space features are fused, and space-time features are obtained; inputting the spatial-temporal features into an MLP module, and further extracting high-level features through multi-layer MLP; and outputting the high-level features through a regression layer to obtain a prediction result. According to the method, the problem that the traditional prediction model is difficult to process the time-space complexity at the same time is effectively solved by utilizing a decoupling type time and space multi-head self-attention mechanism, and the accuracy of traffic flow prediction is improved.