1595 results about "Urban road" patented technology

The invention belongs to the technical field of charging station planning, and discloses a charging station planning method and system based on an automobile charging demand, and the method comprises the steps: building a space-time charging demand distribution map through multi-source data collection and fusion processing, carrying out the seasonal change analysis and future demand prediction, and obtaining a dynamic charging demand prediction model; site layout optimization under a multi-constraint condition is performed by combining an urban road network structure and traffic flow data to form a preliminary charging station layout scheme, and power grid load capacity evaluation and renewable energy access analysis are implemented to establish an energy collaborative supply guarantee system. And designing a peak-valley period charging price dynamic adjustment and appointment queuing mechanism to form an intelligent scheduling control strategy, and finally obtaining a diversified charging facility configuration scheme through different charging power requirements and vehicle type suitability evaluation. According to the method, the problems of inaccurate demand prediction, unreasonable layout, uneven resource allocation and the like in traditional planning are solved, and accurate matching of charging resources and user demands is realized.

The invention discloses a road traffic flow prediction method based on a space-time mixed attention network, and the method breaks through the limitation of a conventional time sequence model and a single deep learning architecture based on the systematic analysis of urban road traffic flow space-time heterogeneity, periodic non-stationarity and road network topological relevance, constructs the space-time mixed attention network, and achieves the prediction of road traffic flow. Spatial heterogeneous correlation of road network nodes is captured through a graph convolution network, dynamic time sequence evolution characteristics of traffic flow are modeled by adopting a hybrid architecture, a residual attention mechanism is introduced to realize layer-by-layer refining of multi-scale spatio-temporal characteristics, and the overall architecture of the method has remarkable advantages in the aspects of spatial topology modeling and time dynamic capture compared with a traditional model. Feature decoupling learning is carried out on multi-source heterogeneous data such as weather and events, adaptive integration of environment sensitive features is realized through a parameterized gating fusion strategy, and the prediction error fluctuation amplitude in an extreme weather scene is reduced by 34.8%.

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

The invention 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 an urban road mobile source intelligent monitoring method and system based on multi-source data coupling, and relates to the technical field of environment monitoring and intelligent traffic. Utilizing a machine learning algorithm to construct a pollutant emission, carbon emission and energy consumption prediction model; acquiring vehicle inventory data in the city, and calculating the pollutant emission, energy consumption and carbon emission of the whole city; the method comprises the following steps of: constructing a dynamic distribution diagram of automobile emission in a city by using real-time position data of vehicles and urban road network information, introducing an atmospheric diffusion model, simulating pollutant migration by combining urban geographic information and environmental data, comparing and verifying a simulated migration result with actually measured data of a national control site, and optimizing parameters of the atmospheric diffusion model; and storing the result into a real-time database, and displaying the pollutant distribution, emission, energy consumption and carbon emission of the urban road network in real time through a visual interface. According to the invention, the temporal-spatial resolution and prediction precision of data are effectively improved.

The invention discloses a method and a system for evaluating the running risk of an automatic driving taxi, and relates to the field of intelligent traffic and automatic driving. Aiming at the problems of insufficient dynamic traffic environment adaptability, rigidity of space decision, lack of vehicle operation rule compatibility and the like in the prior art, the evaluation method comprises the following steps: dynamically dividing an urban road network into hexagonal honeycomb units, adjusting the honeycomb radius according to a real-time travel demand, and selecting key nodes; accessing traffic flow density, conflict event number, communication delay and environment parameters in real time to construct a key node risk index; a hidden Markov chain model is adopted to simulate behavior response of an automatic driving taxi at a key node, an S1 safety state and an S2 risk state are defined, and a Viterbi algorithm is combined to deduce a hidden state probability; and finally, comprehensively judging the risk level according to the risk index and the state probability. According to the method, through multi-source data fusion and scene behavior modeling, dynamic evaluation and control of the operation risk of the automatic driving taxi 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 relates to the technical field of urban road construction, in particular to an intelligent flying dust monitoring and dust falling control system for urban road construction, which comprises a flying dust monitoring module, a dust falling control module, a dust falling control module and a dust falling control module, the data fusion processing center is used for processing the monitoring data and generating a dust fall control instruction; the intelligent dust falling execution module is used for triggering a dust falling device according to the instruction; the cloud platform and the mobile terminal are used for storing the monitoring data, visually displaying the flying dust thermodynamic diagram and pushing standard exceeding early warning information to management personnel; data are collected in real time through the flying dust monitoring module, a flying dust pollution source and the diffusion trend of the flying dust pollution source are recognized in combination with the dynamic analysis and prediction capacity of the data fusion processing center, the intelligent dust falling execution module is used for triggering dust falling measures, and meanwhile remote monitoring and early warning are achieved through the cloud platform and the mobile terminal. The problem of response lag in a traditional method is solved, and the control efficiency is improved.

The invention relates to the technical field of optical measurement and geometric position measurement, in particular to a remote multipoint monitoring system and method for roadbed surface settlement, and the system comprises a plurality of distributed monitoring nodes and a remote data platform; the monitoring node comprises a heterogeneous sensor group and an edge processing module which is connected with the sensor group and is used for carrying out local preprocessing and fusion on multi-source data; the wireless communication module is used for sending the data to a remote data platform; the power supply and maintenance module provides electric energy and supports node cleaning and environment adaptation; the remote data platform comprises a data receiving and storage unit, a data fusion and analysis unit, an early warning unit and a visual user interface. According to the invention, a multi-sensor fusion intelligent monitoring network is constructed, all-weather and high-precision remote monitoring of urban road subgrade settlement and associated crack deformation thereof is realized, settlement trend prediction and early warning are provided through AI analysis, and the initiative and safety guarantee level of urban road maintenance are improved.

The invention relates to the field of urban road modeling, in particular to an aerial photography data processing method and system for urban road modeling. The invention discloses an aerial photography data processing system for urban road modeling. The aerial photography data processing system comprises an aerial photography video acquisition module, a road modeling module, a photogrammetry module, a spatial registration module and a rendering synthesis module. According to the invention, on the basis of a spatial calibration mechanism of the original aerial image and the BIM model, redundant processes such as point cloud acquisition, data fusion and manual correction are eliminated, and linear processing from data acquisition to dynamic result output is realized; according to the architecture, the hardware resource dependency and the operation complexity are greatly reduced, and designers are endowed with the capability of completing large-scale scene visualization processing in a conventional engineering environment; according to the method, a transmission chain from original data to decision support is remarkably shortened, and important engineering scheme deduction and dynamic review have unprecedented timeliness.

The invention discloses a road network abnormal event identification and risk evaluation early warning method based on vibration perception, and relates to the technical field of urban road safety monitoring. Comprising the steps of collecting road vibration data of a road network through a sensor; preprocessing the road vibration data through Kalman filtering and multi-dimensional feature extraction; abnormal event recognition is carried out on the processed road vibration data through a differentiation algorithm, wherein abnormal events comprise vehicle overload, illegal construction and road structure damage; and carrying out risk scoring on the abnormal events through dynamic weight distribution, dividing the risk scores according to the risk levels, and carrying out graded response. According to the invention, through differential algorithm design, a mixed model architecture and a hierarchical response mechanism are adopted, abnormal event identification, risk level quantitative evaluation and differential early warning processing are realized, the safety state of an urban road network is monitored in real time, and early warning information is provided.

The invention discloses a hybrid electric vehicle predictive energy management system based on multi-layer cooperative control, and aims to improve the vehicle performance and energy efficiency. According to the method, a hierarchical cooperative control architecture is adopted, and the reinforcement learning, vehicle speed multi-step prediction and model prediction control technologies are combined, so that joint optimization control over vehicle acceleration and power distribution is achieved. The system obtains the vehicle running state and the surrounding environment information in real time through the state sensing module, and determines the expected acceleration by using a strategy network trained by a PPO algorithm. In addition, the future vehicle speed is predicted in combination with an XGBoost multi-step regression model, the optimization module is designed through an MPC strategy, oil consumption, the battery state and driving smoothness are comprehensively considered, and therefore efficient management of the power system is achieved. According to the scheme, the obvious energy-saving advantage is achieved, the driving smoothness and comfort are improved, the good self-adaptive capacity and a real-time feedback mechanism are achieved, and the method is suitable for the complex urban road traffic environment.

The invention provides an urban road ponding monitoring and drainage system. The urban road ponding monitoring and drainage system comprises a monitoring module which collects ponding depth, water velocity, rainfall intensity and field image data in real time; the initialization configuration module sets parameters such as an accumulated water depth threshold value; the data transmission module encrypts and transmits data to the central control module; the model algorithm optimization module constructs a three-dimensional ponding diffusion model and outputs a predicted ponding depth and an optimized drainage strategy; the central control module generates a drainage pump rotating speed instruction and the like; the drainage execution module dynamically adjusts drainage pump power and a drainage path; the self-checking and fault-tolerant module calculates a health index of drainage equipment and switches redundant equipment; the early warning module sends risk level information; and the interaction module displays information such as a real-time accumulated water thermodynamic diagram. The urban road drainage efficiency can be improved, the water accumulation risk is reduced, and smooth urban traffic and public travel safety are guaranteed.

The invention discloses a road congestion prediction system and method based on spatial-temporal feature extraction, and belongs to the field of intelligent traffic. The system adopts a layered distributed architecture, and comprises a multi-source data acquisition module, a data preprocessing unit, a double-flow spatio-temporal feature extraction network, a two-stage spatio-temporal attention mechanism module, a congestion prediction model and a result feedback interface. Multi-modal data such as a vehicle-mounted GPS track, checkpoint flow, video monitoring and meteorological data are integrated, a double-flow feature extraction network is constructed by adopting a graph convolutional network and a bidirectional gating circulation unit, and a key space-time region is dynamically focused in combination with a multi-head self-attention and time weighted dot product attention mechanism; and finally, optimizing the generalization ability of the model through a composite loss function. According to the method, a dynamic adaptive learning framework and multi-source data combined modeling mode is adopted for urban road traffic flow characteristics, the space-time precision and the real-time response capability of road network congestion prediction are remarkably improved, and reliable decision support is provided for intelligent traffic control.

The invention discloses an urban road network emission dynamic evaluation method for a large-scale road section scale, and belongs to the technical field of urban road network emission prediction. In order to accurately consider the comprehensive influence of multi-scale space factors on carbon emission, a road network topological structure is constructed, obtained emission historical data of all road sections are expanded, then a multi-scale space feature extraction module is constructed, and the multi-scale space feature extraction module comprises a district feature extraction layer, a road section feature extraction layer, a gating fusion layer, a time feature extraction module and an emission prediction module. And then urban road network emission dynamic prediction oriented to a large-scale road section scale is carried out based on the emission historical data of all road sections. According to the method, data of all levels are organically integrated, potential relations among the data are fully mined, spatial distribution differences of road carbon emission are comprehensively and meticulously reflected, a solid foundation is laid for carbon emission analysis and prediction, the characterization capability of spatial features of the road carbon emission is improved, and the method is suitable for popularization and application. And powerful support is provided for formulation of accurate prediction and effective emission reduction strategies.

The invention relates to the technical field of intelligent traffic, in particular to an urban traffic flow prediction method fusing a dynamic graph convolutional network and a Transformer, which comprises the following steps: firstly, collecting traffic data in a road network to form a data set, then constructing a space-time dynamic GCN unit to capture dynamic evolution of road network topology, and extracting multi-scale space-time characteristics through expansion time convolution; then, constructing a Transform unit for modeling a global time sequence dependency relationship of the traffic flow; finally, in combination with a gating fusion prediction unit, spatial-temporal features are fused through a spatial-temporal cross attention mechanism, and the prediction precision and robustness are effectively improved. Through verification and evaluation of real data, the method is suitable for a traffic flow prediction scene in an urban road network dynamic environment, and especially has good performance in sudden road conditions and peak hours.

The invention aims to provide a large-range urban road network intelligent management and control method based on mobile phone signaling data, and belongs to the technical field of traffic management and control. Real-time traffic flow parameters are obtained by preprocessing the mobile phone signaling data, a high-fidelity microscopic traffic simulation environment is constructed, and on the basis, the real-time traffic flow parameters are obtained; the method comprises the following steps: establishing a multi-dimensional evaluation index system containing operation safety and efficiency, constructing a macro-micro collaborative double-layer planning model, adopting a deep reinforcement learning algorithm, taking continuous-discrete mixed decision variables such as intersection signal timing and a variable lane strategy as optimization objects, carrying out strategy learning and iterative optimization through an Actor-Critic architecture, and carrying out optimization on the optimization objects. And outputting the optimal control strategy combination. According to the invention, dynamic and accurate cooperative management and control of the large-range urban road network are realized, and the traffic efficiency is remarkably improved while the operation safety is guaranteed.

The invention relates to the technical field of Internet of Vehicles communication, and discloses an Internet of Vehicles channel prediction method based on multi-modal fusion and related equipment. The method comprises the following steps: constructing an urban road scene of an Internet of Vehicles channel, and obtaining known time frame data and unknown to-be-predicted time frame data in the urban road scene in combination with a weather scene; carrying out data enhancement, data vector representation and position information processing to obtain a multi-modal vector, and carrying out feature extraction and splicing processing on the multi-modal vector to obtain a multi-modal feature vector; performing inter-modal deep fusion on the multi-modal feature vector to obtain a joint feature vector; and performing nonlinear mapping on the joint feature vector through a multi-layer perceptron module, outputting to obtain channel index probability distribution, and predicting the Internet of Vehicles channel according to the channel index probability distribution. The technical problems that in the prior art, the single-mode sensing capacity is limited, the original data quality is poor, and the robustness is insufficient in a complex scene are solved.

The invention discloses an outdoor robot control system based on a fusion positioning method, which comprises a positioning navigation module, an interaction module and an execution module, and is characterized in that the positioning navigation module is in data connection with the interaction module and the execution module, and the interaction module is in data connection with the execution module; according to the invention, the RTK, UWB and IMU units are fused, and all the units work cooperatively, so that the problem of continuous and accurate positioning of the traffic isolation robot in a complex urban road environment is solved; according to the method, the road scene where the robot is located is judged through the positioning signal attenuation model, precise switching of the corresponding optimal positioning method is achieved, and high-precision positioning of the traffic isolation robot is achieved; according to the invention, the positioning signal attenuation map and the obstacle map are fused into the two-factor environment map, an optimal path which can avoid obstacles and consider the positioning signal attenuation factor is generated, and navigation is carried out, so that the positioning precision and the navigation accuracy are ensured.

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 belongs to the technical field of trajectory planning, and discloses an urban road trajectory planning and control method and system, and the method comprises the steps: generating an obstacle set and a Frenet projection of a road physical boundary through employing a spatial clustering algorithm; utilizing an extreme value method and a self-adaptive smoothing algorithm to dynamically construct a feasible trajectory region trajectory, and synchronously performing interval constraint expansion on a future prediction trajectory of the dynamic obstacle; constructing a path optimization objective function with interval constraints, and solving an optimal path variable in real time by using a numerical optimization method; an expected speed and acceleration sequence of each sampling point is generated based on a space-time domain dynamic planning method, and a longitudinal motion curve is optimized in real time by using an objective function; and inputting the space path and the longitudinal speed sequence into an MPC system, and calculating an optimal front wheel steering angle and acceleration control instruction to realize vehicle trajectory tracking. The method can sense errors and environment changes in a self-adaptive mode, the response speed and robustness of dynamic traffic flow are improved, and multi-target collaborative optimization is achieved.

The invention discloses an urban road CO2 concentration hourly simulation prediction method based on multi-source data fusion and integration algorithm driving, and the method comprises the steps: integrating road underway monitoring CO2 concentration data, road information and traffic data, meteorological data, human geographic data and other multi-source heterogeneous data; a CO2 concentration simulation model based on an integrated algorithm of a random forest (RF) model and a long short-term memory (LSTM) network model is constructed. According to the method, when the CO2 concentration of the urban road is simulated, the problem that a single model can not simulate accurately possibly is considered, an integration algorithm is adopted, the random forest model and the long and short-term memory network model are combined and integrated, and the optimal model is selected to simulate the CO2 concentration of the road, so that the simulation accuracy is effectively improved.

Aiming at the problem of time-dependent multi-target green vehicle path optimization, the invention designs a space-time attenuation factor-driven urban logistics low-carbon robust optimization method, takes the total vehicle distribution time and carbon emission as optimization targets, and adopts double-layer ant colony pheromones to guide ant colony search, so that the optimal path optimization is realized. The adaptive capacity of ants in a high-disturbance uncertain environment is improved, the convergence of solutions is enhanced, more solution sets balancing robustness and optimality are excavated through solution robustness evaluation and a feedback mechanism thereof, the diversity of optimal solutions is improved, an optimal vehicle path robust optimization scheme is obtained, the carbon emission of urban road network distribution is reduced, and the urban road network distribution efficiency is improved. And the distribution efficiency of logistics is improved.

The invention discloses an urban road multi-risk evaluation method based on DAS and acoustic emission signal characteristics, and relates to the technical field of urban road safety monitoring. The method comprises the following steps: receiving voiceprint signals around existing communication optical fibers of urban roads acquired based on a plurality of channels of a DAS (Data Acquisition System); extracting a time domain, a frequency domain and composite features based on the acquired voiceprint signals around the existing communication optical fiber of the urban road, removing redundant features of the time domain, the frequency domain and the composite features through a Pearson correlation coefficient matrix, and generating optimized time domain, frequency domain and composite features; and for the optimized time domain, frequency domain and composite features, performing anomaly judgment by adopting an area outside a confidence interval of single feature density distribution and two-dimensional Gaussian distribution, and positioning an abnormal DAS channel in combination with principal component analysis dimension reduction and a DBSCAN clustering method. According to the invention, underground cavity hidden dangers of urban roads can be effectively detected, ground risk events can be classified, and urban road safety risk monitoring and evaluation are realized.

The invention provides a traffic flow prediction method based on multivariate spatial-temporal feature extraction, and the method comprises the steps: employing a multivariate spatial-temporal feature extraction model, and employing a Res2Net module improved through gating convolution in a time dimension, so as to capture a change mode of a traffic flow along with a time scale; the method comprises the following steps of: capturing global spatial correlation, geographic neighborhood spatial correlation and semantic similarity spatial correlation among nodes in an urban road network by using a spatial attention mechanism and a double-view Geom-GCN from global and local perspectives in a spatial dimension; on the time-space interaction dimension, a dynamic space-time synchronous aggregation module constructed by using space-time attention is used to capture a dynamic interaction relationship between nodes or regions across time slices so as to predict the future road traffic flow.

The invention discloses an urban rail transit station site selection multi-objective optimization method and a related device, and belongs to the crossing field of urban planning and traffic engineering, and the method comprises the following steps: according to preprocessed urban road network data, screening crossing points of urban three-level roads and roads above the three-level roads as candidate stations; the coordinates of the candidate sites are coded; based on the preprocessed crowd travel origin and destination data, POI data, building data and land utilization data, quantifying the service people flow, facility accessibility and land development intensity of each candidate site, and forming a feature matrix of each candidate site; and constructing a multi-target traffic station site selection model, screening candidate stations meeting distance constraints through a greedy search algorithm according to the feature matrix, inputting the candidate stations to NSGA-II, and solving a multi-target optimization function of the multi-target traffic station site selection model to obtain an optimal planning result. The method can solve the problems that in the prior art, the target is single during site selection, and the inter-site distance constraint is not considered.

The invention discloses a road garbage detection method in combination with Real-Time Desection Transform (RT-DETR) and a state space, and the method comprises the following steps: firstly, obtaining an image through a crawler, carrying out the marking of the image, and constructing a garbage detection data set of a diversified urban road scene; secondly, designing a brand-new feature pyramid structure (FFS) and an internal scale feature enhancement module (IFEM) based on a state space, and establishing a road garbage detection model combining the RT-DETR and the state space; finally, a self-built road garbage data set is used for training the model, an experiment is carried out on a COCO2017 data set, the experiment proves that the algorithm provided by the invention obtains good balance between real-time performance and accuracy, and the performance of the model in a complex environment, especially the detection precision under small target recognition and shielding conditions, is remarkably improved.

The invention provides a distributed multi-agent traffic signal cooperative control method, and belongs to the field of intelligent traffic control. The method comprises the steps of performing initialization operation on a road network environment, constructing an adjacency list according to a topological structure of an urban road network, and regarding each traffic signal lamp control intersection in the urban road network as an independent intelligent agent; for each intelligent agent, acquiring a local observation state of the intersection where the intelligent agent is located; performing action selection through sampling according to the action probability distribution; sending the selected action to the traffic simulator by using the intelligent agent, updating the state of the traffic signal lamp, and calculating and returning the intelligent agent information of the next time step by using the traffic simulator; sending the action probability distribution to all neighbor agents; and parameter updating is carried out until a preset training round is reached, and cooperative control of the traffic signals is completed. By adopting the distributed multi-agent reinforcement learning framework, the exploration efficiency is improved, and the collaboration among the agents is promoted.

The invention discloses a ground penetrating radar (GPR) section horizontal interference suppression method based on a diffusion model. The method comprises the following steps: (1) constructing a standardized profile interference data set containing an actual measurement sample and a simulation sample; wherein an actual measurement sample comes from an urban road detection profile, and proper samples are screened out through mean value reduction preprocessing operation; a simulation sample is subjected to forward modeling based on a time domain finite difference method (FDTD), and various road structures and underground anomaly features are simulated; (2) a comprehensive ResBlock is introduced into the diffusion model, a lightweight Agent Agent module is introduced between ResBlocks of all layers, a space attention mechanism is deployed at the middle connection position of the Unet architecture, and a strategy based on Cosine theta schema is introduced in the forward diffusion stage; (3) training by using an improved diffusion model based on the constructed data set to obtain a trained model; and (4) sending test set data into the trained model, and completing the horizontal interference suppression test reasoning of the GPR profile. According to the method, construction of the horizontal interference data set and improvement of the diffusion model are combined together, the problems that a current traditional low-rank decomposition method is insufficient in intelligent degree, difficult to consider multi-source data and depends on manual parameter adjustment are solved, and technical support is provided for GPR high-resolution imaging and anomaly detection.