10 results about "Street network" patented technology

The invention discloses an urban healthy community space intelligent assessment method and system, and relates to the technical field of urban space intelligent assessment and healthy community planning, and the method introduces space grid division, street network modeling and graph theory analysis technologies, achieves the high-precision modeling and reachability simulation of an urban community space, and achieves the intelligent assessment of the urban community space. And the coverage capability and the service balance of the 15-minute walking life circle of residents can be accurately evaluated. According to the method, the reachable path network taking each grid unit as a starting point can be dynamically constructed, and the service accessibility level and the comprehensive service scoring index of each grid are quantitatively evaluated according to the quantity vector of the resident reachable service facilities. By setting dual thresholds of coverage rate and score, a'potential function blind area 'is intelligently identified, and a'cold space grid' is accurately discriminated by further combining a resident popularity activity sequence, so that scientific support and decision basis are provided for subsequent city updating, service facility layout optimization and community health space transformation.

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 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 relates to the technical field of network planning, and discloses an intelligent wireless network networking method and system, and the method comprises the following steps: 1, obtaining basic spatial data, binding the length and trend of a street to a street network, and calculating the geodesic distance and tangential direction of any two points; 2, selecting anchor points, constructing a minimum spanning tree, and mapping to a street network to form a return skeleton tree; step 3, generating candidate stations along streets according to step length, and supplementing and screening at intersections to form an initial station set; step 4, performing geodesic Voronoi division, and newly adding sites to obtain a first site set; step 5, marking backbone nodes and access nodes and distributing father nodes; step 6, constructing a coupling adjacency graph and adjusting sites to generate a second site set; and step 7, detecting coverage, isolation and communication sufficiency, and outputting a final site set when the coverage, isolation and communication sufficiency are all satisfied. According to the invention, intelligent wireless network networking with high coverage rate, high isolation and high connectivity is realized.

The present application relates to the technical field of urban development planning, and in particular to an urban development planning optimization method and system based on deep learning and data mining. The method comprises: obtaining multiple sub-street maps based on a street network map and historical traffic data, generating a topology coefficient array and a congestion coefficient array; generating a coherence array based on the topology coefficient array and the congestion coefficient array, normalizing the coherence array to generate a coherence coefficient array, and obtaining a target street; obtaining the congestion factor of the target street based on historical traffic data and design information, and generating an optimization scheme combination based on the congestion factor and the coherence coefficient array; obtaining a simulation result through a simulation unit; if the ratio of the simulation result to the optimization target does not meet the set threshold, re-obtaining the coherence coefficient array and the simulation result until the best optimization scheme combination is iterated. The present application solves the problem of insufficient accuracy of the optimization scheme and improves the accuracy of the optimization scheme combination.

The application provides a historical urban area potential street vector identification method based on axis extraction. The method comprises the following steps: step one, historical urban area map data acquisition and processing; step two, division of calculation units and preliminary evaluation; step three, street axis extraction based on a triangular mesh and parameter rules; and step four, street axis optimization and output. The application can set rules for the existing planning framework and street layout, explore potential street axis lines with traffic conditions in the historical urban area, and screen the generated results according to the parameter rules, thereby guiding the generated results. The application can provide quantitative data support for designers to optimize the street network structure without destroying the existing texture of the historical urban area.

Various embodiments relate to techniques for using street lights to indicate emergency or other priority routes along a route in a network of streets. In one example, a controller determines a plurality of street lights along a priority route for one or more vehicles and transmits, to respective street light controllers associated with the plurality of street lights, respective instructions to operate the plurality of street lights in a priority route mode, wherein a street light operating in the priority route mode is visually distinct from the street light operating in an illumination mode used to illuminate areas around the street light.

This invention discloses a collaborative improvement method for horizontal wind speed algorithms in urban street network models. The method includes using high-resolution CFD wind field simulations as near-true values, comparing and verifying wind speeds at different locations in the urban street network model with the near-true wind fields, introducing local roughness and displacement height parameters incorporating building characteristics, and systematically evaluating the impact of combinations of local roughness and displacement height, along with CFD-derived local friction velocity, on wind speed simulation at different scales. Based on the evaluation results, the optimal parameter combination is selected to replace the regional average roughness and displacement height parameters, optimizing the wind speed simulation capability of the urban street network model under non-uniform urban underlying surface conditions. The advantages are: by combining the CFD-derived local friction velocity with local roughness and displacement height parameters, the non-uniformity of the underlying surface height in the city center can be more accurately characterized, thereby precisely simulating the drag effect of urban buildings and helping to obtain horizontal wind speeds closer to reality.

The invention discloses a multi-type street network walkability dynamic optimization method based on reinforcement learning, and the method comprises the following steps: obtaining multi-source road network data, and recognizing and classifying different forms of street networks; setting a two-level action space of general structure optimization and type exclusive optimization according to the network characteristics of various types of streets; a two-dimensional walkability evaluation index system fusing the dynamic behavior result and the static road network structure is constructed, and index fusion and standardized evaluation are achieved; an autonomous iterative optimization mechanism is constructed based on reinforcement learning, a walking comprehensive score is used as a reward function, and an agent autonomously decides and dynamically adjusts a road network state; and realizing three-dimensional visual real-time rendering and multi-terminal interactive comparison of the optimization scheme, and outputting a Pareto optimal strategy set. The problems that in a traditional method, the evaluation dimension is single, optimization depends on manpower, simulation and optimization are disjointed and the like are solved, and efficient, accurate and systematic improvement of the multi-type street network walkability is achieved.

This invention proposes a 6G intelligent reflective surface-assisted street network deployment method and parameter setting method, relating to the field of wireless communication networks, particularly network planning and network planning tools. The intelligent reflective surface is deployed on streetlights, providing services to vehicle users by reflecting base station signals. The main contents are as follows: Obtaining the locations of users, streetlights, and base stations; calculating the channel gain of the intelligent reflective surface; calculating the user availability rate based on channel information; determining the number of intelligent reflective surface units to be deployed based on distance; solving the problem to obtain the base station beamforming vector and the maximum value of the user's minimum transmission rate; solving the problem to obtain the phase shift of the intelligent reflective surface and the maximum value of the user's minimum transmission rate; solving the problem to obtain the deployment location of the intelligent reflective surface and the maximum value of the user's minimum transmission rate; iteratively solving the problem until the increment of the maximum value of the user's minimum transmission rate is less than a set threshold, thus obtaining the deployment scheme.