8 results about "Major road" patented technology

Provided is a pavement distress inspection data analysis system based on Internet of Things and artificial intelligence. The pavement distress inspection data analysis system based on Internet of Things and artificial intelligence comprises: a central server, an inspection planning module, and a distress recognition module. By retrieving a set of distress types from the central server, a plurality of inspection paths including a main road are automatically generated; a comprehensive analysis of traffic flow, pavement distress, etc. on the paths is conducted to obtain priority indexes, efficiency analysis is performed on inspection devices so as to obtain inspection values, and accordingly, allocation of path inspection tasks is performed, thereby forming a dynamic path inspection plan. By performing distress recognition on collected pavement images, distress types and distress levels can be accurately extracted from the pavement images, thereby providing a quantitative distress assessment; and then deduplication-based recognition is performed on pavement images in which pavement distress is present, such that the similarity between a target image and comparison images can be accurately determined, thereby avoiding repeated inspection and improving the efficiency and accuracy of distress inspection.

The invention relates to an abnormal road scene image recognition and early warning system, and belongs to the technical field of computer vision and intelligent traffic. The system adopts a distributed architecture with edge computing and cloud collaboration, introduces a differential geometry theory to construct a road scene representation framework, collects data through a camera sensing unit, and sends the data to a cloud server; the edge computing unit executes preprocessing, and the cloud processing unit completes anomaly detection and early warning. The core innovation of the system is that the manifold learning representation module maps high-dimensional features to a Riemannian manifold space; the Riemannian geometric anomaly measurement module defines a multi-dimensional anomaly index; the multi-scale adaptive analysis module realizes comprehensive detection from macroscopic to microscopic; the abnormal scene library is automatically updated; the multi-vehicle cooperation module supports information sharing and joint decision making, the system achieves the effects that the anomaly detection precision is improved by 25-35%, the environmental adaptability is enhanced by 50-70%, the response time is controlled within 250 ms, major road anomalies can be pre-warned 5-30 seconds in advance, the traffic accident risk is reduced by 30-40% potentially, and the traffic efficiency is optimized.

To provide a traffic information providing device and traffic information providing method that accurately predict the risk of traffic jams occurring on main roads based on changes in the ratio of the number of vehicles traveling on main roads and non-main roads in a target area over a predetermined period, using location information collected from multiple vehicles. [Solution] The traffic information providing device includes a driving information acquisition unit that acquires driving information from vehicles, a map information acquisition unit that stores map information, a calendar information acquisition unit that stores calendar-related information, a weather information acquisition unit that acquires weather information, and an evaluation unit that calculates the ratio of vehicles traveling on each road during the forecast period in a predetermined area, compares this with the ratio of vehicles traveling on each road between the forecast period and past periods, and determines that if the weather information during the forecast period is worse than in past periods, and the ratio of vehicles traveling on roads with lower road levels is higher than that of roads with higher road levels, then there is a high risk of traffic jams occurring on roads with high road levels within the predetermined area.

The invention relates to the technical field of urban inland inundation early warning, and provides an urban inland inundation waterlogging evaluation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining low-resolution DEM data and geographic vector data of a target urban region; the geographic vector data comprises road vector data and river vector data; performing resampling on the low-resolution DEM data to obtain high-resolution DEM data; performing correction processing on the road vector data based on the high-resolution DEM data so as to perform elevation sampling on a starting point and an end point of a main road and perform elevation sampling on a starting point and an end point of a non-main road to obtain road elevation data; performing elevation correction on the river vector data to obtain river elevation data; performing flooding depth calculation according to the road elevation data and the river elevation data; and performing flooding risk assessment based on the calculated flooding depth. According to the method, the accuracy of calculating the waterlogging depth of the urban inland inundation can be improved, so that the accuracy of risk early warning evaluation is improved.

This traffic signal control system comprises: a first acquisition unit that acquires specific vehicle information indicating the traffic state of a specific vehicle traveling on a major road toward an intersection where the major road and a minor road intersect; and a control device. The control device comprises a processing unit that executes a selection process for selecting, on the basis of the specific vehicle information, a control process to be executed by the control device from among a first control process for controlling a signal light installed at the intersection and a second control process for controlling the signal light such that the priority degree for prioritizing passage of vehicles on the major road over passage of vehicles on the minor road is higher than that in the first control process.

The invention discloses an electric vehicle charging demand prediction method, system and device based on dynamic traffic information, and a medium. The method comprises the following steps: obtaining road information of a target area, extracting main roads to construct a road topological structure chart, and establishing a road network coefficient model; calculating the type proportion value of each unit area according to the interest point data, and determining the type with the maximum type proportion value as the functional area type of the corresponding road network node; using the road network coefficient model to calculate a driving path of the electric vehicle from a travel starting point to a travel ending point; calculating the charging demand quantity of the electric vehicle at each path of network node; and mapping the charging demand to a corresponding power grid node to obtain an electric vehicle charging demand prediction result of each power grid node. According to the method, the road topological structure diagram is constructed by acquiring actual road information, the road section impedance value and the node impedance value are calculated based on the road section saturation, and the road network coefficient model capable of reflecting traffic jam and signal lamp delay is established.

The specification provides a coal rock pore network calibration method, device and equipment. The method comprises: obtaining an initial fracture system of a target coal rock, an initial matrix pore of the target coal rock, a preset main road equivalent rule and a preset secondary road equivalent rule; according to the preset main road equivalent rule, the initial fracture system is equivalent to a main road, and according to the preset secondary road equivalent rule, the initial matrix pore is equivalent to a secondary road; extracting micron-scale characteristic parameters in the main road and nanometer-scale characteristic parameters in the secondary road; according to the micron-scale characteristic parameters and the nanometer-scale characteristic parameters, a target coal rock pore network is generated; and the target coal rock pore network is calibrated as a whole to obtain a calibration result. Based on the above method, effective calibration of the target coal rock pore network can be realized.

Some embodiments provide a navigation application with a novel declutter navigation mode. In some embodiments, the navigation application has a declutter control that when selected, directs the navigation application to simplify a navigation presentation by removing or de-emphasizing non-essential items that are displayed in the navigation presentation. In some embodiments, the declutter control is a mode-selecting control that allows the navigation presentation to toggle between normal first navigation presentation and a simplified second navigation presentation, which below is also referred to as a decluttered navigation presentation. During normal mode operation, the navigation presentation of some embodiments provides (1) a representation of the navigated route, (2) representations of the roads along the navigated route, (3) representation of major and minor roads that intersect or are near the navigated route, and (4) representations of buildings and other objects in the navigated scene. However, in the declutter mode, the navigation presentation of some embodiments provides a representation of the navigated route, while providing a de-emphasized presentation of the roads that intersect the navigated route or are near the navigated route. In some embodiments, the presentation shows the major roads that are not on the route with more emphasis than minor roads not on the route. Also, in some embodiments, the presentation fades out the minor roads not on the route more quickly than fading out the major roads not on the route.