869 results about "Urban road" patented technology

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

The invention relates to the 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 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 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 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 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 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 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.

The invention relates to the field of traffic engineering, and discloses an urban road network toughness management platform and evaluation method based on dynamic cascade failure, and the platform comprises a disturbance scene simulation and random capacity degradation module, a dynamic cascade failure process simulation module, and a multi-dimensional toughness index calculation and uncertainty evaluation module. A toughness three-dimensional vector is formed by integrating system robustness, restorability and performance accumulated loss, and a time-varying dynamic traffic distribution model considering road network structure change is used for depicting a cascade failure dynamic process; a random variable is introduced into a damaged road section capacity index, and a new evaluation method is provided for solving the defect that the existing toughness evaluation method is difficult to comprehensively quantify the robustness and the restorability of a road network. A failure diffusion mechanism in an abnormal scene is truly restored, and an effective method is provided for overcoming the double defects that a traditional model is insufficient in quantification of travel behavior dynamics and a system recovery process.

The invention relates to the technical field of unmanned aerial vehicle flight path planning, in particular to a public route network generation method for a small unmanned aerial vehicle, which comprises the following steps: reading urban road network data, removing expressways and branches, and generating a two-dimensional route network; determining the lifting height according to the height of the tree, and generating a three-dimensional air route network; constructing a space matrix by using the surface feature passenger flow data and rasterizing a road network; rasterizing a coherent feasible region of a road network through morphological opening operation, and identifying an intersection and generating a topological structure by openCV; establishing a layered traffic line for the intersection to realize two-way traffic; the airspace utilization rate and the flight safety can be improved, and the problem of large-scale unmanned aerial vehicle path conflict is solved.

The embodiment of the invention relates to the technical field of intelligent driving, and discloses a motorcade passing control method, device and equipment based on global planning, and the method comprises the steps: obtaining the driving related information of vehicles in a motorcade; the motorcade comprises a plurality of vehicles, and the driving related information comprises at least one of road condition information, vehicle driving information and map navigation information; according to the driving related information of at least one vehicle in the motorcade, performing global planning by taking the vehicles in the motorcade not to fall behind or the fallen vehicles to get up to the motorcade as a control target, and determining a target state of a target vehicle in the motorcade; the target state indicates driving information needing to be executed by a target vehicle, and the target vehicle is any vehicle in a motorcade; and sending the target state of the target vehicle to the target vehicle to control the target vehicle to be switched from the current state to the target state. By applying the technical scheme of the invention, the current problems that the formation vehicles are easy to fall behind and are difficult to recover because of being cut off in the complex urban road can be solved.

The invention provides an intelligent monitoring and risk early warning method for urban road settlement, and belongs to the technical field of urban management based on machine learning. The method comprises the following steps: firstly, collecting four types of multi-source space-time monitoring data, including urban road settlement data, underground environment data, pavement structure data and dynamic load and environment data; secondly, constructing a multi-source data space-time completion model, performing unsupervised completion on sparse monitoring area data, and generating space-time continuous settlement field data; thirdly, constructing a road settlement health index prediction model fusing multiple factors, inputting complementation data and original features, and outputting a health index of a 0-1 continuous interval; and finally, in combination with the health index and the road function level, four-level risk classification and dynamic early warning are realized. According to the method, the problems of space-time faults and data islands of a traditional method are solved, urban road global real-time monitoring is achieved, and early warning upgrading from qualitative judgment to quantitative grading is achieved.

The invention provides an urban traffic management intelligent evaluation system and method based on a large language model, and belongs to the technical field of intelligent traffic management. The system comprises a data processing module, an MECA traffic flow analysis module, a feature engineering module, a driving behavior evaluation module, a clustering analysis module, an LLM intelligent decision module, a visualization generation module and a visualization module. The system analyzes the road traffic data collected by the unmanned aerial vehicle, adopts the MECA technology to automatically identify the road type and the traffic environment, intelligently judges the congestion level, evaluates the driving behavior, and combines a big language model to generate a targeted traffic management optimization suggestion. According to the method, the adaptive context sensing technology is innovatively combined with multi-criterion learning, adaptive congestion judgment of different road types is realized, different traffic characteristics of urban expressways, common urban roads and expressways can be accurately recognized, and differentiated management strategies are provided accordingly. The system supports real-time processing of large-scale traffic data, and provides scientific and accurate decision support for urban traffic management departments.

The invention discloses a fast charging station planning method based on urban dynamic traffic demand prediction and correction, and relates to the technical field of electric vehicle fast charging station planning, and the method comprises the steps: S1, constructing a traffic topology connection matrix according to urban road network topology, and recognizing traffic hub nodes and long-distance nodes as candidate nodes; s2, proposing three dynamic factors including social demands, user public praise and demand elasticity, and correcting the electric vehicle traffic demand prediction value of each path unit; s3, constructing a multi-target fast charging station capacity optimization model taking the minimum planning total cost and prediction error as targets, wherein the multi-target fast charging station capacity optimization model comprises fast charging station self constraints and power grid adaptation constraints; s4, solving the model by adopting a non-dominated genetic sorting algorithm to obtain a Pareto solution set, and selecting a planning result according to the demand of a decision maker; according to the method, the problems of blind site selection and static demand prediction in traditional planning are solved, economy and power grid safety are considered, planning scientificity and decision-making flexibility are improved, and supply and demand of charging resources can be effectively balanced.

The application discloses a kind of urban road physical structure hidden danger investigation method, device and electronic equipment, method includes: obtaining the road physical structure data in target road network;According to the road physical structure data, determine the risk evaluation index data corresponding to target type road, the risk evaluation index data is determined according to the road attribute of corresponding type road;According to the risk evaluation rule and the risk evaluation index data of the preset, the risk of target type road is evaluated;According to the risk evaluation result of target type road, determine the hidden danger investigation scheme of target type road.The method of the application can make the road hidden danger investigation more efficient, effectively solve the problem that the dependence degree of existing road hidden danger investigation scheme on the working experience of investigators is high and the investigation is not thorough, greatly save the labor cost.

The invention belongs to the technical field of near-roadside pollution diffusion simulation, and particularly relates to a road traffic pollution diffusion rapid simulation method based on a physical structured proxy model. Comprising the following steps: S1, meteorological characteristic engineering and sampling; s2, road network standardization decomposition: carrying out topology discretization decomposition on the complex urban road network into standardized unit line sources which can be independently calculated; s3, reconstructing spatial geometric features; s4, physical mechanism dual partitioning: constructing an orthogonal physical mechanism partitioning system according to the atmospheric stability level and the relative position of the receptor point; s5, proxy model training: respectively training an XGBoost regression model for each physical partition; and S6, area field rapid reconstruction: receiving real-time boundary conditions for parallel inference, and rapidly reconstructing an area pollutant concentration field through linear superposition. On the premise of ensuring the simulation precision highly consistent with that of a full-physical model, the calculation efficiency is remarkably improved, and the method is suitable for real-time air quality simulation of a large-scale urban road network.

The invention relates to a vehicle-mounted road defect detection system and method based on binocular vision and deep fusion, and the system comprises a binocular camera module, a main control platform, a positioning module, a communication module and a power supply control module. The system realizes automatic detection and severity quantitative evaluation of various pavement defects such as cracks, pits and ruts, obtains defect positions in combination with a positioning module, and uploads the defect positions to a cloud platform in real time through a communication module, thereby forming a closed-loop urban road defect information perception and management system. According to the method, the quantitative evaluation capability of the identification accuracy and severity of the road defects is remarkably improved, and automatic identification, accurate positioning and real-time cloud synchronous uploading of the road defects are realized; the method has the advantages of flexible deployment, efficient operation, high adaptability and the like, is particularly suitable for urban road intelligent inspection and maintenance management scenes, and has good engineering application prospects and popularization values.

The invention provides a vehicle path planning method based on a multi-head adaptive Actor-Critic algorithm, and the method comprises the steps: obtaining urban road network data comprising at least one warehouse center and more than two demand client nodes through a simulation generation technology, obtaining a training and testing data set, carrying out the coding processing of the urban road network node training data, and carrying out the coding processing of the urban road network node training data, obtaining a multi-dimensional vehicle path coding sequence on a two-dimensional plane space [0, 1] * [0, 1]; a deep reinforcement learning framework is built based on the multi-dimensional vehicle path coding sequence, a multi-head adaptive Actor-Critic algorithm is integrated to build an MHAAC model, and a vehicle path planning system is formed; and the constructed MHAAC model is adopted to carry out path solving on urban road network data, and a global optimal path scheme is generated under the condition that all customer demand constraint conditions are met. According to the scheme, the problems of poor adaptivity, prolonged solving time, reduced solution quality and the like caused by limitations of sparse environmental information features, fixed feature embedding, static parameter generation, single decoding strategy and the like are successfully solved.

The invention provides an underground cavity nondestructive detection method and system using a mobile terminal. The method comprises the following steps: generating a sound wave signal with a preset frequency through the mobile terminal; the sound wave signals are collected and preprocessed; analyzing the frequency spectrum of the sound wave signal, and extracting the frequency spectrum characteristic of the sound wave signal; and carrying out resonance frequency shift detection and cavity depth estimation based on the frequency spectrum characteristics. According to the invention, underground cavity rapid detection without extra hardware is realized; the method disclosed by the invention is extremely low in deployment cost and suitable for large-scale popularization; the method is high in detection precision, and meets the requirements of complex scenes such as urban roads and culverts.

The invention discloses a multi-fractal-based urban green vision rate dynamic evolution mode identification method. The method comprises the following steps: S100, acquiring urban street green vision rate data; s200, acquiring urban multi-scale walking isochronous circle data, and acquiring walking reachable areas within 5 minutes, 10 minutes, 15 minutes and 20 minutes through an API (Application Program Interface) of an OpenRouteService platform on the basis of an urban road network; s300, identifying a dynamic evolution mode of the green vision rate; and S400, identification result demonstration, including spatial distribution of a green vision rate dynamic evolution mode, key threshold identification and spatial structure differentiation, is used for assisting urban greening structure optimization and scientific intervention strategy formulation. According to the method, the problems of staticization, single scale and insufficient structure expression in the existing urban street view green visual rate analysis are solved.

The invention provides an MPC lane changing trajectory tracking control method based on Transform, and aims to improve the trajectory tracking precision of an automatic driving vehicle in a complex urban road. The method specifically comprises the following steps of collecting a vehicle motion state and corresponding Q and R matrix data through a simulation experiment, and screening data with an optimal control effect to form a training set; utilizing a Transform model to learn a mapping relation between a vehicle state and a weight matrix; inputting the real-time state of the vehicle into the model to obtain dynamically updated Q and R matrixes and applying smoothness constraint; the control matrix is input into an MPC controller, and high-precision lane changing trajectory tracking is achieved through rolling optimization. The method can achieve the real-time dynamic updating of the MPC parameter matrix, remarkably improves the trajectory tracking precision and control adaptability, is good in real-time performance, robustness and safety, and is suitable for the motion control and optimization of an automatic driving vehicle in a complex traffic environment of urban roads.

The invention discloses a method for calculating road network traffic operation carbon emission by using vehicle trajectory data. The method comprises the following steps: firstly, segmenting vehicle GPS trajectory data according to timestamps and serial numbers to obtain a trajectory subset of each vehicle; determining a vehicle stop interval based on the minimum instantaneous speed threshold value, and dividing the trajectory data into driving sections to obtain a driving trajectory; constructing an urban road network model, calculating a spatial distance between a track and a road section, screening candidate road sections according to a shortest distance threshold value, obtaining a matched road section in combination with a minimum difference value of an azimuth angle, mapping track points to the road section, and obtaining re-expanded driving section track data; according to the timestamps and the road section serial numbers, segmenting according to the road section serial numbers to obtain each road section track subset; idle speed and normal driving intervals of a road section are identified through spatial-temporal clustering, a MOVES model database is introduced to perform classified calculation on carbon emissions of the corresponding intervals, the carbon emissions are summarized to obtain road section emissions, and road network traffic carbon emissions are summarized to obtain road network traffic carbon emissions. According to the method, macrotopography and microscopic real-time working condition data are fused, and a road section carbon emission measuring and calculating method is perfected.

The invention discloses a new energy automobile endurance mileage increasing system and method based on multi-mode energy collaborative management. The system comprises a multi-mode energy input module which is used for converting solar energy into direct current electric energy through a photovoltaic array and integrating power battery energy and hub motor kinetic energy recovery energy; the predictive management module is used for acquiring front road condition information through a V2X technology and predicting an energy consumption curve based on a deep learning model; the dynamic weight distribution module is used for calculating photovoltaic, battery and kinetic energy recovery weight coefficients through a dynamic weight distribution algorithm according to the real-time vehicle environment information and the prediction result; and the energy distribution control module is used for carrying out weighted distribution on the multi-modal energy based on the weight coefficient and outputting total available energy and a control signal so as to drive electric equipment or charge a battery. According to the invention, the problem of dynamic energy distribution during multi-energy coupling is solved, and the purposes of improving the endurance of urban roads, improving the endurance of expressways, reducing the increment cost of the system and being compatible with a 400V / 800V platform are achieved.

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

The invention belongs to the technical field of intelligent traffic, and particularly discloses an urban road network traffic state and congestion propagation probability prediction method and system. The method comprises the following steps: firstly, introducing information such as spatial distance and historical flow correlation on the basis of road topology to form a weighted gate road network; secondly, a conditional denoising diffusion model based on a bayonet space-time diagram is provided, the model takes historical multi-step diagram signals and a road network structure as conditions, and modeling is carried out on conditional distribution of future multi-step diagram signals through forward noise adding and reverse denoising processes; according to the method, multiple traffic evolution sample tracks in the future are obtained through multiple times of sampling, node operation indexes are mapped into discrete congestion levels, the occurrence frequency of each node under different congestion levels is counted, and the node congestion level probability is obtained; and further counting a time sequence co-occurrence relationship of congestion states between adjacent nodes, estimating a propagation condition probability of congestion in a road network, and constructing a key node influence degree index and a high-risk propagation path.

The invention provides an urban road network traffic congestion state prediction method and system based on deep learning, and relates to the technical field of intelligent traffic control, and the method comprises the steps: obtaining congestion state image data of an urban road network, and carrying out the preprocessing to extract structured road congestion information; constructing a road network topological graph based on the road congestion information, and performing structure sensing state coding on the road network topological graph to generate a structure sensing state vector fusing local congestion features and global topological position information; and inputting the structure perception state vector into an improved deep learning prediction model, performing feature deepening and enhancement through a graph neural network which dynamically adjusts a graph structure and aggregates node information, and finally outputting road network congestion state prediction results of a plurality of time scales in the future. According to the method, accurate prediction of the urban road network congestion state can be realized, and reliable technical support is provided for intelligent traffic management, resource scheduling optimization, travel path planning and the like.

The invention relates to the technical field of intelligent traffic monitoring, and discloses a road surface accumulated water detection and identification method based on multi-view feature fusion. The method comprises the following steps: acquiring image data, depth information and environmental parameters through sensor nodes deployed at multiple positions of a road surface to form an original monitoring data set; performing multi-source feature extraction and fusion processing on the data set to obtain an accumulated water feature image set; performing spatial domain analysis by using the image set to generate a pavement partition consistency map; time dimension data is extracted based on the map, dynamic change evaluation is carried out, and a ponding evolution report is output; detecting an abnormal mode from the report, and identifying an abnormal ponding area; and calculating a risk index according to the abnormal region, and generating a final road surface ponding risk map. According to the method, through multi-source data fusion and spatio-temporal conjoint analysis, accurate detection, dynamic evolution tracking and risk assessment of pavement ponding are realized, and the accuracy and early warning capability of urban road ponding monitoring are remarkably improved.

The invention discloses an urban road tunnel reconstruction and extension scheme comparison and selection method, which belongs to the field of tunnel engineering, and comprises the following steps: establishing a universal standardized comparison and selection condition parameter library, and quantizing and encoding parameters in the comparison and selection condition parameter library to construct an urban road tunnel reconstruction and extension scheme database matrix; constructing an expert authority coefficient matrix based on the expert basic information; training a random forest model by using the urban road tunnel reconstruction and extension scheme database matrix, and generating a preliminary scheme suggestion based on a proposed project demand; independently scoring the preliminary scheme suggestion and giving an independent total score to form an expert scoring tensor and an independent total score vector; weighting the expert score tensor based on the expert authority coefficient matrix, training a random forest model by using the weighted data and the independent total score vector, obtaining the comprehensive score of each scheme, and determining the optimal scheme; and adjusting the expert authority coefficient matrix according to the scoring result of the optimal scheme, and bringing the optimal scheme data into the scheme database matrix for continuous learning and optimization.