1260 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 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 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 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 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.

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 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 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.

The invention relates to a ground penetrating radar urban road hidden disease image detection method based on YOLO11n-GPR, and the method comprises the steps: obtaining an urban road underground crack and cavity image data set collected by a ground penetrating radar, and carrying out the preprocessing; a target detection model YOLO11n-a based on the improved YOLO11n is constructed, and YOLO11n-b is further obtained according to a structured pruning strategy; the YOLO11n-a serves as a teacher model, the YOLO11n-b serves as a student model, knowledge distillation training is conducted through the training set, and a hidden disease detection model YOLO11n-GPR is obtained; and obtaining a to-be-detected urban road underground ground penetrating radar scanning image, inputting the image into the hidden disease detection model YOLO11n-GPR, and outputting an urban road hidden disease detection result. Compared with the prior art, the method has the advantages of solving the problems of complexity, accuracy and timeliness of existing detection and the like.

The embodiment of the invention provides a training method of a signal lamp control model, a signal lamp control method, a device, equipment, a medium and a product, and relates to the technical field of urban road traffic. The method comprises the following steps: acquiring a plurality of first traffic data, constructing a state characterization matrix and a reward function according to the plurality of first traffic data, and performing model training according to the plurality of first traffic data, the state characterization matrix and the reward function to obtain a signal lamp control model. According to the method, in a model training process, a state representation matrix is utilized to dynamically quantify passing priorities of buses in all directions, and meanwhile, a reward function is utilized to guide the model training process, so that a signal lamp control model obtained through training can recognize and preferentially respond to bus passing demands, and signal lamp actions are dynamically adjusted; therefore, the collaborative optimization of multi-direction bus priority requests in a complex scene is realized, and the coordination capability and response efficiency of a bus priority system in a complex urban traffic environment are improved.

The invention discloses a traffic flow prediction method based on multi-scale dual hypergraph fusion. Aiming at the problem that an existing traffic flow prediction model is difficult to capture a multi-scale high-order spatial dependency relationship of a traffic road network, the method comprises the following steps of: firstly, constructing an urban road dual hypergraph with three scales of microscopic individual travel intention, mesoscopic community commuting interaction and macroscopic area flow conduction, and then designing a traffic flow prediction method of space-time perception based on the urban road dual hypergraph. Wherein the spatial perception module extracts and fuses a dependency relationship between a high-order local spatial feature and a global spatial feature in a multi-scale dual hypergraph; the time sensing module captures short-term fluctuation and long-term trend of traffic flow; and residual connection is introduced to enhance spatio-temporal feature fusion, and finally multi-step traffic flow prediction is realized. The invention provides a traffic flow prediction model with multi-scale high-order space perception, which adaptively fuses high-order space features of three scales of microcosmic, mesoscopic and macroscopic, and significantly improves the precision and generalization ability of traffic flow prediction.

The invention discloses an urban road rainwater runoff stage treatment system and method, and relates to the technical field of urban rainwater pollution treatment. In order to solve the problems that an existing system is difficult to monitor multi-index water quality in real time, the pollution degree is accurately recognized, and distribution control lacks dynamics, the system is provided with a multi-source spectrum water quality monitor at the tail end of a pipe network, turbidity, COD, TOC and heavy metal concentration are monitored in real time, rainfall intensity data are accessed into a control center, the pollutant load instantaneous rate L is calculated through a weighted summation formula, and the real-time monitoring of the pollution degree is achieved. A controllable flow distribution module in the flow distribution well distributes the rainwater to an enhanced treatment unit, a biological aerated filter system or a horizontal flow sedimentation tank, wherein the enhanced treatment unit comprises a rotational flow sand setting unit, an electrochemical heavy metal degradation unit and a chemical flocculation unit. The system is mainly used for realizing quality-divided efficient treatment and resource recycling of rainwater and guaranteeing the safety of a receiving water body.

The invention relates to the field of computers, and discloses a visual target detection method and system based on hypergraph calculation, and the method comprises the steps: collecting the traffic data of different modes of a city through a multi-source sensor device; performing space-time alignment and denoising on the traffic data of different modes, and extracting multi-scale features through a feature encoder; traffic participants, road infrastructures and dynamic environment information are used as hypergraph nodes, interaction relations between the nodes are used as hyperedges to construct a hypergraph, and in a hypergraph feature space, a classifier is used to carry out target category prediction on the nodes; and target tracking and prediction: based on a hypergraph reasoning mechanism, fusing historical trajectory data and real-time traffic flow information, and predicting a target position. The method can solve the problems of low detection precision, low calculation efficiency and the like of an existing visual target detection technology in a complex traffic environment, and is suitable for application scenes such as urban road monitoring, traffic flow analysis and abnormal target detection.

The invention relates to the field of intelligent traffic, and discloses a space-time big data-based software planning auxiliary decision-making method and system, and the method comprises the following steps: fusing mobile phone signaling, a vehicle GPS track, weather and traffic event data, constructing a dynamically updated space-time knowledge graph, and defining a space-time association relationship among a demand point, a connection station and a traffic event node; establishing a government, enterprise and passenger three-party asymmetric Nash negotiation model based on the knowledge graph, dynamically adjusting bargaining capability parameters in combination with real-time traffic events, and solving a game equilibrium solution; constructing a mixed integer collaborative optimization model by taking the equilibrium solution as a constraint condition, and solving a connection station layout and departure frequency scheme by adopting a two-stage algorithm; dynamically correcting a target function weight parameter according to the passenger flow prediction error through a PID closed-loop feedback mechanism; and finally, outputting a visual decision scheme. According to the method, the real-time decision-making efficiency and the scheme robustness in a large-scale urban road network scene are improved.

The invention relates to the technical field of traffic management, in particular to an urban road intersection passage intelligent optimization method based on multi-modal information, which comprises the following steps: acquiring real-time sensing data of pedestrians and non-motor vehicles through a video camera, a millimeter wave radar and a laser radar, and generating fusion sensing data through timestamp synchronization and coordinate system unification; identifying and generating a target list with category labels by using a detection and clustering algorithm, and obtaining a stable motion trail and intensity by combining with multi-target tracking; predicting a crossing intention and a path based on time sequence deep learning, calculating an interleaving point and quantifying a conflict risk; and according to a comparison result of the conflict risk coefficient and a threshold value, generating a strategy control instruction of different time periods, different paths or a mixed mode, and in combination with execution time window information, forming an optimized timing scheme through cooperative execution of an intelligent prompt identifier, a telescopic isolation belt and a signal control machine, so as to realize cooperative passage. The method improves the recognition precision, reduces the conflict risk, and improves the passing efficiency.

The invention discloses a simulation method for urban flood emergency rescue path planning, and the method comprises the steps: carrying out the modeling of an urban road network based on an urban topographic map, rainfall data, existing water distribution and key position information; mIKE simulation software is used for establishing a hydrodynamic model to simulate the rainfall and flood spreading process of the city, and flood dynamic information, including the depth and flow velocity of the flood, at each position point in the flood disaster scene model is obtained; respectively constructing objective functions according to the shortest maximum time of arrival of the disaster relief point, the lowest driving risk of the rescue vehicle and the shortest total weighted rescue time; setting constraint conditions to ensure that all disaster relief points are covered, and preventing vehicles from being excessively distributed; based on the flood dynamic information, the objective function and the constraint condition, adopting an NSGA-II non-dominated sorting genetic algorithm to solve a Pareto solution set; and performing weighted analysis on a result in the Pareto solution set to obtain an optimal solution, and outputting an urban flood emergency rescue path plan.

The invention discloses an urban traffic signal real-time collaborative optimization system and method based on a space-time diagram convolutional network and reinforcement learning, and relates to the technical field of intelligent traffic control. In order to overcome the defects of traffic signal fixed period control, the technical scheme adopted by the invention comprises edge computing equipment which is deployed beside an intersection camera and is used for acquiring video stream data in real time through a built-in local model, extracting traffic flow state characteristics and realizing dynamic phase timing optimization through cross-intersection collaborative decision, meanwhile, local model parameters are generated and uploaded to the cloud federated learning platform; the cloud federated learning platform is used for aggregating and optimizing the local model parameters of the edge computing devices, and regularly issuing global update parameters to the edge computing devices; and the traffic signal control equipment is deployed at the intersection and is used for adjusting the display state of the traffic signal lamp in real time according to the dynamic phase timing instruction. The traffic efficiency of the urban road network can be obviously improved, and the traffic control cost is reduced.