45 results about "City street" patented technology

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

The invention discloses an emotion perception quantification method considering scene semantic information based on a graph structure, which comprises the following steps of: firstly, performing label conversion preprocessing on an emotion perception data set of a streetscape image, and providing a data source for training an emotion perception model; and carrying out transfer learning by adopting a scene graph generation model based on an attention mechanism to obtain entity representation and entity relationship representation for emotion perception. Through the representations, node features and edge features are generated, and multi-dimensional image features are fused to construct richer graph structure features. Then, screening the features by applying a confidence screening rule, and carrying out feature fusion and training by using a graph convolutional network based on the screened features; the method has the advantages that the emotion-related features in the streetscape image are structured into highly explained entity and relation features, and finally quantitative model training of emotion perception is realized to reveal the relation between the city appearance and the emotion perception, deepen the understanding of the physical appearance of the city street and further assist in decision making of city planning.

The application discloses a flood simulation method based on urban infrastructure and a device thereof, and the method comprises the following steps: collecting street view images of a city, wherein the street view images comprise street view data collected from different urban infrastructures at different angles and different times; classifying and processing the street view data according to the types of the infrastructures, and extracting urban infrastructure data; fusing the infrastructure data and terrain data of the city to obtain fused terrain data of the city; dividing catchment areas of the city based on the fused terrain data and extracting water system data, and establishing a flood simulation model based on urban infrastructure according to the water system data, so that the influence of urban street networks, fence systems, buildings and other infrastructures on urban flood processes is fully considered, the simulation accuracy of a city rainstorm flood model is improved, and urban flood disasters are reduced.

The utility model provides a self-rolling reconnaissance ball for city street battle, which belongs to the technical field of spherical robots and comprises a ball body provided with a containing cavity and a reconnaissance hole; the bracket is rotatably connected with the ball body and is positioned in the accommodating cavity; the camera is connected with the bracket; the driving assembly comprises a gear ring, a gear and a first motor, the gear ring is connected with the support, the gear is connected with the first motor and meshed with the gear ring, and the first motor is connected with the support; and the power supply element is connected with the bracket. The beneficial effects of the utility model are that the first motor drives the gear to rotate and drives the sphere to move relative to the support through the gear ring, so that flexible movement is realized, and the camera can shoot clear pictures; the self-rolling investigation ball for the city street battle can autonomously move in a complex city street battle environment and carry out reconnaissance.

The application provides a multifunctional folding table for a tree pool of a smart city, and belongs to the technical field of urban public facilities and municipal engineering. The multifunctional folding table for the tree pool of the smart city comprises a plurality of support columns arranged around a tree trunk. The plurality of support columns are provided with an auxiliary mechanism. A lifting mechanism for driving the auxiliary mechanism is arranged in the support columns. A compensation assembly is arranged at the bottom of the support columns. An adjusting assembly is arranged at the bottom of the support columns. The compensation assembly and the adjusting assembly are transmissionally connected. The tree pool is used as a carrier, a three-layer rest layer is arranged, and functions such as a rest bench, a storage table top and a fence protection are integrated. The independent structure does not need to be newly added. The tree pool space idle problem is solved without changing the land attribute of the tree pool and affecting the growth of the tree, and the utilization rate of the urban street public space is significantly improved.

The invention discloses a flood simulation method and device based on urban infrastructure, and the method comprises the following steps: collecting streetscape images of a city, the streetscape images comprising streetscape data collected from different urban infrastructures at different angles and in different time; performing classification processing on the streetscape data according to the types of the infrastructures, and extracting the infrastructure data of the city; fusing the infrastructure data and the topographic data of the city to obtain fused topographic data of the city; the method comprises the following steps: dividing water collecting areas of cities based on fused topographic data, extracting water system data, establishing a flood simulation model based on urban infrastructures according to the water system data, fully considering the influence of infrastructures such as urban street networks, wall systems and buildings on the urban flood process, and improving the simulation precision of the urban rainstorm flood model. And urban flood disasters are reduced.

The present application relates to the technical field of plant intelligent identification, and particularly relates to a method for intelligently identifying pollen-allergic plants based on a YOLO network. The method comprises the following steps: collecting video data of pollen-allergic plants in the air in urban streets, and constructing an image data set of pollen-allergic plants in the air; using LabelImg software to label the categories of pollen-allergic plants in the air in the images in the image data set of pollen-allergic plants in the air, and automatically generating an.xml file; training and optimizing a YOLOv8 model, saving the best weight file after training, and obtaining a YOLO intelligent identification model for pollen-allergic plants in the air; inputting video data to be detected or pictures to be detected, detecting the video data, and outputting corresponding target detection results. The method of the present application is used for identifying pollen-allergic plants in urban streets, and provides a reference for urban community layout, selection of greening tree species, and community configuration, and has the characteristics of high identification accuracy.

The invention discloses a city street four-season green vision rate estimation method based on an improved local regression model, and the method comprises the steps: generating street scene sampling points according to road network data, collecting street scene images, carrying out the semantic segmentation of the street scene images through a deep learning model, and extracting a vegetation region in the street scene images. The method comprises the following steps of: calculating the green vision rate of a street scene sampling point in each season, extracting a multi-season normalized vegetation index of the sampling point through remote sensing data, establishing a regression model for the normalized vegetation index and the green vision rate of a specific season by utilizing a local regression model fused with a season weight, and estimating the missing green vision rate by utilizing the normalized vegetation index. According to the method, annual street view data are accurately collected and processed, a remote sensing technology, a deep learning technology and an image segmentation technology are combined, and an effective means is provided for scientific evaluation of urban street greening in different seasons. Meanwhile, the method provided by the invention is high in automation degree, and is suitable for practical application of large-scale urban landscaping evaluation.

1. Name of this design product: Road Crack Sealing and Maintenance Vehicle. 2. Application of this design: This design belongs to the field of road repair equipment and is used for road repair in various working conditions such as urban streets, elevated tunnels and bridges, highways, cement and coal mines, docks and ports, metal smelting, warehouses and runways. 3. The key design features of this product are its overall shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

The application discloses a kind of based on street view data's urban boulevard tree geometry and geographic information automatic acquisition method, belong to artificial intelligence technical field, the method includes: obtaining the street view image data of target area;Wherein, street view image data refers to the street view image containing the boulevard tree in target area, and each boulevard tree corresponds multiple street view images of different perspectives;Tree contour shape image is extracted from the street view image data by preset tree detection model;Based on tree contour shape image, calculate tree pixel size information;Based on the multiple street view images of different perspectives corresponding to each boulevard tree, the tree position information is obtained by calculation;Based on tree pixel size information and tree position information, the real size information of tree is corrected.Compared with traditional artificial investigation and record method, the application greatly reduces the calculation time, improves the efficiency of boulevard tree information acquisition.

1. The name of the design product: street lamp (bamboo rhyme). 2. The use of the design product: for the fixation and display of outdoor lighting and road traffic signs in urban streets, parks, squares, pedestrian streets, residential areas, tourist attractions, commercial areas, etc. 3. The design points of the design product: the combination of shape and pattern. 4. The picture or photo that best indicates the design points: design 1 perspective view. 5. Design 1 is designated as the basic design.

The invention discloses an urban street space safety prediction method based on human-vehicle collision risk assessment, and belongs to the technical field of smart city and public safety. The problems that existing urban street space safety prediction is low in efficiency and poor in evaluation accuracy are solved. According to the method, standardized dynamic-static security features of a target city are obtained, and an undirected graph model is established in combination with a topological structure of streets of the target city; fitting distribution of feature data of each node in the undirected graph model by using a Gaussian mixture model, and optimizing potential function parameters of the undirected graph model through maximum likelihood estimation to obtain a target city street safety probability graph model; establishing a domain adaptive graph neural network, adding a local sample of a target city to train and adjust the graph neural network, and obtaining a street safety assessment model suitable for the target city; and inputting parameters of the street safety probability graph model of the target city into the street safety evaluation model to realize street space safety prediction of the target city. The method is suitable for urban street space safety prediction.

1. The name of the design product: street lamp (open of Davidia involucrata Baill). 2. The use of the design product: for the fixation and display of outdoor lighting and road traffic signs in urban streets, parks, squares, pedestrian streets, residential areas, tourist attractions, commercial areas and other places. 3. The design points of the design product: the combination of shape and pattern. 4. The picture or photo that best indicates the design points: design 1 perspective view. 5. Design 1 is designated as the basic design.

The invention provides an unmanned aerial vehicle cluster control method and cluster system for urban street battle. The method comprises the following steps: dividing an unmanned aerial vehicle cluster into an information detection unmanned aerial vehicle, an information processing unmanned aerial vehicle and a combat unmanned aerial vehicle according to a preset urban street battle demand; the information of the urban roadway is detected through the information detection unmanned aerial vehicle, and detection information is obtained; processing the detection information through the information processing unmanned aerial vehicle to generate a combat control signal; under the condition that the combat control signal is effective, combat is carried out in the city street combat environment through the unmanned aerial vehicle for combat; and under the condition that the combat control signal is invalid, the detection step of the information detection unmanned aerial vehicle and the processing step of the information processing unmanned aerial vehicle are repeatedly and iteratively executed until the combat control signal is valid, and smooth proceeding of the combat task is ensured. Through cooperative operation of the unmanned aerial vehicle cluster, the complex environment in the city street battle is effectively handled, the battle risk is reduced, the battle efficiency is improved, and an innovative solution is provided for modern city battle.

A kind of urban street view information retrieval method, comprising the following steps: S100, street view data acquisition, by Google map API interface, obtain a city full city street view image information and its corresponding geographic latitude and longitude information;S200, street view image visual feature extraction and quantization, the visual feature of street view image is extracted and quantitatively analyzed multidimensionally, constructs comprehensive visual representation system, wherein including: visual entity representation, visual impression representation and visual field structure representation three big categories;S300, the formation of street view data comprehensive training and intelligent search system, the street view visual representation system data set is comprehensively trained using self-organizing mapping neural network algorithm, constructs structured, visual street view feature distribution diagram, forms the core system of intelligent search system;S400, the use of intelligent search system, including street view visual information retrieval, visual display and interactive exploration.Provide intuitive, efficient city street view data query and analysis service.

The invention discloses an urban street quality optimization method and device, a medium and equipment, and the method comprises the steps: obtaining road network data, street scene picture data, interest point data, remote sensing data and community housing rental and sale price data of a to-be-analyzed region, and taking the data as a data set; constructing a street quality index system, and calculating the quality index of each feature in the street quality index system according to the data set; determining the factor weight of each quality index according to the importance score of each quality index for the housing price; according to the accumulative local effect graph of each quality index and the housing price, calculating the optimal interval of each street quality factor construction; and according to the difference value and the factor weight between the current construction situation of each street and the optimal interval, identifying a to-be-improved area of the street quality and an improvement time sequence of each street. According to the scheme, the universality and diversity of the samples can be improved, the working efficiency is improved, large-scale refined street quality evaluation can be conveniently achieved, and the improvement time sequence of the street quality is determined.

The application provides a kind of city drainage pipe network clogging real-time analysis method based on street monitoring, to solve the problem that city rainwater pipe network siltation is difficult to be found in time, siltation position and degree are difficult to determine, professional pipe network monitoring equipment is expensive, and real-time analysis and diagnosis are difficult, the present application combines existing city street monitoring and convolutional neural network (CNN) real-time calculation flood situation, using city rain flood model, monte carlo method, back propagation neural network (BP) coupling model for pipe network siltation and position real-time judgment analysis, in addition, the city drainage pipe network clogging real-time analysis method based on street monitoring provided by the application does not need professional equipment, does not need to add hardware equipment, and can realize real-time calculation of city rainwater pipe network siltation position and degree prediction.

The invention provides an urban street facade transformation method and system, electronic equipment and a storage medium, and relates to the technical field of building design, and the method comprises the steps: obtaining an image set of a to-be-transformed street facade; identifying each image in the image set to obtain an identification result, wherein the identification result comprises an original component set used by the street facade to be transformed; a set of facade components are automatically matched from a target component library according to the recognition result, each facade component in the target component library is associated with geometric data, material attributes and construction data, and the set of facade components comprise facade components matched with all components in the original component set; combining and arranging a group of facade components to generate a three-dimensional reconstruction model; and converting the three-dimensional reconstruction model into a lightweight format file, and sending the lightweight format file to AR equipment through a cloud server to assist construction and installation. By implementing the technical scheme provided by the invention, the effect of improving the transformation efficiency is achieved.

1. The name of the design product: street lamp (moon guitar). 2. The use of the design product: for the fixation and display of outdoor lighting and road traffic signs in urban streets, parks, squares, pedestrian streets, residential areas, tourist attractions, commercial areas, etc. 3. The design points of the design product: the combination of shape and pattern. 4. The picture or photo that best indicates the design points: design 1 perspective view. 5. Design 1 is designated as the basic design.

The application discloses a kind of fusion positioning methods for urban street dynamic scene, comprising the following steps: S1, camera and IMU data acquisition;S2, image feature point detection and tracking;S3, estimate pose transformation;S4, filter out dynamic feature points on image frame;S5, static feature point re-collection;S6, tight coupling optimization of vision and IMU;The application improves the positioning accuracy and robustness in the scene of more dynamic objects such as urban streets, introduces a target detection neural network to filter out feature points on dynamic objects to prevent more outliers from causing significant interference to the visual estimation result, and in the case of too few feature points, re-collect some filtered feature points on vehicles, people and other targets that may be static to avoid losing tracking due to too few feature points. Provides a reliable positioning solution for drones, logistics robots, autonomous vehicles and the like.

The present invention discloses a method for urban street space analysis based on an extreme gradient boosting algorithm, which belongs to the technical field of urban space analysis. The method first converts the road network vector data of the target analysis area into a graph structure containing equidistant road network nodes, and assigns POIs to the nearest road network nodes based on the shortest path algorithm. Then, a heuristic algorithm is used to calculate the proximity centrality and betweenness centrality of the road network nodes, the complexity index of the business structure, and the target POI path reachable number used to measure the spatial distribution characteristics of the business format. The reference set graph structure of these quantitative information is input into an urban street space recognition model pre-trained using an extreme gradient boosting algorithm; the urban street space point set of the target analysis area is obtained; and finally, boundary post-processing is performed to obtain the urban street space range and boundary. The present invention has high recognition efficiency and robustness, and is of great significance for large-scale automatic recognition of the location and range of urban street space.

The invention discloses an optimal touring path optimization selection method for an urban landmark group based on behavior preference, and the method comprises the steps: obtaining urban landmark positions and urban road network data, calculating the shortest touring path in all touring sequences, and taking the shortest touring path as an alternative path; the selection identification degree of the alternative paths is calculated, and sightseeing visual angle attraction evaluation is carried out; acquiring urban bus station data, and carrying out bus connection accessibility evaluation on alternative paths; urban street building distribution and function attribute data are obtained, and interface function richness evaluation of alternative paths is carried out; acquiring point density data of the urban population, and evaluating the visit space vitality of the alternative paths; and finally, on the basis of user behavior preference requirements, comprehensive weighted evaluation is carried out from four dimensions, and the path with the highest comprehensive score is determined as the optimal recommended path. According to the invention, a more comprehensive and personalized urban landmark group touring path can be generated.

This invention discloses a method for optimizing the allocation of urban street green space resources based on lidar, relating to the field of urban planning and management. The method includes: acquiring lidar point cloud data of a target urban street area, preprocessing and classifying it, and extracting vegetation point clouds representing street trees; calculating shape parameters reflecting the external characteristics of street trees, and quantity parameters reflecting the volume and visual greenness of the green space; constructing a comprehensive greening index reflecting the combined relationship between the shape and quantity parameters, and determining key thresholds for matching the shape and quantity parameters through a piecewise linear regression model; and based on the key thresholds, classifying each street within the target urban street area into different types, generating corresponding green space resource optimization allocation strategies. This method achieves collaborative analysis of the shape and quantity indicators of urban street greening, and, based on objective thresholds, classifies them to generate differentiated and human-centered green space resource optimization allocation strategies.

1. The name of the design product: street lamp (fire dance). 2. The use of the design product: for the fixation and display of outdoor lighting and road traffic signs in urban streets, parks, squares, pedestrian streets, residential areas, tourist attractions, commercial areas, etc. 3. The design points of the design product: the combination of shape and pattern. 4. The picture or photo that best indicates the design points: design 1 perspective view. 5. Design 1 is designated as the basic design.

The application discloses a kind of based on behavior simulation severe cold city life activity street ice and snow road surface traffic efficiency evaluation method, belong to severe cold city traffic planning and simulation technical field.Step one: build micro heterogeneous ice and snow road surface environment model;Step two: establish the motion logic of agent based on "speed-friction" coupling;Step three: implantation based on finite state machine FSM fall trigger mechanism;Step four: execute the attribute mutation logic of "dynamic obstacle" of falling individual;Step five: based on collision time algorithm TTC simulation secondary congestion;Step six: multi-dimensional index evaluation of traffic efficiency.The present application is aimed at the problems existing in the evaluation of micro pedestrian simulation technology in severe cold city street, such as road surface attribute homogenization, lack of mutation logic of individual motion state, and inability to restore the space blocking effect after pedestrian falls.

1. Name of the product in this design: Streetlight (Soaring High). 2. Application of this design: for the fixing and display of outdoor lighting and road traffic signs in urban streets, parks, squares, pedestrian streets, residential areas, tourist attractions, commercial districts and other places. 3. The key design features of this product are the combination of shape and pattern. 4. The image or photograph that best illustrates the key design points: Design 1 3D view. 5. Design 1 is designated as the basic design.

The invention relates to an urban street walking space pollen sensitization risk evaluation method and system, and belongs to the technical field of pollen sensitization risk evaluation. In order to solve the problem that the existing sensitization evaluation tool has single evaluation on pollen sensitization influence factors, the method comprises the following steps: acquiring multi-source data information of urban street walking space, and extracting street space features, meteorological features and street vegetation risk source features from the multi-source data information; the method comprises the steps of determining a walking space sensitization risk dispersity index according to street space features and meteorological features, calculating a walking space sensitization risk index according to the walking space sensitization risk dispersity index and street vegetation risk source features, dividing grids, calculating risk index scores of intersection positions of the grids, and drawing a street sensitization risk distribution map. And obtaining an evaluation result of the walking space sensitization risk. According to the method, the influence of wind and air quality on the pollen sensitization risk is fully considered, the vegetation evaluation mode is improved, real-time walking space sensitization risk distribution is generated, and allergic people are helped to know the sensitization risk.

A spectacle eyewear device and methods for using a spectacle eyewear device to attenuate glare experienced by a person while driving a car during nighttime hours. The lenses of the spectacle eyewear device provide spatially non-uniform light attenuation using an optical filtering material that is gradiently disposed along an gradient axis oriented between 10 degrees to 30 degrees away from the vertical axis of the lenses. In some variations the average visible light transmittance in a first quadrant of the lenses is less than 30 percent and is greater than 70 percent in a second quadrant. In some variations the lenses comprise a selectively disposed blue-light reflective coating. In some variations the spectacle eyewear device is configured for use on urban city streets and freeways. In some variations the spectacle eyewear device is configured for use on country roads and two-lane highways.