6176 results about "Road condition" patented technology

The invention provides an AI algorithm-based scenic spot car intelligent path establishing and planning method and system, and relates to the technical field of path planning, and the method comprises the steps: collecting scenic spot road geographic information data, and constructing a weighted road network model; collecting real-time positioning of the trolley and sensor data; using the reinforcement learning model to generate a real-time optimal path planning strategy; calculating an initial recommendation path; road condition changes are monitored, and a path is dynamically adjusted; and outputting the updated optimal path scheme and carrying out navigation. According to the method, intelligent and real-time path planning of the vehicles in the scenic area can be realized.

The invention provides an intelligent traffic decision-making method based on multi-source sensor data, and the method comprises the steps: obtaining vehicle sensor data and road information, and obtaining a standardized traffic flow feature vector through data synchronization, fusion, feature extraction and dimension reduction processing; inputting the standardized traffic flow feature vector into a traffic flow prediction model, and predicting the traffic flow through a vehicle dynamics model constraint state transition matrix; according to the predicted traffic flow and the real-time road condition information, the road congestion state is judged, and a real-time congestion map is generated; in combination with the real-time congestion map, the road network speed limit rule and the starting and ending point position information, an optimal path is planned, and path smoothing processing is performed through a driving behavior mode; and constructing a vehicle intelligent decision-making system, integrating traffic flow prediction, congestion detection and a path planning model, and outputting a vehicle driving strategy according to real-time vehicle sensor data.

The invention discloses an intelligent driving multi-sensor fusion data processing system, which belongs to the technical field of fusion data processing and comprises a data preprocessing unit, a data fusion unit and a data post-processing unit. The data preprocessing unit normalizes the original data of the laser radar, the camera, the millimeter wave radar and the ultrasonic sensor through a standardization, cleaning, noise reduction and calibration synchronization module; the feature level fusion module deeply digs the characteristics of each sensor, and fuses geometric, visual, motion and close-range obstacle features by using a deep neural network; the decision-making level fusion module generates a driving instruction through weighted voting and fuzzy logic reasoning through classification and situation evaluation; and the data post-processing unit combines the vehicle and road condition optimization instruction, evaluates the risk, and stores the data feedback optimization system. The system can improve the sensing precision and decision reliability, enhances the expansion adaptability of the system, and provides guarantee for safe and efficient operation of intelligent driving.

The invention relates to the technical field of logistics transportation scheduling, in particular to a logistics management system based on intelligent scheduling, which comprises a data acquisition module, a demand analysis module, an intelligent scheduling module, a path planning module, a monitoring and feedback module, an optimization module and the like. The data acquisition module is used for collecting information such as goods, vehicles, storage, customer demands and traffic road conditions; the demand analysis module predicts logistics demands by using a time sequence analysis or machine learning algorithm; the intelligent scheduling module adopts a greedy and dynamic programming mixed algorithm to allocate resources; the path planning module calculates an optimal path according to road conditions and starting and ending points; the monitoring and feedback module monitors logistics parameters in real time and feeds back abnormity; and the optimization module optimizes the scheduling scheme according to the fitness function based on the genetic algorithm. According to the invention, data and resources of all links of logistics are effectively integrated, accurate demand prediction, efficient resource configuration, transportation path optimization, real-time monitoring feedback and overall performance improvement of the system are realized, the logistics cost is significantly reduced, and the transportation efficiency and the service quality are improved.

The invention relates to the technical field of image processing, and discloses a display method and system for a vehicle-mounted LED display screen. The method comprises the following steps: acquiring ambient light intensity data, performing fusion processing to obtain a characteristic value, and predicting ambient light change; identifying a driving scene according to the road condition data, and analyzing the priority by combining the ambient light change data; arranging a display area in combination with the eye movement data, and determining display parameters; analyzing the multi-modal interaction data, determining an interaction strategy, and verifying to generate response data; adjusting display content according to the ambient light features, and outputting a display effect in combination with response data; and energy consumption is estimated by integrating various data, and control parameters are generated to adjust an LED driving circuit. Through intelligent analysis and processing of multi-dimensional data such as ambient light intensity, driving scene and eye movement tracking, dynamic optimization and energy-saving control of display content are realized, and the problem that display effect and energy efficiency are difficult to consider in the prior art is overcome.

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 power distribution operation and maintenance inspection track intelligent planning and optimizing method, and relates to the technical field of power distribution network intelligent operation and maintenance, and the method comprises the steps: collecting the topological data and equipment state information of a power distribution network, and constructing a power equipment association relationship model through an intelligent analysis model; on the basis of the incidence relation model, the inspection importance degree of the power equipment is calculated, and an initial inspection sequence is generated according to the inspection importance degree; and in combination with the initial inspection sequence and the real-time road condition information, constructing a dynamic inspection track network, and generating a final inspection path through an intelligent optimization algorithm. The problems of inaccurate equipment state evaluation, unreasonable routing inspection task planning, insufficient routing inspection path optimization and the like in the prior art are solved. The method has the main beneficial effects that the utilization efficiency of inspection resources is improved, the adaptability of an inspection strategy is enhanced, and the scientificity of an inspection path is optimized.

The invention discloses a vehicle intelligent driving decision optimization method based on deep learning. The method belongs to a vehicle intelligent driving decision technology. Firstly, various sensors are installed on a vehicle to collect data, and data sets are divided according to a proportion after cleaning and marking. A model containing an encoder-decoder and an attention mechanism is constructed, the weight is initialized, and a hyper-parameter and anti-overfitting strategy is set. And training the model by using a specific algorithm, calculating a loss value and evaluating and adjusting according to a verification set. After the model is integrated to the system, a preliminary candidate decision is determined according to a probability threshold, the decision is optimized in combination with a vehicle surrounding environment information set and a risk assessment function, and decision probabilities are redistributed in real time according to environment changes by means of an environment perception feedback mechanism. The method overcomes the defects of the traditional technology, utilizes the advantages of deep learning, effectively improves the driving decision precision, enhances the adaptability of the system to complex road conditions and environments, and guarantees the safe and efficient operation of intelligent driving.

The invention provides a green agricultural product distribution path optimization and inventory control method, and the method comprises the steps: selecting a distribution path with the minimum agricultural product quality influence degree as an optimal path according to a prediction result of the agricultural product quality influence degree, and transmitting the optimized distribution path to a distribution vehicle terminal in real time; after the distribution vehicle terminal receives the optimal distribution route issued by the cloud, the distribution route is navigated in real time through a vehicle-mounted navigation system, a driver is guided to drive along the optimal route, and the distribution route is dynamically adjusted according to the real-time road condition information; meanwhile, according to the real-time position and the driving speed of the distribution vehicle, the arrival time of the agricultural products is dynamically estimated, and the estimated arrival time information is pushed to a destination customer to remind the customer to make preparation for receiving goods in advance.

The present invention relates to the technical field of highway maintenance management, and in particular to a highway maintenance safety management system. The system includes a road condition monitoring module, a traffic load assessment module, a traffic flow redistribution module, a maintenance timing determination module, and a maintenance effect verification module, which are used to monitor multi-layer road condition data of the highway, and perform table-deep correlation detection to obtain road surface abnormal point data; quantify the road surface loss risk of the road surface abnormal point data to obtain road surface loss early warning indicators; collect vehicle images and meteorological data; analyze vehicle model loss contribution based on vehicle images; construct an environmental impact coefficient matrix based on meteorological data; construct a dynamic weight model based on vehicle model loss contribution and environmental impact coefficient matrix to obtain a road surface loss dynamic weight model. The present invention improves the efficiency, benefit and intelligence level of highway maintenance management by organically integrating highway maintenance with traffic management.

The invention provides a food material distribution intelligent control method and system based on big data, and the method comprises the steps: collecting the temperature and humidity of a distribution compartment in a distribution process in real time, collecting the position information and speed of a distribution vehicle, and obtaining the sensitive parameters of distributed food materials in the distribution vehicle; all reachable paths are obtained, a prediction model is used for predicting the remaining fresh-keeping time of the food materials distributed on all the reachable paths according to the current compartment internal environment parameters, the fresh-keeping state data of the food materials and the following path parameters, and the path parameters comprise weather and road conditions; screening out reachable paths of which the difference value between the remaining fresh-keeping time and the arrival time of the reachable paths does not exceed a preset time threshold value from the reachable paths; and obtaining the reachable path with the minimum cost calculated through the environment-sensitive supply chain cost model from the screened reachable paths. By means of the method and the corresponding system, it is ensured that the food materials are delivered at the lowest cost and keep the best freshness after delivery is completed, and the quality loss caused by transportation is reduced.

This application discloses a method, system, and related equipment for intelligent energy management of new energy vehicles. Based on the vehicle's starting and ending points, at least one candidate energy-saving path is determined. The predicted energy consumption of the vehicle along at least one candidate energy-saving path is lower than that of other paths. The total energy consumption is predicted based on road condition information and energy consumption impact information for each path. In response to the selection of at least one candidate energy-saving path, a preset travel route is determined. The preset travel route includes multiple road segments, and the total energy consumption includes the energy consumption of each road segment. With the goal of minimizing fuel consumption along the preset travel route, the engine's operating state is controlled based on the initial state of charge (SOC) of the power battery in each road segment, the energy consumption of the road segment, and the vehicle's actual overall demand, ensuring the engine operates within its high-efficiency range. Using this application can reduce fuel consumption for users and improve the driving experience.

A multi-degree-of-freedom drive-by-wire unmanned vehicle obstacle avoidance path planning method mainly comprises the following steps that based on environment perception information, surrounding obstacles are analyzed and processed, and an environment safety boundary is established; on the basis of an environment safety boundary, speed sampling is carried out based on dynamic window trajectory planning of hierarchical response, and an optimal local trajectory is calculated and selected; in the driving and obstacle avoidance process, according to obstacle distribution and road condition fluctuation real-time information, the posture of a vehicle body is dynamically adjusted, speed sampling is updated, and path planning is updated. According to the method, the optimal obstacle avoidance path can be planned in real time, so that the unmanned vehicle can quickly respond in a complex scene, and potential collision risks can be avoided in time; the characteristics that the multi-degree-of-freedom drive-by-wire unmanned vehicle is suspended and the vehicle body posture is adjustable are fully utilized, vehicle posture adjustment is fused into an obstacle avoidance strategy, and the feasibility of an obstacle avoidance planning track and the stability of vehicle execution are improved.

The invention relates to the technical field of intelligent transportation control, in particular to a tomato transportation speed self-adaptive adjustment method based on path road condition feedback, which comprises the following steps: acquiring road images, vibration waveforms and altitude data through a multi-source sensing unit, and constructing real-time road condition information; identifying a driving mode and extracting a corresponding bumping parameter; detecting the maturity grade of the tomato by combining multispectrum and thermal imaging, and querying a maturity-compressive strength corresponding table to calculate a cargo damage threshold value; constructing a dynamic mapping model under multiple working conditions, predicting vibration response and converting the vibration response into equivalent pressure; comparing the equivalent pressure with a damage threshold value to obtain a safety margin, constructing a speed adjustment decision tree and generating a maximum allowable speed value of each road section; and dynamically generating a segmented variable-speed control instruction based on the speed decision matrix, and controlling a throttle valve and a braking system to cooperatively change speed. The method has the advantages of accurate working condition identification, dynamic fruit adaptation, closed-loop speed regulation and control and the like, and is suitable for fine speed control of a high-sensitivity fruit and vegetable transportation scene.

The invention belongs to the technical field of intelligent monitoring, and particularly relates to a 5G Internet of Things fused video monitoring intelligent early warning method, which comprises the following steps of: acquiring vehicle attribute information and environment perception data, constructing a 5G Internet of Things perception network, fusing a vehicle movement track and road condition environment data through a cross-modal attention mechanism, generating an enhanced environment perception graph, and simultaneously, carrying out early warning on the vehicle movement track and the road condition environment data. Optimizing the transmission efficiency by adopting a dynamic resolution adjustment algorithm; the method comprises the following steps: constructing a dynamic behavior prediction model by using a space-time diagram convolutional network, predicting a vehicle abnormal behavior probability and an evolution trajectory, calculating an early warning level through an adaptive risk quantification algorithm, simulating a risk diffusion coefficient by using a dynamic risk propagation model according to the road section vehicle density, the average vehicle speed and the road traffic capacity, and correcting early warning sensitivity parameters in real time. And transmitting to a command platform, performing situation deduction, triggering a grading early warning instruction, and generating thermodynamic diagram warning information. Therefore, the problems of insufficient positioning precision, weak analysis capability, poor transmission efficiency and the like in the prior art are solved.

The invention relates to the technical field of road condition intelligent perception and application, in particular to a road condition adaptive topology perception and application system and method. A condition cluster perception module monitors road condition parameters in real time; the data transmission communication module realizes differential data transmission strategies; a road section dynamic risk grade evaluation module calculates a road section risk index and grade according to the road structural state parameters and a risk evaluation model; the node adjustment and resource allocation module adjusts the activation state and sampling frequency of each sensor according to the road section risk level, simulates and verifies the implementation effect of a resource allocation scheme under different road section risk levels by using a constructed road system digital twin model, and optimizes the resource allocation scheme by using a multi-objective optimization algorithm; the application service module converts the risk level into a specific service action; the cooperative operation of the modules realizes the flexible allocation of road condition perception, communication and computing resources, and improves the utilization efficiency of the resources and the service efficiency of the system.

The invention discloses an end-cloud cooperative detection method and system for traffic anomalies, and relates to the technical field of intelligent traffic control. The method comprises the following steps: collecting traffic video streams through edge equipment, and identifying abnormal behaviors and generating structured event data by using a lightweight YOLOv3-tiny model; when the confidence exceeds a dynamic threshold and the event type is a high-risk type, uploading a video clip and data to a cloud; the cloud integrates a historical road condition map, meteorological data and a real-time traffic flow state, reconstructs a three-dimensional event scene by adopting a space-time attention pyramid network, and verifies the authenticity of an event in combination with a traffic flow sudden change detection algorithm; and generating a signal lamp forced switching instruction for the risk level overrun event, and issuing the signal lamp forced switching instruction to roadside equipment within 3 seconds to execute emergency response. According to the invention, full-link closed-loop control of traffic accidents from identification to response is realized, and the false alarm probability is greatly reduced while the identification accuracy is guaranteed.

The invention discloses a scheduling information management method and system based on fire fighting of a fire fighting truck, and relates to the technical field of intelligent fire fighting scheduling, and the method comprises the steps: receiving fire alarm information, and collecting real-time fire scene data based on a preliminary fire behavior evaluation result; combining the collected real-time fire scene data with historical fire data, and analyzing a fire spreading trend through an AI fire prediction model; according to the fire behavior spreading trend and the equipment state of the fire fighting truck, dynamically allocating fire fighting truck resources, and planning an optimal driving route; the driving state of the fire fighting truck is monitored in real time, dynamic adjustment is performed based on road conditions and equipment information, and task allocation of the fire fighting truck is updated according to the real-time fire scene situation; in the fire extinguishing process, resources are dispatched in real time for supply according to the material consumption condition of the fire fighting truck. By monitoring the material consumption condition of the fire fighting truck in real time, the supply resources are intelligently dispatched, and the continuity and high efficiency of the fire extinguishing action are ensured.

The invention provides a highway event intelligent association analysis and prediction method, which comprises the following steps: constructing an event time-space association network according to time-space distribution characteristics of events, in which nodes represent events and edges represent association between the events; training the event space-time association network by adopting a graph neural network model to obtain an association evolution rule of the event in time and space; if an event possibly occurring in the future is predicted, other associated events possibly caused by the event are reasoned according to the event space-time association network, and an event causal logic chain is formed; according to the event causal logic chain, evaluating the effects of different coping strategies by adopting a reinforcement learning algorithm to obtain an event handling scheme; in the event occurrence and evolution process, vehicle track data and road condition monitoring data are continuously obtained, and the event time-space correlation network and the prediction model are dynamically updated.

The invention relates to the technical field of intelligent driving, and discloses an obstacle avoidance analysis system for intelligent driving. The system comprises a data acquisition module, a road feature extraction module, a lane type judgment module, a virtual marking generation module, an obstacle detection and trajectory prediction module, a dynamic obstacle avoidance decision module and a system verification and optimization module. The method comprises the following steps: acquiring data through a laser radar, a camera and the like; fusing point cloud and an image to extract the width and curvature of a road; judging the type of a lane according to the width; generating a virtual marking line on a marking-free road, fitting by using a B-spline curve when the curvature is large, fusing the point cloud and the image to detect an obstacle, and predicting a track by using Kalman filtering or a social force model. In combination with virtual marking and risk assessment, a safe trajectory is generated through an OccupanyGrid map, an A * algorithm and MPC, and a simulation and real road test optimization system is adopted, so that the problem of marking-free road trajectory planning deviation is solved, and the obstacle avoidance precision and the road condition adaptability are improved.

The invention belongs to the field of vehicle early warning, and particularly relates to a vehicle hidden danger recognition and safety early warning method and device, and the method comprises the following steps: collecting vehicle speed, tire pressure and road condition data, obtaining the states of key parts of an engine and a braking system through combining an OBD system, and achieving the dynamic perception of a whole vehicle; based on a deep learning algorithm, accurately identifying the too short distance of the front vehicle, fatigue driving, overspeed, area deviation, long-term left-occupying driving of a lane, vehicle retrograde driving and abnormal line pressing tracks; through vehicle-mounted OBD data and cloud large model analysis, potential mechanical faults of tire wear and brake pad aging are predicted, and maintenance suggestions are pushed in advance, so that a driver can be reminded of safety, the driving safety is improved, the hidden danger recognition capability is improved, safety preventive warning is performed on hidden dangers, the driving safety is ensured, and the driving safety is improved. Meanwhile, the vehicle can be guided to avoid high-risk or forbidden road sections, and the probability that the vehicle enters a dangerous or forbidden area is reduced.

The invention provides an unmanned aerial vehicle real-time road condition monitoring method and system and a medium, the method relates to the field of image data processing, and the system comprises an unmanned aerial vehicle which comprises an image acquisition device and a sensor group and is used for acquiring aerial videos and flight parameters of the unmanned aerial vehicle in real time; the streaming media server is used for receiving and storing the aerial video transmitted by the unmanned aerial vehicle; analyzing the video stream into a frame-by-frame image sequence with aligned timestamps; performing target detection on each frame of image, and identifying a target vehicle by using a pre-trained deep learning model; carrying out target tracking on the target vehicle in the continuous frames based on an intersection-to-union ratio matching and trajectory prediction algorithm, and constructing a target motion trajectory; and calculating the displacement distance of the target vehicle in the image based on an optical flow method, feature point matching and a deep learning algorithm, and estimating the actual running speed of the vehicle in combination with the flight parameters of the unmanned aerial vehicle. According to the system, computer vision and sensor technologies are combined, and accurate speed measurement is realized through target detection, tracking and speed estimation.

The invention discloses an accident early warning analysis method and system based on driving data, and relates to the technical field of accident early warning analysis, by extracting a road condition complexity coefficient Cen, a sight distance chaos coefficient Cvs and a dynamic interference coefficient Cdn, the system not only considers the current state, but also analyzes the dynamic interference characteristics of the current state when modeling the external environment, so that the accident early warning analysis accuracy is improved. And the system still has high reliability in the scenes of rain and fog, night, congestion and the like. The comprehensive score value Srisk is not single binary judgment any more, multi-factor fusion interpretation is carried out in combination with information such as operation disturbance, posture deviation and fatigue indexes, a response result WIN with grading characteristics is output, and multi-level early warning from voice reminding to active intervention can be achieved. And the dynamic feedback optimization module constructs a risk response parameter psi stab according to a score fluctuation trend, dynamically adjusts a weight coefficient omega j of the multi-modal model, generates a new coefficient n omega, and realizes self-parameter adjustment and adaptive reinforcement learning.

The invention relates to an artificial intelligence-based cargo transportation path planning method and system for automatic warehouse logistics. The method comprises the steps of performing weighted scoring calculation based on customer historical transaction data and cargo attributes, and generating service label data for distinguishing a high-value hierarchy and a common hierarchy; a differentiation algorithm strategy is called according to the service label, an exclusive transportation path is distributed to the high-value goods, obstacle avoidance is optimized in real time, and adjacent orders are matched for common goods to be merged and distributed; in combination with real-time road conditions and storage scheduling data, through path weight dynamic calculation and genetic algorithm iterative optimization, generating a global transportation scheme considering both time efficiency and cost; customer preference parameters are updated in a closed loop mode based on execution feedback data, and system self-adaptive learning is achieved. According to the method, the problems that differentiated services, dynamic response to environment changes and low-efficiency resource utilization cannot be achieved in a traditional method are solved, the punctuality rate of high-value orders and common orders can be increased, and meanwhile the transportation cost of the common orders can be reduced.

The invention discloses an augmented reality head-up display system and a display method thereof, relates to the technical field of auxiliary driving, and aims at complex road conditions and diversified driving requirements. The comprehensive fusion analysis of the driving environment and the driver state is realized by sequentially executing the four steps of environment perception and multi-source data acquisition, situation analysis and information complexity judgment, dynamic content screening and display strategy generation and real-time monitoring and self-adaptive feedback closed loop. The method comprises the following steps of: marking and normalizing multi-dimensional sensing data to form an environment factor and a driving load factor, and judging the current information complexity by using a nonlinear algorithm; thirdly, screening the most critical content from the information pool according to the priority, and performing refined arrangement according to the display position and style; and if the HUD configuration deviation is monitored and detected, dynamic strategy updating is executed, so that the AR-HUD always ensures the accuracy and the conciseness of information display in different scenes, and the driving safety and the user experience are improved.

The invention relates to the technical field of new energy vehicles, in particular to a multi-working-condition energy management method and system for a hybrid chariot, and the method comprises the steps: determining a corresponding working condition weight coefficient through obtaining the altitude, environment temperature and road condition data in real time, and the real-time working parameters of three power sources, namely an engine, a fuel cell and a lithium battery, so as to achieve the multi-working-condition energy management of the hybrid chariot; the system constructs a multi-objective optimization model based on the data to realize comprehensive fuel consumption minimization, lithium battery SOC balance and lithium battery life protection, and the model comprises establishment of a power source efficiency parameter mapping relation, determination of a corresponding relation between lithium battery power and cycle life, and construction of a membership function of an optimization objective. By adopting a method of combining Pareto local optimization and fuzzy evaluation, the optimal power distribution proportion among the engine, the fuel cell and the lithium battery is calculated, the overall efficiency of the hybrid chariot is effectively improved, and the service life of the hybrid chariot is effectively prolonged.

The invention discloses a tire wear diagnosis method, device and equipment and a storage medium, and the method comprises the steps: generating a tire wear calculation model based on obtained key factors affecting tire wear, wherein the key factors comprise vehicle driving mileage, historical driving road condition information, driving habits, vehicle tire pressure and four-wheel positioning information; and calculating a current tire wear coefficient according to the tire wear calculation model, and diagnosing tire wear by using the current tire wear coefficient. The abnormal condition of tire wear can be effectively identified, a terminal client is reminded of the tire wear state of the vehicle, and the use safety of the vehicle tire is ensured.

The invention discloses an intelligent scheduling and path planning system and method for a mine carry-scraper, and belongs to the technical field of mining, and the system comprises an environment sensing module which is used for collecting the environment data of a mine operation area in real time, and the environment data comprise the equipment state, the road condition and the dynamic obstacle position; processing the collected data to generate environment state parameters; and the task scheduling module is used for allocating operation tasks according to the equipment state, the task target and the environment state parameters, and performing global optimization on task allocation to minimize task completion time and energy consumption. Generating a task allocation scheme based on the equipment state and the task target through a task scheduling module; the path planning module realizes optimal path planning through multi-device game optimization, and the global optimization control module coordinates the work of each module, optimizes global targets such as task completion time, energy consumption and conflicts, realizes deep fusion of scheduling and path planning, and improves the working efficiency, global optimality and robustness of mining devices.

The invention provides a traffic control strategy self-adaption method and system based on simulation feedback, and belongs to the technical field of traffic prediction and control, and the method comprises the steps: firstly obtaining traffic control scene data containing traffic flow data and road condition information, then constructing a simulation evaluation environment, configuring scene parameters based on the traffic control scene data, and carrying out the simulation evaluation environment; the method comprises the following steps: simulating traffic operation states under different traffic control strategies, calling a pre-trained reinforcement learning model to perform simulation evaluation on each strategy in a traffic control strategy set, generating a strategy effect feedback set comprising a traffic operation efficiency index and a traffic order stability index, and according to the strategy effect feedback set, calculating the traffic order stability of the traffic control strategy. And performing parameter adjustment on the strategy in consideration of the index association relationship to obtain an adjusted strategy, and finally outputting the adjusted strategy to the traffic control system to realize strategy updating, thereby effectively improving traffic operation efficiency, ensuring traffic order stability, and realizing adaptive optimization of the traffic control strategy.

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.