1493 results about "Urban transportation" patented technology

The invention provides a real-time map updating method and system based on multi-source geographic information data fusion. Wherein a three-dimensional model is constructed by integrating traffic flow data, remote sensing images and road network topology, the road traffic pressure is quantified, and a feature correlation index is established. And obtaining a vehicle displacement vector and a speed gradient in combination with laser point cloud and video monitoring data, generating a dynamic road feature data set, and establishing a mapping relationship with the index table. On the basis, a road state prediction model is constructed, and through correlation analysis of traffic pressure data and a real-time feature data set, deep correlation between vehicle motion features and road states is established. And dynamically adjusting a road network connection structure, synchronously updating mapping parameters and predicting model precision, and realizing real-time adaptive adjustment of the road network topology. According to the technical scheme provided by the invention, the real-time performance of map updating and the road condition prediction accuracy are remarkably improved, and the urban traffic congestion index can be reduced.

The invention discloses a low-altitude aircraft take-off and landing platform site selection optimization method. The method comprises the steps that real-time dynamic data and static GIS data including urban traffic flow data, meteorological data, landform data, environment data and POI data are acquired; constructing an urban three-dimensional digital model according to the static GIS data, and constructing a digital twin model according to the three-dimensional digital model and the real-time dynamic data; constructing environment constraint conditions and safety constraint conditions of candidate take-off and landing platform positions in the digital twin model; performing multi-objective optimization on the digital twin model through a particle swarm optimization algorithm, and generating an optimal candidate take-off and landing platform site selection scheme set meeting environment constraint conditions and safety constraint conditions; and dynamically updating the digital twin model according to real-time data feedback, and dynamically adjusting the site selection scheme of the take-off and landing platform. According to the method, the virtual city model is constructed through the digital twin, an accurate simulation environment and real-time feedback are provided for site selection optimization, and the accuracy and feasibility of a site selection scheme are improved.

The invention discloses an intelligent decision analysis system and method based on multi-source data, and belongs to the technical field of artificial intelligence. According to the invention, multi-source data related to bus operation is acquired, real-time acquisition and uploading of bus operation data are realized, data preprocessing is carried out, and the multi-source data are integrated; feature engineering is carried out, and a passenger flow prediction model is established; based on a passenger flow prediction model, obtaining predicted passenger flows in different time periods of the day, and predicting a driver state; establishing a bus scheduling model by using a deep learning model, and inputting predicted passenger flow, predicted driver state, historical data of bus operation and urban traffic condition data to obtain a bus scheduling scheme on that day; when the dispatched buses in a certain period of time of the bus route are not enough, a route from a bus station closest to a starting station of the bus route to the starting station is obtained, and idle buses are dispatched; and if no idle bus exists in the bus station, the buses of the rescue bus route allowed to be dispatched are dispatched.

The invention discloses a built environment and traffic accident causal effect analysis method based on bi-robust learning, and belongs to the field of intelligent traffic safety and traffic accident risk assessment. By integrating satellite remote sensing, interest point distribution and traffic accident data, a spatial-temporal feature enhanced environment index system is constructed; realizing cross-regional data collaborative analysis by adopting federal learning, and guaranteeing data compliance in combination with differential privacy encryption; a generative adversarial network is introduced to generate a high-fidelity anti-fact scene, and a dual robust learning framework is coupled to accurately quantify a condition average processing effect of a built environment on accident risks. According to the scheme, a technical tool with both theoretical preciseness and practical feasibility is provided for urban traffic planning and safety management, and a scientific basis is provided for optimizing and building environment design and reducing traffic accident risks through a multi-source data fusion and privacy cooperation mechanism; and the blank in the aspects of causal robustness, insufficient anti-fact generation precision, privacy protection and multi-source data fusion in the prior art is filled.

The invention discloses an urban traffic intelligent regulation and control system and method based on digital twinning. The method comprises the following steps: S1, collecting and fusing multi-source data in real time; s2, constructing a digital twinborn model; s3, traffic flow prediction and optimization; s4, multi-system cooperative control is carried out; the system comprises the following modules: a multi-source data real-time acquisition and fusion module which comprises a video data acquisition and processing unit, a geomagnetic sensor network construction unit and a floating car data integration unit; the digital twinborn model building module comprises a three-dimensional road network model building unit and a data fusion and calibration unit; the traffic flow prediction and optimization module comprises an intelligent prediction model unit and a real-time optimization control unit; and the multi-system cooperative control module comprises an emergency priority passing system unit, a reversible lane dynamic adjustment unit and a parking lot induction unit. The traffic efficiency can be remarkably improved, the cooperative response speed is greatly increased, and carbon emission is effectively reduced.

The invention discloses an urban traffic flow prediction method and system based on time sequence deep learning, and relates to the technical field of intelligent traffic, and the method comprises the steps: 1, collecting traffic flow data and road network topology, and carrying out the preprocessing; 2, constructing a static adjacency matrix and a dynamic adjacency matrix according to the preprocessed traffic flow data and road network topology; 3, calculating a space-time attention weight by adopting a space-time attention mechanism according to the preprocessed traffic flow data; and 4, according to the static adjacency matrix, the dynamic adjacency matrix and the space-time attention weight, using the improved space-time diagram convolutional network to predict and output future traffic flow. The urban traffic flow prediction method combines the space-time diagram convolutional network, the space-time attention mechanism and the adaptive diagram generation mechanism to realize accurate urban traffic flow prediction.

The invention discloses a smart city traffic resource optimal configuration method, a platform and a storage medium, and relates to the technical field of traffic optimization. When a road accident is detected, accident information is collected, and a traffic accident position and traffic accident characteristics are obtained; time consumption prediction is carried out according to the traffic accident characteristics, and traffic control duration is obtained; calling real-time traffic data from an intelligent traffic system to obtain associated road data; executing multi-thread traffic flow back-stepping analysis to obtain associated traffic flow prediction information; carrying out shunting performance evaluation; and dynamically updating the signal lamp configuration of the plurality of shunting candidate roads by taking the associated traffic flow prediction information as a constraint. According to the invention, the technical problems of aggravation of traffic jam and low emergency management efficiency caused by lack of scientificity and dynamic adaptability of shunting planning and lagging of signal lamp regulation in a traffic accident scene in the prior art are solved, and the technical effects of optimizing traffic resource configuration, relieving traffic jam and improving traffic emergency management efficiency are achieved.

The invention discloses a smart city traffic signal lamp management and control system, and relates to the technical field of traffic signal control. The system comprises a region division module, a traffic flow detection sensor, a region traffic flow analysis module, a signal lamp management and control strategy generation module, an inter-region signal lamp cooperative control module and a variable information sign and information processing and generation module. The system can accurately monitor and analyze urban traffic flow conditions in real time by comprehensively applying an advanced region division algorithm, a traffic flow detection technology and an accurate traffic flow state evaluation model, and can intelligently generate a signal lamp management and control strategy according to an analysis result, so that the traffic lamp management and control efficiency is improved. Comprising the steps that a congestion relieving dynamic timing algorithm is adopted in a congestion area, the number of times of signal lamp switching is reduced, a flow balance guiding timing algorithm is applied in an area with small flow, in addition, cooperative control over signal lamps between the areas can be achieved, and efficient connection and balanced distribution of traffic flow between the areas are ensured.

The invention relates to the technical field of smart city traffic management, in particular to an urban traffic jam intelligent prediction and dispersion system and a method thereof, and the system integrates a data acquisition module, a traffic prediction module, an event influence evaluation module and an intelligent dispersion module. The data acquisition module updates multi-source traffic data in real time, and a solid foundation is provided for prediction; and the traffic prediction module accurately predicts the traffic flow by using a deep learning model combining a graph neural network and a space-time attention mechanism based on the received data. The event influence evaluation module analyzes the influence of emergencies on traffic and ensures the pertinence of a dispersion strategy; the intelligent dispersion module optimizes a traffic signal lamp control scheme in real time by using deep reinforcement learning in combination with the prediction result and the event influence; the system comprehensively senses the traffic condition through multi-source heterogeneous data fusion, the traffic flow prediction accuracy is remarkably improved, and intelligent and efficient traffic management is achieved.

The invention provides a high-precision perception-driven intelligent road network collaborative optimization method and system, and relates to the technical field of intelligent traffic, and the method comprises the steps: collecting road sensing data through multi-sensor fusion, and obtaining multi-source perception data; performing dynamic traffic flow modeling according to the multi-source sensing data to obtain a traffic state prediction result; performing optimization decision on the traffic state prediction result to obtain an intelligent optimization decision; performing road network level cooperative control on the intelligent optimization decision to obtain a road network level cooperative control result; and performing real-time dynamic adjustment by using the road network level cooperative control result to obtain a road network scheduling result. According to the method and the device, the technical targets of global collaborative scheduling, improvement of traffic flow prediction precision, optimization of a real-time dynamic scheduling scheme and improvement of the intelligent level of urban traffic management can be realized, and the technical effects of reducing traffic congestion, improving road traffic efficiency, optimizing traffic resource allocation and improving emergency response capability are achieved.

The invention relates to the field of artificial intelligence, particularly discloses an intelligent optimal management system for urban traffic congestion based on artificial intelligence, and aims to solve the problems of congestion and low efficiency caused by response delay, local optimization and low data utilization rate of an existing traffic management system. The system comprises a data acquisition and fusion module, a traffic state perception and prediction module, a decision optimization module, an instruction issuing and execution module and a man-machine interaction and visualization module. Through multi-source data fusion, graph neural network prediction and multi-agent reinforcement learning, traffic flow real-time perception, accurate prediction and adaptive control are realized, congestion is effectively relieved, and the overall operation efficiency and toughness of a road network are improved.

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

The invention discloses an intelligent traffic data processing method and system based on a 5G communication system, and relates to the technical field of traffic data processing, and the method comprises the steps: collecting data based on a 5G communication network; preprocessing the collected traffic data through an edge computing node; real-time prediction and risk assessment are carried out on traffic flow based on the cloud platform; sending real-time traffic information and optimization suggestions to a traffic management center and related vehicles; when an emergency occurs, a rapid response mechanism is triggered through a 5G network, and traffic resources are scheduled. The system collects, processes and analyzes traffic data in real time through the characteristics of high bandwidth and low delay of the 5G communication network and the distributed processing capability of edge calculation, achieves the real-time prediction and management of traffic flow and accident risks, and can improve the road utilization efficiency, reduce traffic accidents and improve the traffic safety through the intelligent decision and optimization control strategy. And the emergency response capability is improved, so that a more intelligent solution is provided for urban traffic management.

The invention belongs to the technical field of intelligent traffic system and artificial intelligence crossing, and particularly relates to a traffic multi-agent simulation decision-making method and system based on a large language model. Road network state data are coded into a three-dimensional feature matrix containing channel dimensions, time dimensions and space dimensions, and joint representation of numerical road network data and text event reports is achieved through a hybrid embedding model. The decision-making layer comprises a dynamic Prompt generator which generates a candidate scheme set based on a four-layer progressive prompt structure; the Monte Carlo tree search multi-objective optimization is executed in cooperation with the decision core module; and the execution layer comprises a cross-language communication bridging device which adopts a gRPC bidirectional stream communication protocol and a Protobuf data serialization scheme to realize millisecond-level data interaction between services and support a hot plug mechanism of a strategy injection interface. According to the invention, dynamic optimization of urban traffic resource allocation and breakthrough improvement of simulation deduction efficiency are realized.

The invention relates to the technical field of traffic mode recognition, in particular to an urban traffic mode recognition system based on trajectory clustering, which comprises a trajectory credibility rejection module, a trajectory semantic clustering module, a trajectory evolution stage recognition module, a dynamic parking judgment module and an urban traffic mode generation module. According to the method, boundary dispersion rate difference analysis is carried out on traffic trajectory data, continuous cross-grid anomalies are identified, unstable sections in the trajectory are effectively eliminated, the spatial direction distribution concentration degree is taken as a basis, the trajectory coverage rate difference of the clustering center in adjacent time periods is calculated, and the spatial evolution trend of the trajectory is quantified; according to the method, dynamic description of a traffic mode in the time dimension is achieved, the misjudgment risk caused by a single time or space index is avoided in combination with double indexes of the space overlapping degree and the parking persistence between track fragments, and efficient identification of urban area travel function types is achieved through similarity matching between track features and travel features.

The invention discloses a metro station non-commuting passenger proportion calculation method based on a random forest model, and the method specifically comprises the steps: selecting a research region, and collecting metro-related built environment data and transportation data; important built-up environment variables are determined through preliminary analysis, and variables influencing non-commuter are added; establishing a prediction model by adopting a random forest algorithm, and dividing a training set and a test set; analyzing the output of the random forest model by applying an SHAP technology, and determining the importance sequence of the influence of each built-up environment factor on the proportion of non-commuting passengers and the action mechanism of the influence of each built-up environment factor; according to the size of the SHAP value and in combination with an actual scene, a numerical matrix of all built-up environment factors around the subway station in the scene is determined; and substituting a model coefficient result into the established environment factor matrix, and calculating the subway station non-commuting travel proportion. According to the method, the prediction precision of the non-commuting passenger quantity of the subway station is remarkably improved, the understanding of non-commuting travel behaviors is deepened, and powerful support is provided for urban traffic management and planning.

The invention relates to the technical field of smart city traffic, in particular to a smart city traffic indication system and a working method thereof. According to the technical scheme, the system comprises a multi-source heterogeneous data acquisition module, a data preprocessing and fusion module, a feature extraction and analysis module, a traffic flow prediction and signal timing decision module and a signal display and communication module, the acquisition frequency is dynamically adjusted according to the traffic flow. Traffic and meteorological data are dynamically collected through multiple types of sensors, the quality of data preprocessing and fusion is guaranteed, features are accurately extracted, signal timing is optimized through reinforcement learning, the display and communication module effectively transmits information and cooperates with surrounding traffic, emergency situations can be dealt with, tail gas emission is reduced, pedestrian waiting time is shortened, and traffic safety is improved. And a basis can be provided for long-term traffic planning, and the traffic efficiency, the environmental protection property and the pedestrian friendliness are comprehensively improved.

The invention discloses an urban traffic optimization and planning system based on reinforcement learning, and relates to the technical field of computer information, and the system comprises a traffic data acquisition unit, a reinforcement learning processing unit and a traffic planning unit. The traffic data acquisition unit is used for regarding target urban traffic as a network and regarding each intersection as a node, and constructing a nonlinear traffic flow dynamic model to update a multi-dimensional tensor at each time step; the reinforcement learning processing unit is used for taking a signal timing matrix of a signal lamp of a road section between every two nodes of target urban traffic as a state space and calculating a corresponding action space when a target reward function is a maximum value; and the traffic planning unit is used for finishing the traffic planning of the target city according to the action space of each road section. According to the method, the global optimization capability, the multi-objective optimization capability, the adaptive learning capability, the data processing capability, the safety and the interpretability of traffic signal control are remarkably improved.

The invention discloses a traffic flow prediction method based on a space-time frequency adaptive embedded layer and multi-scale trend perception attention, relates to the field of traffic, and aims at improving the accuracy and real-time performance of traffic prediction aiming at the limitation of an existing traffic flow prediction model so as to support urban traffic management and decision making more effectively. For the problem, the invention provides a method based on a space-time frequency adaptive embedding layer and multi-scale trend perception attention, which covers the following aspects: the space-time frequency adaptive embedding layer is designed, and space-time adaptive embedding and frequency enhancement are combined to obtain effective representation of data. And a multi-scale trend perception attention mechanism is designed to solve some defects of the existing attention mechanism and further improve the adaptability of the model.

The invention discloses an urban traffic event semantic recognition method based on a knowledge graph, and relates to the technical field of intelligent traffic and artificial intelligence, and the method comprises the steps: obtaining the multi-modal traffic data of urban traffic, and constructing a knowledge graph model; preprocessing and feature extraction are carried out on the multi-modal traffic data, the extracted multi-modal features are mapped to entity nodes of a knowledge graph model, a fusion feature vector is generated, and semantic embedding coding is carried out on the fusion feature vector through a graph neural network; constructing an event inference rule base based on a semantic embedding coding result, and performing multi-layer inference calculation on the fusion feature vector by using a graph convolutional neural network to obtain a matching strength score of the candidate traffic event and a standard event mode in the knowledge graph; and in combination with the event space-time constraint condition and the historical event mode, outputting a traffic event recognition result, confidence evaluation and disposal suggestions. According to the invention, the accuracy and practicability of urban traffic event identification are improved.

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

The invention relates to the technical field of smart city traffic, and discloses a smart city traffic dynamic optimization system and method based on digital twinning. The system obtains urban traffic network multi-dimensional data from a plurality of heterogeneous data sources through a traffic multi-dimensional data acquisition module and integrates the urban traffic network multi-dimensional data into a traffic related data warehouse; a traffic digital twinning model construction module extracts features from the data warehouse to generate a traffic related feature matrix, and a digital twinning traffic dynamic model is constructed according to the traffic related feature matrix to output a theoretical traffic state value; the traffic flow map construction module determines a dimension link map of each dimension and constructs a traffic flow link map; the traffic core feature screening module screens a traffic core feature sequence based on the map; the traffic multi-dimensional optimization analysis module performs multi-dimensional difference analysis on the theoretical traffic state value and real-time actually measured traffic data, and generates a region-level difference coefficient matrix in combination with the core feature sequence; and the traffic event association positioning module can realize accurate management and dynamic optimization of urban traffic.

The invention discloses a multi-dimensional data-driven urban traffic scheduling method and platform, and relates to the technical field of traffic scheduling, and the method comprises the steps: obtaining the distribution information of urban traffic facilities, and determining a traffic key region set; a fixed source detection device is arranged, and a key area multi-dimensional traffic operation data flow set and a road traffic multi-dimensional real-time data flow are obtained; acquiring a traffic operation associated index set, and performing preprocessing and index feature extraction; performing congestion prediction analysis to obtain urban traffic congestion parameters, performing scheduling analysis on the urban traffic congestion parameters, and determining a traffic scheduling strategy scheme set; scheduling simulation and effect comparison optimization are carried out, and traffic scheduling optimization control is carried out. The technical problems of insufficient traffic scheduling and low operation efficiency in the prior art are solved, and the technical effects of accurately mastering the traffic condition and improving the traffic scheduling and operation efficiency are achieved.

The invention discloses an urban traffic flow pre-judgment system, and the system comprises a multi-source sensing module which is disposed at a traffic node, and collects traffic flow time series data in real time; the multi-source sensing module is connected with the dynamic graph engine, and the dynamic graph engine comprises a time sequence correlation calculation unit which is used for generating a dynamic correlation weight between nodes based on a dynamic time warping algorithm; and a topology fusion unit. According to the urban traffic flow pre-judgment system provided by the embodiment of the invention, the urban traffic flow pre-judgment system effectively captures the time-space dependency relationship of the traffic flow through multi-source data fusion and dynamic graph modeling, and provides real-time data support for congestion early warning and signal control; and the decision support terminal assists a traffic management department to efficiently formulate a scheduling strategy through visualization and interpretability analysis, so that the urban traffic monitoring and regulation efficiency is improved.

The invention relates to the technical field of big data and deep learning, in particular to a big data urban traffic flow prediction method and device based on geographic information and a computer readable storage medium. According to the method, through the technical means of multi-source data fusion, spatial feature extraction, time sequence analysis, external factor dynamic fusion and the like, high-precision prediction of the urban traffic flow is realized. The method has remarkable technical effects in the aspects of data quality, multi-source data fusion, spatial feature extraction, time sequence analysis, real-time performance and the like, provides powerful technical support for urban traffic management, planning and decision making, and has wide application prospects and important social and economic values.

According to the urban traffic automatic simulation plug-in based on the large language model and the implementation method, after a user inputs a request for a road network and a request for vehicle routing by using a natural language, the request is transmitted into a full-process automatic simulation module, and the full-process automatic simulation module carries out simulation; the input analysis agent extracts a key value from a natural language to generate a character string in a Json format, then the character string is spliced, a prompt input simulation input construction agent containing detailed simulation information is generated, the simulation input construction agent calls a packaged tool function, a vehicle route xml file and a road network xml file are generated respectively, and the vehicle route xml file and the road network xml file are connected with the simulation input construction agent. And the simulation execution intelligent body opens the SUMO-gu i to start simulation, and returns a result through a graphical interface. According to the method, the technical complexity of traditional traffic simulation is effectively simplified, a simulation tool is promoted to be transformed from professional modeling to intelligent decision support, and an innovative technical path is provided for optimization and management of a dynamic traffic system.

The invention discloses an urban traffic flow space-time distribution prediction method based on multistage road operation information, and relates to the technical field of traffic flow prediction, and the method comprises the steps: S1, traffic flow information preprocessing, S2, employing channel attention and space attention mechanisms, constructing a space convolution network module, extracting space features, and carrying out the prediction of the spatial and temporal distribution of the urban traffic flow. The method comprises the following steps: S1, constructing a time cycle network module for capturing long and short term time dependence characteristics, and extracting time sequence characteristics, S4, constructing a connection layer module for regression prediction, and realizing grid area traffic flow space-time distribution prediction, and S5, constructing a traffic flow characteristic clustering and residual regression prediction module, and realizing traffic flow space-time distribution prediction of each road section in a grid. The urban traffic flow space-time distribution prediction method based on the multistage road operation information can flexibly adapt to the requirements of different cities, different road network scales and different prediction granularities, and is suitable for various intelligent traffic management systems such as intelligent traffic scheduling, congestion early warning and green travel guidance.

The invention provides an urban road traffic risk prediction method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining the multi-source data of each road of a to-be-predicted target city, and carrying out the preprocessing of the multi-source data, and obtaining the standardized data; on the basis of a trained time-space sequence deep learning model, traffic risk prediction is carried out on the standardized data, the traffic risk probability of each road of the target city in the future time period is obtained, and the time-space sequence deep learning model comprises a convolutional neural network CNN model and a long short-term memory network LSTM model which are connected in series. According to the method, the acquired multi-source data is preprocessed, the generalization ability and adaptability of the model are enhanced, traffic risk prediction is performed on the standardized data based on the trained time-space sequence deep learning model, a complex mode can be automatically learned from a large amount of time-space data, the risk prediction accuracy is improved, and convenience is brought to urban traffic management.

The invention relates to the technical field of road settlement monitoring and early warning, in particular to a road settlement monitoring analysis and early warning system based on artificial intelligence. Comprising a monitoring area division module, an influence factor monitoring analysis module, a road state monitoring analysis module, a soil layer state monitoring analysis module, a settlement area identification module, a settlement degree analysis module, an early warning execution module and a display terminal. Through cooperative work of a plurality of modules such as monitoring area division, influence factor analysis, road state and soil layer state monitoring and the like, a potential settlement risk area can be found and identified in time, and corresponding early warning and execution instructions are automatically triggered according to different settlement degrees, so that the settlement risk is effectively improved. Therefore, the traffic of vehicles is limited, the ground load is reduced, and time is won for first-aid repair and reinforcement work, so that the safety of roads and infrastructures is effectively protected, and the smoothness and safety of urban traffic are improved.

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.