75 results about "City street" patented technology

The invention belongs to the technical field of city planning, and discloses a dynamic adjustment method for a city update planning scheme and related equipment, and the method comprises the steps: obtaining city street view video data through the normalized cruise shooting of an unmanned plane; preprocessing the urban street scene video data, identifying building facade damage information and traffic information from the preprocessed urban street scene video data, and evaluating a building facade damage level and a traffic influence level; based on a preset building facade damage grade threshold value, a traffic influence grade threshold value, a building facade damage risk weight and a traffic influence risk weight, constructing a weighted risk assessment model, and according to the building facade damage grade and the traffic influence grade, calculating a comprehensive risk score; determining an adjustment strategy according to the comprehensive risk score, so as to dynamically adjust the urban update planning scheme; therefore, the adjustment efficiency of the urban update planning scheme can be improved.

According to the cold region city emotion perception evaluation and measurement method, a cold region city is selected as a research object, and physiological signals and subjective scores are comprehensively considered; street view images are scored by training the street view perception model, perception of the street view images is realized, physiological signals and subjective scores are combined to realize comprehensive evaluation of corresponding areas of the cold region city, and a comprehensive evaluation result is less subject to subjective influence and is stable; the method can analyze the influence of the winter street space of the cold region city on the residential emotion perception, accurately measure the quality of the cold region city street space influencing the residential emotion, has the advantages of high efficiency and easy popularization and application, can be widely applied to the field of urban planning living street space quality investigation and measurement, and has a wide application prospect. And a method and a basis are provided for cold region city planning street space optimization practice.

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 urban governance large model detection method and system fusing multi-modal data. The method comprises the following steps: acquiring picture data of a city street view or a city block to obtain initial picture data; obtaining question content needing to be detected; constructing a text description set of urban management violation behaviors according to the question content, and processing the text description set by using a text thinking chain method; performing text feature extraction on the simplified description set; performing feature matching and screening on the initial picture data and the text feature vector; carrying out image feature coding on the initial picture data in combination with a matching result; and performing cosine similarity calculation according to the text feature vector and the image feature vector, and converting the cosine similarity into a problem suspected score to determine whether a city governance problem exists. By implementing the method provided by the invention, the limitation of a traditional city management solution in facing a rapidly changing city environment can be solved, and various city management problems can be automatically identified and processed.

The invention discloses an intelligent street lamp and a road emergency communication system, which integrates two light sources with information transmission and illumination functions into the same street lamp system by combining wireless optical communication with widely distributed city street lamps, adopting a distributed architecture and utilizing a dual-mode fusion light source driving technology. And abnormal information in the road surface data is monitored in real time by using the multi-mode road surface intelligent detection system, so that the accurate sensing of the road state is realized. In a road emergency communication system, a multi-node emergency communication network for information interaction is constructed by combining smart street lamps with light receivers, and efficient and stable information transmission and processing capacity is provided for smart city road emergency communication; the intelligent street lamps are used for deeply mining and analyzing city data, intelligent and personalized decision support is provided for city management, convenience is brought to the life of city residents, and the city management level is greatly improved.

The invention discloses a high-precision automatic identification and classification method for styles and features along a street based on a three-dimensional topology network. The method comprises the steps of current street scene image data acquisition, three-dimensional form point cloud data preprocessing, three-dimensional topology network construction, street type identification, type identification result verification and man-machine interaction display feedback. According to the method, street three-dimensional space forms are automatically identified and classified by using point cloud data of the street three-dimensional space forms and adopting a graph neural network technology. According to the method, street space type identification in the urban planning field can be coped with, a street three-dimensional space form type database based on the graph neural network technology is established, automatic identification and classification of street three-dimensional space form types are realized, the accuracy of urban street three-dimensional space identification and classification is improved in an accurate quantification mode, and the method is suitable for popularization and application. And a powerful support and a reliable basis are provided for refined management and control of the urban street space three-dimensional form.

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.

1. The name of this design product: Outdoor intelligent unmanned sanitation robot. 2. Purpose of the product with this appearance design: This product is suitable for intelligent cleaning of public places such as urban streets, crosswalks, parks, squares or closed parks. 3. The design key point of this appearance design product lies in the combination of shape and pattern. 4. The picture or photo that best illustrates the design points: Stereoscopic diagram 1.

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 invention discloses a city street scene vehicle detection method based on YOLOv7-coupling, and belongs to the technical field of vehicle detection, and the method comprises the following steps: S1, obtaining a city street scene image containing a vehicle in history; s2, a YOLOv7-coupling model is constructed, and the YOLOv7-coupling model is constructed; s3, training a YOLOv7-coupling model on the basis of the historical urban streetscape images containing the vehicles until convergence; s4, detecting vehicles in the urban streetscape based on a YOLOv7-coupling model; according to the method, richer gradient flow information can be obtained, the key point detection precision is improved, meanwhile, the model complexity and the omission ratio can be effectively reduced, and the small target detection precision is improved.

The invention relates to an urban traffic atmospheric particulate concentration prediction method, device and equipment and a medium. The method comprises the following steps: acquiring a plurality of urban streetscape images, corresponding geographical location information and PM2.5 concentration of a traffic road section; calculating and determining a fluctuation ratio in PM2.5 pollution and a recovery ratio after PM2.5 pollution corresponding to the traffic road section where the urban street view image is located based on the PM2.5 concentration, and calculating and determining a PM2.5 road section elastic coefficient corresponding to the traffic road section where the urban street view image is located based on the fluctuation ratio in PM2.5 pollution and the recovery ratio after PM2.5 pollution; and constructing a sample data set based on the geographic position information, the urban environment characteristic factor, the fluctuation ratio, the recovery ratio, the PM2.5 road section elastic coefficient and the PM2.5 concentration corresponding to the PM2.5 road section elastic coefficient, and predicting the PM2.5 concentration corresponding to the traffic road section where the target urban streetscape image is located by adopting an atmospheric particulate concentration prediction model trained by the sample data set. According to the invention, the prediction precision of atmospheric particulate concentration prediction can be significantly improved.

The present application relates to an intelligent city early warning and drainage system, and to the technical field of urban drainage, including a main pipeline, a branch pipeline, and a water storage tank. A drainage mechanism is arranged on the water storage tank, and the drainage mechanism includes: a drainage pump, which is arranged in the water storage tank; a drainage pipe, which is arranged on the drainage pump and connected to the main pipeline; and a water level detector, which is arranged on the water storage tank and electrically connected to the drainage pump. In the present application, when rainwater enters the water storage tank, and the water level detector detects that the water level exceeds a specified value, the drainage pump is started, and the accumulated water in the water storage tank is discharged into the main pipeline through the drainage pipe. At the same time, the water flow discharged from the drainage pipe has a certain thrust, and the water flow pushes the water flow in the main pipeline to move, thereby further accelerating the speed of draining the accumulated water, reducing the probability of waterlogging in urban streets, and thus reducing the probability of traffic paralysis and people's property losses.

The invention relates to the technical field of eco-friendly street lamps, in particular to a bird-friendly city street lamp which comprises an environment acquisition module, a feeding auxiliary module, a security defense module, an intelligent lighting module, an execution feedback module and a data storage module. According to the method, the biological environment data are identified to obtain the biological feeding data, the biological feeding data are analyzed to obtain the feeding control instruction, adaptive adjustment is performed according to the biological feeding condition, the feeding amount is dynamically adjusted, dependence of birds is avoided, and the ecological vitality is improved; the natural enemy behavior data is analyzed to obtain a biological threat state, and when the biological threat state is correspondingly threatened, a corresponding natural enemy driving instruction is generated; and the biological data is acquired and analyzed to obtain the biological state information, and when the biological state information corresponds to the returned bird nest, the corresponding bird nest control instruction is generated, so that the safety of the inhabiting birds is effectively protected by monitoring the natural enemies and arranging the protection door on the bird nest.

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.

1. The name of the product of this design: Smart shared mobility scooter. 2. Purpose of the product with this appearance design: Traveling on city streets and designated areas, using batteries as the driving power source and driven by electric motors; mainly used for urban transportation or sightseeing, suitable for shared use in specific areas such as communities, scenic spots, and comprehensive parks. 3. The design key point of this appearance design product lies in the shape. 4. The picture or photo that best illustrates the design points: three-dimensional picture.

The invention discloses a distributed street lamp control method and system for urban street lamps, and relates to the field of Internet of Things. The method is applied to a central server, and comprises the following steps: when a load value of a first agent node reaches a threshold value, obtaining an assistance request sent by the first agent node; based on the assistance request, evaluating the computing power resource demand quantity of the first agent node; querying load information of a plurality of idle agent nodes; according to the load information of the plurality of idle agent nodes and the computing power resource demand quantity of the first agent node, service information of the first agent node is sent to the plurality of idle agent nodes for processing, and a plurality of processing results are obtained; and returning the plurality of processing results to the first agent node, so that the first agent node controls the operation states of the plurality of city street lamps according to the plurality of processing results. By implementing the technical scheme provided by the invention, the overload problem caused by excessive data volume of the urban street lamp agent node is solved.

The invention provides an urban drainage pipe network clogging real-time analysis method based on street monitoring, and aims to solve the problems that urban drainage pipe network clogging is difficult to find in time, the clogging position and degree are difficult to determine, and professional pipe network monitoring equipment is high in cost and difficult to analyze and diagnose in real time. According to the method, the submerging condition is calculated in real time in combination with existing urban street monitoring and a convolutional neural network (CNN), and real-time judgment and analysis of the pipe network clogging condition and position are carried out by utilizing an urban rainfall flood model, a Monte Carlo method and a back propagation neural network (BP) coupling model; according to the city drainage pipe network clogging real-time analysis method based on street monitoring, professional equipment is not needed, hardware equipment does not need to be newly added, and real-time calculation of city rainwater pipe network clogging position and degree prediction can be achieved.

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.