19 results about "Road congestion" patented technology

This invention provides a method, apparatus, and electronic device for road congestion analysis from the perspective of a low-altitude unmanned aerial vehicle (UAV). The method includes: determining a pixel offset height; determining a target road inspection image based on an initial road inspection image to be identified; determining vehicle detection results of the target road inspection image based on the pixel offset height and the target road inspection image; determining vehicle target feature extraction results of a corresponding reference road inspection image for the target road inspection image, and determining target road congestion discrimination data based on the vehicle detection results of the target road inspection image and the vehicle target feature extraction results of the reference road inspection image; acquiring road congestion analysis data when the target road congestion discrimination data indicates road congestion; and determining the road congestion analysis result based on the road congestion analysis data. This invention can improve the accuracy of road congestion analysis.

An accident early warning method, apparatus and system. The method comprises: a server receives road anomaly information indicating a position where an accident occurs, and on the basis of the road anomaly information, determines a first broadcast area to which the accident belongs and a first early warning area, wherein the first early warning area is an area where a vehicle is instructed to report vehicle state information, and the vehicle state information comprises a vehicle speed and is used for determining the degree of road congestion caused by the accident; the server broadcasts, to a vehicle in the first broadcast area, accident indication information that indicates the first early warning area. The vehicle in the first broadcast area performs, at least on the basis of a positional relationship between the first early warning area and the ego vehicle, at least one of the following: sending the vehicle state information to the server, controlling an alert apparatus of the ego vehicle to provide accident information, or controlling the ego vehicle to avoid the accident.

This application relates to a road dredging and desilting device, comprising a frame, a garbage bin, and a protective box. The garbage bin is fixedly installed on one side of the top of the frame, and a desilting component is provided on one side of the top of the frame. The desilting component includes a drive component, a roller brush, and a disc brush. The drive component consists of a battery and a motor. The protective box is provided outside the desilting component, and a high-pressure spray pipe is fixedly installed on one side of the protective box. When performing road dredging operations, this utility model can initially flush the road surface and loosen the silt through the rotation of the disc brush and the rinsing of water. The roller brush is responsible for sweeping the flushed silt into the garbage bin for thorough removal. This device, through the combination of the desilting component and the disc brush, can quickly and thoroughly remove silt from the road surface. Due to its compact design and simple operation, it can be quickly deployed in urban roads, effectively removing silt from the drainage system and preventing road congestion.

The embodiment of the application relates to an automatic start-stop system of an intelligent networked vehicle, and the system comprises a self-vehicle perception module, a vehicle networking communication module, a data storage module, a strategy decision module and a start-stop execution module; the self-vehicle collection module is used for collecting self-vehicle state data; the self-vehicle perception module is used for continuously perceiving other traffic participants around the current vehicle; the vehicle networking communication module is used for receiving front road congestion data, front road signal lamp data and vehicle guiding data; the data storage module is used for storing self-vehicle state, target perception, congestion, signal and guiding data sets; the strategy decision module is used for judging whether an automatic start-stop condition is met according to the data sets on the storage module and sending a start-stop control instruction to the start-stop execution module when the judgment result is that the condition is met; and the start-stop execution module is used for controlling the current vehicle to perform corresponding start-stop operations according to the start-stop control instruction. The application can improve the energy-saving and emission-reducing performance of the vehicle and improve the start-stop configuration flexibility.

This application relates to the field of vehicle processing technology and provides a traffic management system, including: multiple vehicle-end devices, multiple road-end devices, and a cloud control center; each vehicle-end device acquires first vehicle information of a target vehicle and second vehicle information of each of the other vehicles within its monitoring range, and sends the first vehicle information and the second vehicle information to the cloud control center; each road-end device acquires traffic information within its detection range and sends the traffic information to the cloud control center; the cloud control center predicts the traffic status of each road in the target area based on the first vehicle information, the second vehicle information, and the traffic information; and sends road traffic alert information to each vehicle-end device based on the traffic status of each road. Compared with existing technologies, the cloud control center of this system can accurately predict the future traffic conditions of the area and send relevant alert information to the vehicle-end devices to avoid road congestion.

The application discloses a charging decision method and device for an automatic driving vehicle, and the method comprises the following steps: obtaining charging demand information and road congestion information at a current time, and charging station occupation information and building energy consumption information corresponding to a plurality of charging station positions; calculating a plurality of decision-related information corresponding to each charging station position according to the charging demand information, the road congestion information, the charging station occupation information and the building energy consumption information; performing weighted accumulation on the decision-related information to determine a current decision coefficient corresponding to each charging station position; and selecting a charging station position corresponding to a maximum value of the current decision coefficient as a target charging decision position. Thus, multi-target decision of the charging station is realized, a response strategy for predicting accidents based on probability distribution and avoiding increase of power consumption peak value is added, robustness of the decision system is increased, and impact on power grid stability is reduced.

This invention relates to the technical field, specifically a method for laying a flexible, intelligent hydrogen transmission pipeline that enables rapid installation. The pipeline includes a polyethylene pipe, the outer side of which is covered with a second adhesive layer. The polyethylene pipe and the second adhesive layer are bonded together by a fiberglass tape reinforcing layer. The outer side of the second adhesive layer is covered with a first adhesive layer, and the second adhesive layer and the first adhesive layer are bonded together by an aluminum foil layer. The first adhesive layer is then covered with a polyethylene pipe protective sleeve. This invention avoids secondary excavation of the pipeline, does not damage existing roads and facilities, does not harm urban green spaces, avoids road congestion caused by construction, utilizes existing or abandoned pipelines, allows for rapid installation, requires fewer personnel, is unaffected by weather, and shortens the construction period.

This application relates to the field of patient guidance technology, specifically a patient guidance method and system based on a self-service robot. The system employs a path planning + real-time status monitoring approach for patient guidance. First, a multi-dimensional planning strategy is used for path planning to avoid potentially congested areas as much as possible. Then, to address potential road congestion, a 3D grid map of the hospital is constructed to achieve precise navigation across floors. Radar-visual perception and a grid occupancy model are deeply integrated to analyze dynamic environmental information in real time. Based on the comprehensive recognition results of the grid ahead, the system intelligently determines the congestion status and dynamically selects a strategy of continuing, dynamically traversing, detouring, or waiting. Dynamic traversal uses a risk-deviation scoring mechanism to balance safety and path efficiency. This effectively solves the obstacle avoidance problem in highly dynamic pedestrian environments, significantly improving patient guidance efficiency, traffic reliability, and patient experience, providing a verifiable and implementable intelligent patient guidance solution for smart hospitals.

The application discloses a traffic congestion prediction method based on GPS data in the technical field of traffic prediction, and comprises the following steps: S1, establishing a basic reference model; S2, importing historical congestion information, marking a pre-congestion area on the basic reference model based on the historical congestion information; S3, correcting the pre-congestion area, updating the pre-congestion area into a congestion area, a smooth area, a first slow-down area and a second slow-down area based on the distribution of the pre-congestion area on the basic reference model and the traffic conditions of the downstream place block corresponding to the place block after the interval time period, and obtaining a prediction model of the interval time period; and S4, uploading vehicle GPS data to the prediction model, obtaining the average driving speed corresponding to the place block based on the place block where the vehicle GPS data is located in the prediction model; the prediction model is more accurate through the correction of the pre-congestion area, the traffic road congestion condition can be predicted through the GPS data of the current time, and the estimated arrival time of the vehicle is obtained.

This application relates to a method and system for dynamic optimization of postal logistics networks based on digital twins and adaptive learning, including: acquiring branch information, vehicle information, and delivery demand information; constructing a virtual simulation model; acquiring historical branch data and obtaining the expected parcel inflow volume of target branches through a demand prediction model; acquiring real-time traffic data, identifying road congestion events, and marking corresponding areas in the virtual simulation model; constructing and solving an objective function to generate an optimized execution plan; collecting feedback data, updating the demand prediction model, and correcting the virtual simulation model. In summary, this application, by constructing a digital twin virtual simulation model and combining it with an adaptive learning mechanism, achieves dynamic optimization of the postal logistics network, effectively improving the dynamic response capability of the postal logistics network to real-time traffic changes and fluctuations in parcel inflow volume. It also optimizes resource allocation efficiency and achieves synergistic optimization of delivery timeliness, transportation costs, and resource utilization.

The application discloses a method and device for identifying road congestion state in real time, equipment and storage medium. Including: obtaining trajectory data corresponding to an idling vehicle equipped with a front image device, filtering a target vehicle driving on a road based on the trajectory data; extracting the front view image of the target vehicle, inputting the front view image into a pre-trained traffic object recognition model, and outputting the traffic object recognition result in the image; based on the traffic object recognition result, filtering an initial congestion image and automatically labeling the traffic light category corresponding to the initial congestion image; based on the traffic light category, inputting the initial congestion image into the corresponding congestion recognition model, and outputting the congestion level determination result of the current vehicle. Through image data recognition and congestion level recognition, the application accurately determines the congestion level, improves the accuracy and real-time performance of congestion recognition, provides strong support for traffic management and optimization, and improves road traffic efficiency.

This invention provides a traffic route planning method and device based on pedestrian and vehicle flow monitoring, relating to the field of intelligent transportation technology. It identifies pedestrians on a test road segment using a real-time target detection model, classifies them, and assigns them hazard weights. Combining vehicle flow, pedestrian flow, and hazard weights, it determines a real-time pedestrian hazard index. A pre-set neural network captures the spatiotemporal dependency of pedestrian and vehicle flow, obtaining predicted values ​​for vehicle speed and traffic flow. Based on the predicted values, hazard index, and total pedestrian flow, it calculates the optimal green light duration for balancing pedestrian and vehicle traffic. Then, based on parameters such as the theoretical maximum capacity, it calculates the actual capacity and marks saturated congestion nodes. Combining the green light duration with road topology, it constructs a simulation model to generate candidate routes. Finally, it determines the optimal route based on the total travel time and the average hazard index. This method can balance pedestrian safety and vehicle traffic efficiency, achieving a dynamic balance between pedestrian and vehicle interaction and effectively alleviating road congestion.

This invention relates to the field of big data processing technology, specifically disclosing an artificial intelligence-based method and system for optimizing municipal engineering construction plans. The method includes the following steps: S1: Obtain the total length of the construction section, periodically obtain the traffic flow of the construction section, calculate the traffic flow threshold based on the total length of the construction section and the number of remaining lanes, and mark the congestion cycle; S2: When a congestion cycle is detected, obtain monitoring images of the two intersections with the shortest distances from the start and end points of the construction section, obtain the changing patterns of the intersection traffic lights, and determine the number of vehicles waiting to enter based on the direction of the directional arrows; S3: Calculate the theoretical traffic flow and the comprehensive traffic flow, calculate the overflow traffic flow, and adjust the changing patterns of the intersection traffic lights. By judging the road congestion situation based on the traffic flow of the construction section, the corresponding two intersections are linked, and the changing patterns of the intersection signals are reasonably adjusted to reduce the probability of congestion in congested sections and avoid regional traffic paralysis.

This invention relates to the field of intelligent transportation technology and provides a congestion analysis method and system based on vehicle operating status. The congestion analysis method and system provided by this invention establishes intermittent flow operation models and continuous flow operation models, combines interfering vehicles into operating units, and performs combined analysis to obtain the traffic volume and the speed and density of moving units on a road segment based on the current traffic flow state. Furthermore, the road segment is segmented into sections to analyze changes in traffic volume and moving unit density. Based on the analysis results, the road congestion status is determined, thus solving the problems that current simple average traffic speed cannot accurately represent congestion, and that the calculation of the capacity of intersections and road segments is also relatively idealized, subject to many disturbance factors, and cannot closely reflect reality.

This invention relates to a road congestion control method and system based on high-resolution remote sensing imagery, belonging to the field of traffic control technology. It addresses the technical problems of existing technologies, such as the limited coverage of ground-based sensing equipment and the low precision of traditional remote sensing techniques, which fail to fully extract the spatial details and texture features of high-resolution remote sensing images, thus failing to meet the requirements of refined road congestion control. The method involves acquiring high-resolution remote sensing images of urban road areas and preprocessing them; extracting road areas from the images and identifying traffic targets under the constraints of the road area images; calculating traffic flow parameters; constructing a congestion index calculation model to calculate the congestion index; classifying dynamic congestion levels based on the congestion index; and implementing road congestion control according to the dynamic congestion levels. This invention aims to achieve accurate and reliable road congestion control.

This invention relates to the field of overhead conveying and discloses an intelligent overhead conveying system for garment production. The system proactively screens and isolates abnormal hangers at the source by comparing weight characteristics through a workstation operation trigger module. A global scheduling analysis module generates initial target addresses and process time windows, and a congestion prediction module uses this information, combined with historical data, to predict future road congestion. A path pre-simulation evaluation module and a dynamic path planning module work together to construct a multi-objective evaluation function based on congestion index and energy consumption, generating the optimal conveying path and achieving a comprehensive balance between efficiency and energy consumption. A fault response processing module dynamically blocks faulty road sections and automatically replans the path when a fault is detected. The monitoring and feedback module forms a data closed-loop optimization prediction algorithm. This system effectively solves the problems of abnormal conveying, path congestion, and delayed fault response, significantly improving conveying efficiency and intelligence.

The present application belongs to the field of engineering machinery automation scheduling technology, and specifically discloses a truck precise scheduling method and system based on screen guidance. The method disclosed by the present application comprises: dividing excavators into multiple groups and pre-configuring a target group for the truck; after the truck unloads the earth, the on-board display screen highlights and guides it to drive to the boundary of the target group; when the truck enters the boundary range of the group, a scheduling instruction is triggered, and a specific excavator in the group is assigned as the loading target. The present application moves the scheduling point from the long-distance dump site to the group boundary, shortens the predicted distance, and avoids random disturbances such as main road congestion. The present application can be widely applied to truck scheduling scenarios in open-pit mining and large earthwork engineering.

Techniques are described for controlling and / or influencing the routes driven by vehicles in a fleet of vehicles managed by a fleet routing system. The fleet routing system may determine route data for vehicles to perform requested driving trips based on the current and previous driving trips performed by the fleet. For example, the fleet routing system may evaluate the current driving trips being performed by the fleet to determine modified destinations for driving trip requests. The fleet routing system also may modify various route data, map data, traffic data, and the like, including determining additional waypoints, restricted driving regions, and / or distributed driving regions, to influence the vehicles in the fleet to determine driving routes to prevent congestion on roads and at crowded pullover locations, and to improve vehicle safety, efficiency, and traffic distribution.