420 results about "Urban planning" patented technology

The invention discloses a remote sensing image open vocabulary segmentation method and system based on vision-language interaction. According to the method, the advantages of a multi-modal large language model and a semantic segmentation network based on a visual basic model are cooperatively utilized, language-pixel two-way mapping is taken as a core, five types of marks of images, texts, categories, objects and segmentation are introduced as carriers of cross-modal information, and three types of cross-modal fine-grained information interaction modules are taken as bridges, so that the cross-modal information interaction is realized. Bidirectional mapping and alignment of fine-grained information of the multi-modal large language model and the semantic segmentation network are realized, the open vocabulary segmentation capability of the semantic segmentation model is improved, and the method can adapt to different remote sensing scenes and category definitions. The method has the following advantages: the method has high performance, strong generalization ability and good expansibility, can provide any category of semantic segmentation maps for unlabeled target domain images based on instructions, and has high application value in the aspects of urban planning, map making, disaster response and the like.

The invention relates to an improved NSGA-III-based multi-target geographic site selection optimization method and system, belongs to the technical field of optimization in business management and resource planning, and particularly relates to resource allocation and site selection decision making by using a calculation model. The technology aims to solve the problems of slow scheme convergence, non-uniform solution set distribution and low calculation efficiency when multi-target site selection is carried out under limited resources. According to the scheme, geographic space data is preprocessed, and an initial population representing different site selection schemes is generated; carrying out optimization iteration by adopting an improved NSGA-III algorithm, and calculating a plurality of objective functions such as coverage rate, normalized population density and medical service accessibility; and performing individual selection and population updating by utilizing improved reference point generation and vertical distance measurement. The method is mainly used for improving the efficiency and effect of commercial and municipal decisions such as urban planning, medical resource configuration, energy station layout and logistics network optimization.

The invention relates to the technical field of urban planning, and particularly discloses an urban planning construction land management and control system based on big data, and the system comprises the steps: obtaining geological environment, landform, engineering construction and human activity data through multi-source data collection; multi-source data fusion analysis is adopted to construct a construction land comprehensive evaluation field including geological stability, engineering suitability and environmental bearing capacity; a development suitability partition is generated based on a density peak value spatial clustering method; updating the development suitability level by dynamically monitoring the verification process; according to the method, accurate analysis and early warning of the three-dimensional deformation field are realized, and scientificity and safety of urban planning in a complex geological environment are improved.

The invention relates to the field of intelligent city planning, and discloses a street space quality intelligent evaluation and image generation method and system based on deep learning, and the method specifically comprises the following steps: movably collecting a street space image, and recording the geographic position information; performing perspective correction and multi-view image segmentation on the acquired street image data, and performing spatial matching with geographical location information to obtain a fused image database; and performing semantic segmentation on the image in the fused image database based on a deep learning model, calculating objective and subjective visual perception indexes and facility function indexes of the image, and generating a street space quality evaluation result. The method solves the problems that in the prior art, evaluation dimensions are limited, and an automatic response generation mechanism is lacked, and has the advantages of being intelligent, efficient and comprehensive in subjective and objective evaluation.

The invention relates to the technical field of urban planning, and particularly provides an intelligent site selection method and system based on a spatio-temporal data platform, and the method comprises the steps: collecting multi-source spatio-temporal data of key industry categories of a target region, constructing an enterprise demand feature set based on the multi-source spatio-temporal data, and building a quantitative demand standard based on the feature set; constructing an industrial ecology adaptation model based on the current industrial basic data of the target area, calculating the matching degree between the feature set and the industrial ecology of the target area through the industrial ecology adaptation model, and outputting candidate parks according to the matching degree by taking industrial ecology clustering as a target; establishing a multi-element adaptation relationship between the target enterprise and the candidate parks based on the quantitative demand standard, performing matching verification through a multi-dimensional compliance verification unit, and removing parks which do not meet the constraint condition of the quantitative demand standard; and based on the verification result, outputting a site selection scheme, finally realizing high matching of enterprise demands and public resources, improving site selection efficiency and quality, and assisting sustainable development of regional industries.

The invention provides a multi-conditional constraint fusion generation method and system for generating a planning intention graph. The method belongs to the technical field of urban planning and artificial intelligence crossing. The method comprises the following steps: acquiring sample data containing different territorial and different types of territorial space intention graphs, and cutting a planning graph sample into image segments by using an image cutting technology according to the sample data; generating a detailed description for each image segment by means of a visual model; refining the detailed description of the generated image segment by using a large language model, generating a standardized label group suitable for model training, and forming a multi-modal sample library containing image-text pairs as data to be analyzed; based on the primary screening and secondary screening evaluation results, the model parameters and the control conditions are adaptively adjusted, so that the model can continuously optimize the generation effect according to different design requirements and feedback information, and the adaptability and the flexibility of the model are improved.

The invention discloses a smart city planning three-dimensional scene reconstruction optimization system combined with semantic segmentation, and belongs to the technical field of three-dimensional scene reconstruction optimization. The system comprises a data sensing module which collects aerial images and laser point clouds, and complements a sheltered area to obtain city modeling data; the preprocessing module performs denoising, data registration and data fusion on the city modeling data to generate texture point cloud data; the semantic understanding module realizes multi-modal semantic segmentation of the texture point cloud data through a fine-tuned SAM network and an improved RandLA-Net, and semantic tag data is obtained through restoration and optimization; the reconstruction optimization module constructs an initial three-dimensional grid model based on the texture point cloud data, and optimizes a ground feature boundary and a missing region of the model in combination with a semantic tag; and the output application module converts the optimized model into a standard format and outputs the model. Through deep coupling of semantic segmentation and reconstruction optimization, the precision of the three-dimensional scene model is improved, and reliable support is provided for smart city planning.

The invention discloses an urban planning design method based on a live-action three-dimensional simulation system, relates to the technical field of urban planning, and is used for solving the problem of insufficient technology fusion depth in an existing method. Firstly, an initial data set is formed by collecting multi-source city data; performing cross-modal feature extraction and alignment based on a deep learning model to generate a fusion feature vector; constructing a dynamic three-dimensional city model by using the fusion feature vector, and embedding a GIS spatial analysis module to perform preliminary layout simulation; vR interaction logic is injected into the model, user behavior feedback is simulated, and environment influence indexes are calculated; according to simulation results and index feedback optimization planning parameters, multi-technology collaborative decision making is achieved; outputting an optimization scheme and generating a visual report, and supporting real-time iterative updating; according to the method, the technology fusion depth is remarkably improved, the response delay is shortened to millisecond level, the planning precision is improved, the environmental influence evaluation deviation is reduced, and efficient and sustainable urban planning practice is realized.

The invention discloses a GIS resource management method, system and device and a medium, and belongs to the technical field of resource management, and the method comprises the steps: obtaining a city planning map, constructing a city three-dimensional model, and collecting multi-source city feature information; constructing a multi-source data fusion matrix of a GIS graph and generating three layers of attribute tags; a spatial gravity model is constructed, and reachability analysis is carried out on urban resources; constructing a dynamic prediction model, and triggering a model iteration mechanism; and generating a resource health degree index based on a model iteration mechanism result, and triggering a resource allocation control signal. The method has the beneficial effects that through multi-source data fusion matrix construction, precise space-time association of geographic space, Internet of Things time sequence and visual feature data is realized, and the integrity and accuracy of data integration are improved; quantitative evaluation and prospective pre-judgment of resource supply and demand are realized based on reachability analysis of a space gravity model and a dynamic prediction model of a space-time diagram convolutional network.

The invention discloses a low-altitude take-off and landing infrastructure grading site selection method, device and equipment, and relates to the technical fields of low-altitude economy, urban planning and the like, and the method comprises the steps: obtaining a low-altitude scene demand, and carrying out the quantification processing, and obtaining demand data; a facility grading system is established based on the demand data, configuration data corresponding to the facility grading system is obtained, and the facility grading system comprises at least three grades; performing overlay analysis on the limiting factor data, and determining a candidate region; and performing hierarchical analysis on the candidate region to obtain suitability distribution data under the low-altitude scene requirement, and performing solution based on the suitability distribution data and the configuration data to obtain site selection data corresponding to the facility grading system.

The invention discloses a grid planning analysis system and method based on a power distribution network, and belongs to the technical field of power distribution network planning, and the method comprises the steps: building a tracing cause model of load abnormality and spatial characteristics through collecting the power load, urban spatial characteristics and power grid historical data with a minimum power supply grid unit as a reference in a planning region; a quantitative incidence relation is determined, load prediction logic is constructed and verified, and a space-time dynamic load curve is generated; meanwhile, extracting three constraints of space time sequence, function linkage and environmental sensitivity in urban planning, respectively converting the constraints and power grid facility parameters into feature vectors, introducing an attention mechanism to calculate a dynamic matching degree, and screening schemes meeting requirements; the adaptive capacity of the power grid is evaluated by simulating scenes such as space function mutation, constraint adjustment and multi-fault linkage, and reverse optimization is carried out on substandard schemes; and finally forming a complete planning scheme including load prediction, constraint matching, an adaptation verification report and a dynamic adjustment rule.

The invention provides an urban rail transit station development intensity multi-objective optimization method in a TOD mode, and relates to the technical field of urban planning and intelligent transportation, and the method comprises the steps: analyzing the spatial relation between various types of land and urban function nodes according to the data after standardization processing, and carrying out the location value evaluation; constructing a development intensity evaluation model according to the location value evaluation, calculating the initial average plot ratio of each type of land, and deducing an initial development intensity parameter in combination with the current situation data; establishing a development intensity optimization target system according to a TOD planning principle, performing iterative optimization on the initial development intensity parameters by adopting a multi-target genetic algorithm, and outputting an optimal solution set; and carrying out comparative analysis on the optimal solution set and a current planning index, and obtaining a plot ratio index in combination with a regional development planning requirement. According to the method, a technical framework of subjective and objective collaborative optimization can be constructed by fusing GIS spatial analysis, an analytic hierarchy process and a non-dominated sorting genetic algorithm.

The invention discloses a public facility reachability evaluation method and system based on geographic multiple agents, and belongs to the field of urban calculation and geographic information. According to the method, a multi-agent system comprising a user agent and a facility agent is firstly constructed, and then a dynamic utility function combined with an evolutionary game theory is introduced to simulate the dynamic decision and congestion avoidance behavior of a user; and user strategies are balanced through multiple rounds of iterative evolution, so that a macroscopic reachability pattern emerging from a microcosmic game is obtained. According to the method, starting from the microscopic individual level, by simulating the dynamic game process of a large number of agents, the defects of a static calculation model are overcome, and dynamic evaluation and predictive simulation of reachability of public facilities are achieved. Compared with the prior art, the evaluation result is more accurate, dynamic feedback of the real world can be reflected, and a scientific decision support tool is provided for urban planning.

The invention provides a remote sensing building group depth detection and change intelligent evaluation method for urban planning, and the method comprises the steps: carrying out the multi-modal data collection of a target urban region, and carrying out the standardization processing, abnormal value elimination, multi-modal alignment and interference suppression processing, and obtaining a multi-modal data set; constructing a multi-branch hybrid network, and performing triplet feature extraction on the multi-modal data set through the multi-branch hybrid network to obtain a building group fusion feature map; performing change area detection and change type classification on the two building group fusion feature maps with different time phases to obtain building group change information; and constructing a dynamic evaluation index based on the urban planning core demand, and performing building group change evaluation in combination with the building group change information. According to the method, through multi-source and multi-modal data acquisition and processing, multi-branch hybrid network triplet feature extraction and change detection and dynamic evaluation index application, urban planning core requirements are adapted, and building group detection precision and change evaluation effectiveness are improved.

The invention provides an unmanned aerial vehicle live-action three-dimensional model construction optimization method and system for urban planning, and relates to the technical field of live-action modeling, and the method comprises the steps: preliminarily constructing a three-dimensional model of an urban region; determining a geometric deviation value of the spatial structure units in the same scene semantic unit according to the geometric boundary features and the image projection errors of the different spatial structure units in the same scene semantic unit; determining semantic conflict loss of the three-dimensional model in a scene semantic extraction process based on a spatial constraint relationship between adjacent spatial structure units and color gradients and transition characteristics of category boundaries; and determining a feature constraint value of the spatial structure unit in the three-dimensional model optimization process through the geometric deviation value and the semantic conflict loss, and carrying out geometric and semantic optimization on the three-dimensional model based on the feature constraint value to obtain a real scene three-dimensional model for urban planning application. By adopting the scheme of the invention, collaborative optimization of the urban area live-action three-dimensional model on geometric accuracy and semantic consistency can be realized.

The invention discloses an urban land identification method based on a remote sensing image, relates to the technical field of urban land planning, solves the problem of insufficient refined features of an original identification mode, and locks core boundary pixels from a complex gradient pixel point set through pixel interval quantization, feature circle construction and refined point screening. The contour precision is obviously improved; compared with a traditional single-line contour, the refined contour can more completely represent the surface feature form, is especially suitable for irregular terrains, and reduces classification errors. According to the method, the pixel mean value and variance features are taken as measurement, the number of combination areas and the similarity of internal features are considered, the optimal combination is screened through weighted calculation, scattered division or excessive combination is avoided, the generated optimal combination areas highly meet the requirements of function aggregation and land cooperation in urban planning, and the planning efficiency and scientificity are remarkably improved.

The invention discloses an intelligent redevelopment research method for low-efficiency land use, which comprises the following steps of: acquiring multi-source data including land change survey data, topographic data, urban planning data, remote sensing data, point-of-interest data, mobile signaling data and social economic data in a research area, and inputting the multi-source data into a land use identification model; obtaining a spatial distribution diagram of low-efficiency land in the research area and corresponding low-efficiency factors; inputting the spatial distribution diagram and the low-efficiency factors into a redevelopment potential calculation model to obtain a redevelopment comprehensive potential value of the low-efficiency land; a plurality of redevelopment schemes are obtained based on the redevelopment comprehensive potential value, the rigid constraint of the low-efficiency land during redevelopment and the redevelopment mode library; and obtaining respective corresponding benefit index difference values after the low-efficiency land is redeveloped by using the plurality of redevelopment schemes, and obtaining an optimal redevelopment scheme from the plurality of redevelopment schemes by using a multi-attribute decision algorithm and the benefit index difference values.

The invention relates to a geographic information technology, and discloses a smart city planning management method and system based on digital twinning, and the method comprises the steps: obtaining a multi-dimensional city data set of a target city, constructing a park demand intensity atlas according to the multi-dimensional city data set, recognizing a preset number of candidate regions through the park demand intensity atlas, and carrying out the recognition of the preset number of candidate regions; the method comprises the steps of obtaining a multi-dimensional city data set of a target city, obtaining a candidate region set, constructing a digital twinborn body of the target city according to the multi-dimensional city data set, obtaining a city digital twinborn model, carrying out traffic accessibility analysis on each candidate region in the candidate region set by using the city digital twinborn model, and obtaining a traffic accessibility score set, and selecting an optimal candidate area in the candidate area set according to the accessibility score set, and performing urban park construction planning according to the optimal candidate area. According to the invention, the scientificity of park site selection in urban planning can be improved.

The invention relates to the field of city planning, and discloses a city spatial evolution prediction system fusing multi-modal data, and the system comprises a data preprocessing and multi-modal knowledge graph construction module which is used for configuring a knowledge graph ontology architecture and preprocessing multi-source city data; establishing a multi-modal knowledge graph based on the preprocessed multi-source city data and the knowledge graph ontology architecture; the model setting module is used for configuring a multi-modal representation learning model; the data integration module is used for a multi-modal representation learning model and performing deep integration on multi-modal data in a vector space to obtain vector representation with remote sensing and streetscape semantic information; and the knowledge reasoning and predicting module is used for performing knowledge reasoning on the missing part in the multi-modal knowledge graph based on the vector representation so as to predict the urban spatial evolution event. The urban spatial evolution event prediction can be realized, and the problem that the remote sensing image and the streetscape image are difficult to be deeply fused is effectively solved.

The invention discloses an intelligent road extraction method based on occlusion perception, and the method comprises the steps: firstly obtaining a remote sensing image of a to-be-extracted road region, and obtaining a feature map integrating global and local information through multi-level deep analysis; for the feature map obtained by coding, utilizing strip convolution operation in horizontal, vertical and double-diagonal directions to establish relevance between cross channels, and generating attention weight to obtain optimized features by combining the multi-direction features and the cross channel relevance; finally, depth features are generated through a decoder, the optimization features and the depth features are fused through jump connection, the reasonability of a morphological structure perception constraint prediction result is obtained through combination of segmentation basic loss, structural continuity loss and boundary precision loss, and automatic road extraction is completed based on multi-level supervision. The method is suitable for automatic road information extraction in the fields of automatic driving, urban planning, geographic information system construction and the like.

The invention provides a public transport supply and demand matching method and a related device, and belongs to the technical field of traffic engineering and urban planning. According to the invention, the matching degree of a supply network and a demand network is calculated through a supply-demand matching evaluation algorithm, and a public transport supply-demand matching index of a spatial analysis unit is obtained; and obtaining a public traffic supply and demand matching result based on the public traffic supply and demand matching index. According to the invention, the problem of low accuracy of public transport supply and demand matching is solved.

The invention provides a spatial-temporal feature extraction and depth portraying method and system for urban industry migration, and belongs to the field of urban planning, and the method comprises the steps: S1, collecting data of each dimension including urban industry migration, carrying out the data preprocessing, and constructing a multi-modal spatial-temporal data set and a dynamic spatial-temporal diagram; s2, extracting and fusing spatial features and time features in the multi-modal spatio-temporal data set based on the multi-modal spatio-temporal data set to obtain fused spatio-temporal features; s3, performing high-dimensional mapping and processing on a deep neural network to generate a deep portrait; s4, predicting the direction and scale of future industry migration based on each city; future possible hot spot areas are predicted.

The invention belongs to the technical field of urban planning and construction, and discloses an evaluation method, system and device based on regional coordinated development. According to the method, a multi-dimensional evaluation index system comprising a target layer, a criterion layer and an index layer is established, after an index value and population data are obtained, a single-index regional coordination development difference index is obtained through a Wilson coefficient calculation function based on population weighting, and then an expert scoring method and an AHP analytic hierarchy process are combined to determine a weight, so that the regional coordination development difference index is obtained. And calculating a regional coordination development difference index to judge a regional coordination development level. According to the method, the problems of imperfect evaluation dimension and insufficient index comparability in the prior art are solved, the current situation characteristics of regional coordination development can be reflected more reasonably and accurately, and a scientific decision basis is provided for optimizing a regional coordination development policy.

The invention discloses an auxiliary decision-making method and system for construction project site selection in urban planning. The method comprises the following steps: acquiring urban multi-source heterogeneous data, construction project types and site selection constraint conditions; performing semantic modeling on the city multi-source heterogeneous data based on a knowledge graph technology to generate a city element knowledge graph; performing semantic retrieval in the urban element knowledge graph based on the construction project type and the site selection constraint condition to obtain a candidate site selection set meeting the site selection constraint condition; respectively evaluating each candidate site selection in the candidate site selection set through a subjective and objective dual-channel fusion scoring model to obtain a comprehensive inappropriate score corresponding to each candidate site selection; and iteratively optimizing the candidate site selection set based on the comprehensive improper score through a greedy extraction heuristic algorithm to obtain an optimal site selection scheme. According to the invention, the decision-making efficiency and quality of construction project site selection can be effectively improved.

The invention discloses a city open space accessibility evaluation method based on Ga2SFCA. The method comprises the following steps: acquiring basic data from a city physical examination platform; preprocessing the image data by using remote sensing software; dividing a research area into grid units, and taking each unit as a demand point; determining a supply point position and calculating an attraction coefficient; obtaining the number of people of each demand unit; performing topology check and repair on the road network data; constructing a Gaussian two-step mobile search method model; introducing a Gaussian distance attenuation function; generating a table according to a model calculation result, and generating a spatial distribution diagram; and rendering the generated surface map layer to complete the visual analysis of the accessibility of the urban open space. The evaluation result can be visually presented through an ArcGIS visual tool, data support and decision basis are provided for urban planning, optimization of open space layout and traffic network design is facilitated, and scientificity and operability of overall urban planning are improved.

The invention relates to the technical field of people flow simulation analysis, and particularly provides a people flow simulation analysis method and system based on spatio-temporal behavior dynamics, and the method comprises the steps: obtaining urban multi-dimensional spatio-temporal heterogeneous data, and carrying out the standardization; constructing a dynamic coupling mechanism of crowd behaviors, spatial constraints and industrial function supply, and extracting travel decision rules and destination selection logic; by taking the mechanism as a constraint, constructing a space-time behavior dynamic model in combination with subject modeling, simulating group flow space-time evolution and outputting crowd flow characteristic parameters; and based on the parameters, predicting the people flow distribution and aggregation dissipation trend of the target scene, and performing visual display. According to the method, through combination of multi-factor dynamic coupling and subject modeling, simulation accuracy and scene adaptability are improved, a prediction result is visual, and scientific decision support can be provided for urban planning, traffic management, public safety guarantee and the like.

The invention belongs to the technical field of building three-dimensional visualization, and particularly relates to an urban building three-dimensional visual lightweight system based on data fusion, which comprises a real image acquisition module, a bridging module and a building object updating module. According to the scheme, through point cloud data comparison and vector analysis, elimination of disappearance building point clouds and addition of newly-added buildings, it is ensured that a non-change part model is unchanged, meanwhile, the large-scale point cloud processing calculation amount is reduced, octree block optimization and nearest neighbor search optimization are adopted, the point cloud matching calculation amount is further reduced, and a verification and model correction mechanism is utilized, so that the accuracy of point cloud matching is improved. Compared with a traditional static city model, the method is more suitable for dynamic scenes such as city planning and disaster monitoring; the height is reduced layer by layer from the view angle of the unmanned aerial vehicle, a middle view angle virtual image is rendered through an initial rough model, the feature difference with the ground view angle is gradually reduced, and the cross-view angle three-dimensional reconstruction quality is improved.

The invention relates to an urban planning management method, system and device based on big data analysis and a medium, and the method comprises the steps: obtaining original urban planning data of multiple departments, and generating an encrypted data set with spatial attributes through homomorphic encryption and spatial grid coding; injecting antagonism abnormal data in the ciphertext space to generate a spatial correlation synthetic data set; normal and abnormal event prediction gradients are fused based on double-branch federal training, and a global prediction model is constructed; the privacy risk and the model precision are dynamically balanced through reinforcement learning, and the fusion weight and the synthesis proportion parameter are optimized; and solving the optimal solution of the infrastructure layout in combination with spatial constraints, and generating an encryption planning scheme. The method breaks through the limitation that privacy protection and data utility are difficult to consider in the traditional technology, solves the problems of abnormal simulation failure caused by encrypted data distortion, precision-safety contradiction caused by static parameter stiffness and lack of multi-target collaborative optimization of a planning scheme, and improves the scientificity and adaptability of urban planning decision.

The invention provides a remote sensing building detection method based on YOLOv8, and the method comprises the steps: S1, collecting a large amount of residential area remote sensing building image data, and constructing a target remote sensing building data set based on the collected image; s2, training the building detection model to obtain a remote sensing building detection model; and S3, applying the trained remote sensing building detection model to remote sensing images of other areas to realize efficient identification and accurate positioning of buildings in the areas. The invention provides a dynamic multi-branch feature extraction module, multi-granularity feature extraction is carried out through a cross-stage channel segmentation-aggregation strategy, a dynamic convolution-routing mechanism is integrated, complex background interference such as vegetation and a rural road network is effectively inhibited, and learning expression of discriminative detail features is enhanced. Through the method, the detail features of the building in the remote sensing image can be extracted more accurately, and powerful technical support is provided for related application fields such as urban planning and building monitoring.

The embodiment of the invention discloses an urban agglomeration hot area prediction method based on a large model data driven graph convolutional network. The method comprises the following steps: acquiring historical travel data corresponding to a city, and determining a historical popularity tensor corresponding to each region of the city based on the historical travel data; based on the historical heat tensor corresponding to each region, determining a comprehensive cost matrix between any two regions among the regions; acquiring public opinion information corresponding to the city, and determining a time aggregation feature tensor of the city based on the public opinion information; and obtaining a space-time tensor of each region based on the time aggregation feature tensor and the comprehensive cost matrix, and determining a predicted popularity tensor of each region based on the space-time tensor. According to the embodiment of the invention, the prediction popularity tensor can more accurately and comprehensively reflect the popularity level which can be reached by each region of the city in the future, and a very valuable decision basis is provided for the fields of city planning, resource allocation, tourism promotion and the like.