213 results about "Urban space" patented technology

The invention discloses an urban flood toughness evaluation method, apparatus and device, a medium and a computer program product. The method comprises the steps of obtaining hydro meteorological data of a to-be-evaluated region and geographic information data of an urban spatial form element layer; setting boundary conditions of a plurality of flood scenes according to the hydrometeorological data, constructing a multi-scene urban flood disaster simulation model in combination with geographic information data, and respectively generating flood inundation depth data of urban spatial form element layers of the plurality of flood scenes; and calculating a first toughness index, a second toughness index and a third toughness index of each flood scene so as to calculate a flood toughness comprehensive evaluation result of each flood scene of the to-be-evaluated region. According to the method, an evaluation system of multiple composite flood disaster scenes is established, different disaster factors and interaction thereof are comprehensively considered, and the effect of key space parameters in urban flood toughness is fully considered, so that the capability of coping with flood disasters in cities can be more comprehensively and accurately evaluated.

The invention provides an urban space intelligent processing method based on multi-modal fusion, and the method comprises the steps: taking multi-modal data as input, and constructing a unified data stream processing and feature alignment mechanism; a physical space is used as a core framework, and the multi-modal data is converted into a space behavior graph with space-time position semantics; constructing an entity attribute-relation type-influence weight ternary interaction model on the basis of an interaction layer of the spatial behavior map, and analyzing a human, object and environment ternary interaction relation in the city and the park based on the ternary interaction model; designing a space intelligent engine with time sequence modeling and dynamic prediction capabilities; and constructing a task processing system. According to the invention, through deep combination of multi-modal fusion and a space intelligent technology, full-link upgrading of urban space from data perception to intelligent decision is realized, and powerful technical support is provided for fine management, efficient operation and safety guarantee of complex space scenes.

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 invention provides an urban carbon neutralization path analysis system and an analysis method thereof. The urban carbon neutralization path analysis system comprises a data acquisition module, a data arrangement module, a region division module, a carbon balance calculation module, a path setting module, a path analysis module and a result output module, according to the invention, through regular rasterization region division, fine modeling of the city space is realized; the carbon sequestration capacity is dynamically estimated by combining multiple types of carbon sources and multiple types of vegetation carbon sequestration factors with an NDVI time sequence index, so that a carbon-oxygen balance calculation result has time sensitivity and spatial resolution; meanwhile, the path analysis module supports a user to set various energy structures, traffic modes and greenbelt lifting parameter combinations to form a multi-path parallel simulation matrix, the selection space of path decision is effectively expanded, the system can realize comparable carbon neutralization evolution analysis according to regions and time periods, and the effects of improving the analysis precision and enhancing the path deduction capability are achieved.

The invention belongs to the technical field of urban planning, and discloses an urban inland inundation toughness evaluation method and related equipment, rainfall event characteristic parameters are obtained through obtaining and analyzing rainfall early warning information, dynamic inland inundation toughness evaluation index weights are generated according to the parameters and a preset weight adjustment rule, and evaluation is executed according to the dynamic inland inundation toughness evaluation index weights. And finally, outputting an evaluation result containing weak link identification information, thereby effectively solving the problems of weight staticization and homogenization in the existing evaluation method. The evaluation index weight can be dynamically adjusted according to rainfall event characteristics and urban spatial heterogeneity, so that accurate evaluation of urban waterlogging toughness is realized, weak links are effectively identified, and a scientific basis is provided for accurate scheduling of emergency resources and effective implementation of disaster prevention measures.

The invention relates to the field of city updating, and discloses a city updating decision-making method based on public participation, which comprises the following steps: acquiring city space basic data including a vector map, remote sensing image data and historical, cultural and geographic information data of a city; on the basis of the acquired spatial data, a public participation base map is constructed, and the base map comprises information of a road network, land use types and historical and cultural areas of the city; collecting opinion data of the public on facility site selection through an interactive platform on the public participation base map; and according to the collected public opinion data, constructing a multi-subject scoring model. According to the method, a decision-making method based on public participation is adopted, space basic data and public opinions of cities are combined, facility site selection is evaluated through a multi-subject scoring model, and the opinions of different subjects can be integrated by the system by setting weight values of the public, experts and the government, so that the diversity and fairness of site selection schemes are ensured.

The invention discloses an urban ventilation corridor construction method based on local climate region classification and remote sensing inversion, and belongs to the technical field of urban climate regulation and space optimization. According to the method, a multi-scale urban thermal environment analysis framework is constructed, a coupling mechanism of an urban form and a thermal effect is systematically identified from macroscopic, mesoscopic and microscopic levels, and fine modeling and regulation and control path identification of the thermal environment are realized. Meanwhile, in combination with machine learning and interpretability analysis methods, scientificity and transparency of thermal environment driving factor identification are enhanced. Through combination of remote sensing inversion, morphological factor construction and three-dimensional analogue simulation, a complete technical path of cold source identification, resistance modeling, path optimization and microclimate response evaluation is provided, and the aeration-cooling potential identification capability of blue-green infrastructures is effectively improved. The method is suitable for wind gallery planning and climate adaptation type update design of various urban spaces, and has wide popularization and application prospects.

The invention relates to the technical field of urban space information processing, in particular to an urban functional area fine classification method based on multi-modal data collaboration. According to the method, POI data, AOI data, land utilization data, remote sensing images and other multi-source heterogeneous data are fused, a multi-dimensional feature system is constructed, and an urban functional area classification model covering a single functional area, a composite functional area, a mixed functional area and a non-functional area is provided. According to the method, firstly, block-level basic units are constructed based on an open street map (OSM), then coordinate transformation and function classification are carried out on POI and AOI data, features such as POI density, AOI area proportion and land utilization structure are extracted respectively, and finally, function type identification is carried out on urban block units according to set classification rules. Compared with the prior art, the method has the advantages that through multi-source information fusion and discrimination logic optimization, the accuracy and applicability of urban functional area recognition are remarkably improved, and the method is suitable for scenes such as urban planning, land utilization evaluation and urban management.

The invention provides an urban space resource optimal configuration method based on an urban information model, and relates to the field of space resource configuration, and the method comprises the steps: S1, data acquisition and preprocessing, S2, dynamic association feature extraction, S3, space-time cooperative supply and demand matching, S4, dynamic demand prediction, and S5, facility layout optimization. According to the method, on the basis of a city information model, iterative optimization of city space resources such as infrastructures in the whole life cycle of planning, construction and management is achieved, the problems that in the construction process or after the infrastructures are constructed, due to uncontrollable factors of city development, the requirements are reduced, and public resources are redundant are effectively solved, and the economic benefits of the infrastructures are improved. The utilization rate of urban space is effectively improved, and the planning and construction cost of infrastructures is reduced.

The invention relates to the technical field of city data representation. The invention discloses an urban space general representation learning method and device based on multi-modal spatio-temporal data fusion, a terminal and a storage medium, which can improve the applicability of urban general representation and enable the urban general representation to be applicable to diversified urban analysis tasks. The method comprises the following steps: acquiring multiple modal spatio-temporal data of each space unit in a target city, and setting a corresponding view for each modal spatio-temporal data; on the basis of each modal spatio-temporal data of each space unit, generating a representation of each space unit in a single view under a view corresponding to each modal spatio-temporal data; generating a multi-view fusion representation corresponding to each space unit based on the representation of each space unit in a single view under the views corresponding to all modal spatio-temporal data, and forming a multi-view fusion representation matrix by the multi-view fusion representations corresponding to all the space units; and performing global aggregation processing on the multi-view fusion representation matrix to obtain urban general representation.

The invention provides an urban space toughness full-cycle optimization regulation and control method and system based on ArcGIS Engine and SOM. The method comprises the following steps: 1) multi-dimensional attribute measurement of urban spatial toughness; (2) revealing space-time differentiation characteristics of urban space toughness full-period evolution; 3) identifying influence factors of urban space toughness full-cycle evolution; 4) constructing an urban space toughness full-cycle optimization regulation and control model based on ArcGIS Engine; and 5) searching an optimal solution of urban space toughness full-cycle optimization regulation and control based on SOM. According to the method, a quantitative analysis model and a tool for urban spatial toughness optimization regulation can be provided, and suitable value and threshold range of urban spatial toughness multi-dimensional variables can be identified, so that a full-cycle optimization regulation strategy for urban spatial toughness is supported; therefore, the ability of the planning system to regulate and control the urban space toughness is enhanced from the aspects of space-time integrity and systematicness.

This application relates to the technical field of data processing and provides a method for constructing spatial scenarios based on a blue-green-grey ratio. The method first constructs a spatial scenario classification system, dividing spatial scenarios into primary and secondary spatial scenarios. Attributes corresponding to vector patches of the secondary spatial scenarios are set as follows: type name, perimeter, area, and blue-green-grey ratio. Spatial data is then preprocessed, followed by the identification of urban functional scenarios and non-urban spatial scenarios. A perimeter and an area of each spatial scenario patch are calculated and attributes of type name, perimeter, area, and blue-green-grey ratio are assigned to each spatial scenario patch. The blue-green-grey ratio attribute is used to represent the internal heterogeneity within homogeneous functional zones or the same spatial scenario. This attribute better explains the differences in land use due to varying locations, addressing the challenge in traditional land use / cover methods.

The invention relates to the technical field of big data analysis, and particularly discloses a city update multi-dimensional dynamic supervision and analysis system based on big data, which is provided with a data quality processing module, a topology verification evaluation module, a conflict repair matching module and an interactive management feedback module. And extracting data from the city multi-source data set and performing coordinate conversion to form a unified city space data set. And then, starting a topology check engine through a data processing port, and detecting and evaluating a topology conflict degree value of the data set. And according to the conflict degree, matching and executing a corresponding repair scheme, and marking the repaired data set as a city effective data set. And finally, the effective data set is uploaded to a city updating interaction port, subsequent interaction management operation is carried out, the accuracy and availability of the data are ensured, and support is provided for a city updating project.

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 relates to the technical field of data processing, and discloses an urban space evolution detection method and system based on machine learning. The method comprises the following steps: carrying out space-time stratified sampling on city multi-scale data to obtain block-level, district-level and city-level feature tensors; calculating a collaborative weight matrix through a multi-scale space attention mechanism; a collaborative feature vector is obtained through mapping of a collaborative order degree encoder; performing time sequence interpolation processing by using a space-time cooperation trajectory decoder to obtain a dynamic cooperation index; and performing multi-level fusion on the dynamic collaboration index to obtain a collaboration measurement result. The problem that urban spatial evolution detection is lack of multi-scale cooperative relationship quantitative analysis and dynamic evolution trajectory recognition capability is solved.

Provided are an urban digital twin platform system and a moving object information analysis and management method therefor. Through the urban digital twin platform system and the moving object information analysis and management method, a moving object such as a vehicle or a pedestrian may be detected from multimodal sensor data, data on the moving object may be generated, and a situation may be quickly determined by deriving complex actions of the moving object. The urban digital twin platform system includes a multimodal sensor data input and objectification module configured to detect a moving object and to generate objectification data, a multimodal sensor data analysis module configured to classify basic actions of the moving object, to classify complex actions of the moving object, and to generate moving object information, and an urban space data server configured to store the objectification data and the moving object information.

The invention relates to the technical field of data processing, and discloses an urban space structure collaboration measurement method and system, and a storage medium. The method comprises the following steps: performing gridding processing on urban space elements through multi-temporal data acquisition to obtain a three-dimensional data matrix; calculating a space-time dynamic coupling coefficient by adopting an improved gravity model according to the three-dimensional data matrix; constructing a space-time bidirectional weighting network through a bidirectional weighting algorithm; a dynamic collaboration degree index DSCI is calculated based on network self-adaption; and determining a collaboration degree threshold value through multi-level nesting identification, and obtaining a measurement report. According to the method, through constructing the space-time bidirectional weighted network and the dynamic coordination degree index DSCI adaptive calculation algorithm, the space-time dynamic coupling relation quantification precision of the urban space structure coordination measurement is improved. Meanwhile, a self-adaptive collaboration degree threshold combination is established through multi-level nesting recognition, and the dynamic adaptability of the collaboration evaluation standard to the urban development stage and policy environment change is improved.

The invention relates to an urban land surface environment dynamic monitoring method and system fused with a remote sensing technology. The method comprises the following steps: acquiring an original data set of a target city; performing data cleaning and standardization processing on the original data set to generate a standardized urban space data set; segmenting a city and town development boundary according to the data set, dividing evaluation units and designing a multi-dimensional evaluation index system; on the basis of the evaluation index system, measuring and calculating each index and determining the weight, and obtaining a three-level evaluation grade for evaluating the spatial quality; and finally, on the basis of the three-level evaluation level, targeted quality improvement countermeasures are formulated according to the spatial quality characteristics of different areas. By adopting the method, the urban space quality difference can be accurately identified, a quantitative basis is provided for urban planning, resource allocation optimization, facility layout improvement, heat island effect alleviation and the like are assisted, refined treatment and sustainable development of urban space are promoted, and the life welfare of residents is improved.

The invention discloses an urban space unit digital segmentation method, and belongs to the technical field of urban planning, and the method comprises the steps: inputting site data, simplifying and integrating the plane contour of a building in a site, and obtaining a site space and the integrated plane contour of the first layer of the building; on the basis of the integrated plane contour of the first floor of the building, according to a site space relation, connecting and screening building end points to nearest points of the building contours to obtain preliminary division control lines; performing constraint denaulay triangulation based on the preliminary division control line, the building contour and the site plane contour to generate a triangulation network unit; and merging the triangulation network units according to the maximum edge merging and concave angle avoiding principle to obtain an urban space division result. According to the method, space division can be carried out on a public space in a relatively complex urban plot, a space unit division result relatively conforming to actual space perception of people is formed, and meanwhile, the calculation efficiency and the calculation accuracy are improved.

The invention discloses an urban space-time evolution analysis method based on multi-source historical data. The method comprises the following steps: 1) acquiring and digitally processing historical city maps, point-of-interest data and realistic paintings in multiple periods; 2) performing syntactic analysis on the urban road network and the water system by adopting a mixed model of topology and angle distance, and calculating core indexes such as integration degree and angle selection degree; 3) based on an optimization algorithm, carrying out normalization processing on the spatial syntactic index and the POI data; 4) extracting dynamic city information from the historical drawing; 5) performing statistical test on dynamic data extracted by drawing and a spatial syntactic analysis result; and 6) analyzing the space-time aggregation mode and function evolution of the public service facility by adopting a location quotient formula and kernel density estimation.By constructing a closed-loop research framework of multi-source data fusion, algorithm optimization and quantitative verification, a highly reliable scientific method is provided for deeply revealing an internal driving mechanism of urban form evolution.

The invention relates to the technical field of three-dimensional rendering, in particular to a digital city design city model visual display system which comprises a purpose calibration module, a component combination module, a path guide module, a view angle scheduling module and a rendering display module. According to the method, more accurate visual representation of urban model space purposes is realized by optimizing purpose calibration in the urban space and careful processing of geographic information, more reasonable space utilization distribution and visual display are realized by dynamically adjusting anchor points of the purpose space, more intelligent decision making in urban planning and building design is supported, and the method has the advantages of being high in practicability and easy to popularize. By extracting and combining building components with different functions, the functional semantic expression of the model is further enhanced, the information value and practicability of the model are enhanced, the interactivity and educational significance of the model are enhanced, and the model is more vivid visually and can reflect the appearance of a real city more accurately due to the improvement of rendering display. And the display quality and attraction of the model are improved.

The invention discloses an air pollution concentration prediction method and system based on a space-time agent, and relates to the technical field of air quality prediction, and the method comprises the steps: abstracting an air quality monitoring station in an urban space into a space-time agent, and constructing an agent network topological graph based on the geographic space proximity and pollution propagation correlation; building an air pollution concentration prediction model based on a message passing network, a spatial-temporal feature extractor and a prediction fusion module; training the air pollution concentration prediction model through the intelligent agent network topological graph, and obtaining a trained air pollution concentration prediction model in combination with a multi-task loss function; and performing space-time agent prediction by using the trained air pollution concentration prediction model, and outputting an air pollution concentration prediction result. The method can improve the precision, real-time performance and robustness of pollutant concentration prediction in a large-scale urban environment in which multi-source data is highly heterogeneous and a pollutant propagation mechanism is complex.

The invention discloses a low-efficiency land use redevelopment function prediction method based on a graph neural network, and the method comprises the steps: S1, obtaining the land use benefit of each space unit in a learning region, dividing each space unit into a high land use benefit type, a middle land use benefit type and a low land use benefit type, selecting a space unit with a high land utilization benefit type as an urban land use configuration case library; s2, taking the urban land use configuration case library obtained in the step S1 as a sample set, and constructing and training a functional feature learning model based on a graph neural network according to the sample set; and S3, obtaining each space unit in the prediction area, inputting the space units into the functional feature learning model obtained in the step S2, and performing low-efficiency land use redevelopment function prediction. By simulating urban space function interaction and mechanism innovation, a traditional land utilization prediction normal form based on static rules and spatio-temporal evolution is broken through, and scientific support is provided for land benefit improvement and function replacement decision in urban updating.

The invention discloses a multi-agent collaborative urban community space planning method and device based on large model driving. The method comprises the following steps: converting original urban space data into a structured community space distribution diagram; a planning agent is constructed, the community space distribution diagram is understood and analyzed by using the planning agent, and an initial city planning scheme is output; dividing the initial city planning scheme into a plurality of community areas, and generating local optimization suggestions by a plurality of community planning reviewer agents; and fusing the community optimization suggestions by a high-level planning agent, carrying out global reasoning and coordination, and outputting a final urban space planning scheme. Through cooperative work of multiple types of agents, the intelligent level, cooperative efficiency and service fairness of urban space planning are improved, and the problems of lack of multivariate value expression in traditional generative planning, high labor cost and slow response in participative planning and the like are solved.

The invention relates to a thermal exposure risk calculation method based on urban trip people flow and built environment dynamics, and the method comprises the steps: obtaining the average radiation temperature calculation input data of an urban environment, and calculating the average radiation temperature of the urban environment according to the average radiation temperature calculation input data; calculating general thermal climate index distribution of the urban environment based on the average radiation temperature, and determining a thermal exposure time period based on the general thermal climate index distribution so as to obtain thermal exposure duration according to the thermal exposure time period; and acquiring the hourly travel pedestrian flow of the urban environment, counting the accumulated travel pedestrian flow in the thermal exposure time period based on the hourly travel pedestrian flow and the thermal exposure time period, and calculating the thermal exposure risk according to the accumulated travel pedestrian flow. Therefore, the problems that in the related technology, due to dependence on static population data, the people flow dynamic state of urban day and night travel with high temporal-spatial resolution is difficult to reflect, and a thermal risk calculation result based on the static population data cannot meet the requirements of urban space environment exposure refined analysis and early warning are solved.

The invention provides an urban building group three-dimensional function partitioning and floor population dynamic distribution method and device, and relates to the technical field of urban space information processing. The method comprises the steps of constructing semantic feature vectors of multi-source POI data based on a BERT model and domain specific vocabularies to generate fusion data; building a floor position probability distribution model according to a three-layer progressive reasoning structure and a reasoning technology; carrying out three-dimensional function partitioning according to the function coupling strength and an improved spectral clustering algorithm; building-level demographic data are generated, and a differentiation attraction model is constructed; and solving the floor population distribution constraint optimization model according to the improved Haff model to obtain population dynamic distribution of each floor. According to the method, granularity limitation of traditional function division is broken through, floor-level refined three-dimensional function division and population dynamic distribution prediction are realized, and important decision support is provided for applications such as commercial site selection, emergency evacuation path planning, public service facility configuration optimization and the like in smart city construction.

The invention discloses an urban space type identification method based on a mixed density clustering algorithm, and relates to the technical fields of smart cities, urban planning, tourism planning, urban traffic and the like, and the method comprises the steps: constructing a data set for urban space type identification for a research area; based on the constructed data set, urban space division processing is carried out by using a DBSCAN density clustering algorithm, and an obtained result is marked as a clustering result D; performing unique clustering processing on each category S in the D based on a C-means clustering or Gaussian mixture model (GMM) algorithm to obtain a central point position P of each category S; counting the number of facility POIs in a local spatial range for the central point position P of each category S, and recording an obtained result as a local spatial information counting result; and determining the type of each category in the clustering result D according to a local spatial information statistical result. According to the invention, clustering of the service point space data set and identification of the urban space type can be realized based on the design of the mixed density clustering algorithm.

The invention discloses a life circle defining method, system and device and a storage medium. The method comprises the following steps: acquiring mobile phone signaling data and residential quarter AOI data; according to the mobile phone signaling data, identifying an individual living anchor point, and based on the residential quarter AOI data, determining a function type of a grid of a visited destination of the individual; and based on the function type, extracting activity places of collective regular access, and delimiting a space range of a life circle. The method is beneficial to improving the accuracy of life circle delimiting, and can be widely applied to the technical field of urban space planning.

The invention discloses an urban planning effect evaluation method and system based on carbon emission, and the method comprises the steps: dividing urban space units, and constructing a first space unit division matrix in combination with information data; obtaining a real-time data score, and adjusting the space unit to obtain space adjustment efficiency; updating the space units based on the space adjustment efficiency to obtain a second space unit division matrix; based on the upstream and downstream industry chains, constructing a directed weighted graph, and obtaining an importance sequence through a link analysis algorithm; in combination with a multi-criterion decision analysis method, determining comprehensive weights of carbon emission indexes, and judging key nodes of an industrial chain; a low-carbon technology is introduced, the industrial chain node dependency degree is adjusted, carbon emission is optimized, and evaluation data is obtained. According to the method, the economic and carbon emission characteristics of each block of the city in different development stages are considered, the space units are flexibly allocated by using the space adjustment rate, the industrial chain layout can be automatically adjusted along with the change of data, and the implementation efficiency of the carbon reduction policy is improved.

The invention belongs to the technical field of geographic information, and discloses a low-efficiency living space studying and judging method and system based on multi-source spatio-temporal data, and the method comprises the steps: obtaining the multi-source spatio-temporal data of a target city space, and carrying out the preprocessing of the multi-source spatio-temporal data, and obtaining an index feature vector; performing feature extraction on the multi-source spatio-temporal data based on index screening and an improved deep learning algorithm to obtain index features; constructing a machine learning clustering model based on spatial constraints, and inputting the index features into the machine learning clustering model to obtain a clustering result; performing low-efficiency living space discrimination on the clustering result to obtain a low-efficiency living space in the target city space; according to the method, unified analysis and index conversion can be carried out on multi-source spatio-temporal data in a target city space, and a multi-dimensional residential land efficiency evaluation index is constructed; through index feature extraction and clustering, effective discrimination of the low-efficiency living space in the target city space is realized.