113 results about "Urban land" patented technology

The invention discloses an urban land subsidence analysis method and system based on multi-source data fusion, relates to the technical field of subsidence analysis, and constructs a multi-source three-dimensional monitoring network, performs air-space-ground multi-source data fusion analysis and atmospheric delay error correction, constructs a land subsidence prediction model, and provides an urban land subsidence analysis method and system based on multi-source data fusion. According to the method, a ground surface and underground three-dimensional monitoring network is constructed, the level, layered settlement and underground water data are integrated, minute-level global monitoring is achieved in combination with the Internet of Things, and the method is high in practicability and high in practicability. Through air-sky-ground data fusion and atmospheric correction, monitoring errors are reduced to a millimeter level, an evaluation system combining subjective and objective weights generates a five-level risk map, a visual platform supports dynamic simulation and factor traceability, a whole-process technical closed loop is formed, and the intelligent level of urban settlement prevention and control is remarkably improved.

The invention discloses an ai-based urban land function identification method and system, and belongs to the technical field of artificial intelligence data processing, and the method comprises the steps: obtaining a high-resolution remote sensing image, a dynamic population thermodynamic diagram and POI data of a target region, and constructing a multi-modal original data set; performing space-time alignment and characterization processing on the data set to generate standardized multi-modal data; weighting the remote sensing image based on the standardized data in combination with population thermodynamic and POI data to generate a plot function feature vector; classifying the feature vectors and outputting a preliminary land use function classification result; generating a credibility score based on the classification result; when the credibility is lower than a threshold value, analyzing the feature vector to generate land use function mixing proportion data; and integrating the classification result, the credibility score and the mixing proportion data to generate a land use function identification report. According to the method, multi-modal data fusion is adopted, and a function mixing degree dynamic analysis strategy is combined, so that high-precision identification of urban functional areas and quantitative analysis of mixed functional plots 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 embodiment of the invention provides a traffic travel carbon emission space-time prediction method based on land function evolution modeling, and belongs to the technical field of data processing, and the method specifically comprises the steps: 1, constructing a multi-scenario land layout scenario constraint based on historical land function change data in a target region; step 2, utilizing the target model to predict urban land function space distribution of the target area under the constraint of the multi-scenario land layout scenario; 3, according to the urban land function space distribution, predicting the travel times of the passenger vehicles in each traffic zone, and according to the travel times, calculating the traffic travel distribution; and step 4, calculating the urban traffic travel carbon emission under the constraint of the multi-scenario land layout according to the traffic travel distribution. Through the scheme disclosed by the invention, the prediction efficiency, accuracy and adaptability are 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 a cellular automaton-based land utilization and urban element collaborative simulation method. The method comprises the following steps of: calculating the development probability of each element; the method comprises the following steps of: constructing a cellular automata (CA) model of each element; space collaborative simulation calculation; and evaluating the result precision. According to the method, the urban land use change simulation precision can be effectively improved, the land use change in reality can be better simulated, the simulation precision of population and economic elements can be improved, and the interaction and synergistic effect between different urban elements are effectively revealed. By means of multi-element collaborative simulation, small changes of urban development elements can be caught more finely, and the method has good perception ability for residential land, industrial land, commercial land, public management land and the like and has important significance for simulating urban collaborative development and reasonably planning urban layout.

The invention discloses a land utilization type fine-grained remote sensing interpretation method based on a self-supervised learning framework, and relates to the technical field of hyperspectral remote sensing image processing and geographic information analysis. The advantages of satellite and unmanned aerial vehicle images are combined, the spectral characteristic difference is dynamically balanced by using a double attention mechanism, the relevance between wave bands and labels is mined based on a self-supervised learning framework, and land utilization first-level global and second-level fine-grained interpretation is realized. According to the method, the feasibility of the multi-mode hyperspectral remote sensing image in urban land utilization monitoring is provided, and the defects of the traditional method in the aspects of strong and insufficient marking dependence, obvious same-spectrum foreign matter phenomenon, inaccurate secondary class interpretation and the like are overcome.

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 provides a simulation design method of an urban ecological corridor, which comprises the following steps: S1, collecting a multi-temporal thermal infrared remote sensing image of a city to be simulated and designed and preprocessing to obtain a core heat island node; s2, heat resistance surface construction is carried out based on different surface types, a heat transfer resistance surface is obtained, and the different surface types comprise a digital elevation model, an impervious surface index, a normalized vegetation index, a normalized building index, a normalized water body index and a gradient; s3, taking the core heat island node as a network end point, searching a communication path with the lowest resistance on a heat transmission resistance surface, and obtaining an urban heat island corridor; and S4, on the basis of the urban land utilization data, ecological lands of forests, wetlands, large parks and water bodies serve as candidate ecological source lands, ecological connection paths are constructed from the angle of ecological connectivity by adopting the S2 and the S3, and the urban ecological corridor is obtained.

The invention discloses an urban territorial space dynamic monitoring and intelligent analysis system and method, and relates to the technical field of territorial space dynamic monitoring. The system comprises an initialization module used for constructing a lightweight learning framework, a dynamic modeling module used for establishing a dynamic network of geographical environment factors and a monitoring target based on parameters and a characteristic mode set output by the initialization module, and an optimization feedback module used for establishing a monitoring result feedback channel, and comparing the actual monitoring data set with a model prediction data set output based on the dynamic modeling module to calculate a prediction error. Through cooperative work of the three, the problem of weak model generalization ability caused by spatial heterogeneity of geographic phenomena is solved, and efficient and accurate monitoring and analysis of urban territorial spaces in different regions are realized.

The invention provides a carbon neutralization zoning simulation method, system and device based on a smart city, and relates to the technical field of carbon neutralization simulation, and the method mainly comprises the steps: carrying out the planning zoning of a map of a tested city; calculating the carbon emission of each partition and carrying out visualization processing to obtain geographic space data; calculating the carbon sink amount of each partition; collecting output value basic data of each industry of the detected partition and marking the output value basic data in geographic space data; carbon emission basic data, carbon sink basic data and output value basic data which are regularly acquired by a smart city system are stored in a smart city database based on a time sequence; predicting the future carbon emission, carbon sink and output value of the measured subarea; constructing a time sequence dynamic optimization model, and solving an industrial area layout scheme through a parallelization genetic algorithm; and generating suggestion information. According to the scheme, a quantifiable and adjustable carbon management tool is provided for overall planning of urban territorial space, and the scheme is particularly suitable for low-carbon planning and treatment of high-density coastal cities.

The present invention provides a method and device for determining urban land parcel evacuation identification information, which can be applied to the fields of artificial intelligence and emergency evacuation technology. The method includes: an identification agent obtains an action for laying out evacuation identification based on land parcel information and initial evacuation parameters; the action is executed in an environment to determine the layout and evacuation status of the evacuation identification; an initial strategy network obtains sample actions, sample evacuation identification layout results, sample evacuation status results, and sample distribution parameters based on sample land parcel information and sample initial evacuation parameters; an initial value network evaluates the sample actions, sample evacuation identification layout results, and sample evacuation status results based on an evaluation strategy to obtain an evaluation result; and based on the evaluation result, sample distribution parameters, and a loss function, the initial parameters of the initial strategy network and the initial value network are updated to obtain an identification agent. This method can reduce reliance on mathematical models and algorithms and reduce computing resource consumption.

The present application relates to a kind of carbon emission space refinement simulation and driving mechanism analysis method based on multi-source data and machine learning, including obtaining night light remote sensing, population density data, multi-source data such as interest point density, nonlinear spatial fitting model is constructed and trained by using information entropy weight method to carry out multi-source data fusion and construct economic energy comprehensive index, use city land use classification data as industry space restriction, combined with the total carbon emission of each industry calculated to build carbon emission space simulation base map, based on extreme gradient boosting tree, introduce SHAP framework for post-analysis, from global, local and spatial three scales, the nonlinear driving mechanism of carbon emission of multi-source city form element is analyzed, overcome the over-saturation defect of single night light data in city core area, realize the leap from pure space fitting to mechanism explanation, provide scientific basis and method support for high-density city accurate locking emission hotspot and making low-carbon policy.

The application discloses a kind of urban land remote sensing image classification methods based on evolution multitask optimization, comprising: 1: obtaining urban land cover remote sensing image sample and pre-processing, to build sample dataset;2: the sample dataset is decomposed into multiple training subsets, and the corresponding sub-population is constructed for each training subset;3: the state of sub-population is evaluated to adjust the probability of executing knowledge transfer operation online;4: different source populations are selected according to the state of sub-population to provide knowledge for it;5: more optimal classifier is obtained by using the model representing global information;6: select a part of poor performance classifier to carry out unbalanced adjustment;7: finally, the best individual is selected from the sub-population to form a multi-classifier, to identify different types of urban land use and cover situation.The application can quickly classify different urban land types by constructing and training the optimal multi-classifier, so as to improve the accuracy and precision of land type classification result.

The invention discloses an urban ventilation environment simulation and prediction method and system based on different planning objectives, and particularly relates to the field of urban ventilation environment simulation and prediction, and the method comprises the steps: obtaining ventilation characteristic data, land utilization data and regulation information data for many years as data sources; unifying the data sources to the same coordinate system and spatial resolution, and performing rasterization and numerical normalization processing; constructing a land utilization prediction model based on the PLUS based on the land utilization data and the regulation information data; and rasterizing the driving factors of the land utilization change. Regulation information is introduced in a land utilization prediction process, a dynamic land utilization conversion matrix is constructed, and a space-time diagram neural network and an entropy weight method are combined for calculation, so that joint modeling and quantitative evaluation of an urban land utilization pattern and a ventilation environment under a multi-level management and control condition are realized; the problem of systematic deviation between a simulation result and actual execution in the prior art is solved.

The present application relates to the technical field of geographic information system, and more particularly to a kind of city land planning analysis method and system based on GIS, by integrating remote sensing, historical planning, mobile terminal and social media data, construct multi-scale spatiotemporal cube model, generate standardized city temporary land data, adopt adaptive feature extraction to identify temporary land, establish hierarchical classification system, and monitor abnormal change through time series model, construct the association diagram of temporary land and city function network, analyze interactive relationship, finally propose differentiated city planning optimization suggestion, help city land resource efficient allocation and management, by fusing multi-source data, realize the multidimensional cognition of space, time, function and relationship, fundamentally improve the understanding depth of temporary land.

This invention discloses an urban land use classification method based on the fusion of multi-source data and deep learning, comprising: Step 1: Constructing a spatiotemporal map using Poisson disk sampling and graph neural networks, integrating the spatiotemporal heterogeneity of multi-source data, and dynamically adjusting weights using an attention mechanism; Step 2: Extracting static features from remote sensing and DEM using Swin Transformer, processing dynamic features from social media and mobile phone signaling using LSTM, fusing static and dynamic features through cross-modal attention, and optimizing data matching degree based on Grad-CAM++; Step 3: Combining the person-to-land flow of mobile phone signaling with the spatial correlation of social media activities, predicting trends using LSTM and embedding ecological indicator constraints; Step 4: Constructing a cross-departmental data sharing platform, collaboratively optimizing planning through a unified indicator system, and dynamically adjusting parameters using social media to mine public demands and POI data; Step 5: Introducing ecological footprint and carbon emission indicators to assess sustainability, establishing a monthly update mechanism, and dynamically calibrating model parameters through real-time data streams.

The present invention discloses a method for calculating pond water storage capacity based on dynamic grid sampling, which relates to the field of water resource monitoring technology. The method comprises: extracting pond water surface based on optical images and an improved normalized water index, calculating the pond area and number within a mission area; and using a grid sampling method to classify and sample the ponds; calculating the pond density and grid width based on the land area, pond area, and number within the mission area, dynamically generating a grid, and batch sampling the ponds using programming software; using big data analysis based on urban land space monitoring road network data, evaluating the quality of pond samples and replacing them, encoding the pond samples, and constructing a sample database using geographic information software; and constructing a water depth inversion model using programming software and a machine learning algorithm to obtain water depth inversion results. The water storage capacity of the ponds is calculated based on the pond area. The present invention can optimize the spatial coverage balance of samples.

The invention relates to an urban land utilization recognition method based on a BERT model text classification algorithm, and the method is characterized in that the method comprises the following steps: 1, determining an urban land utilization recognition type, determining an urban land utilization recognition region range, obtaining POI data in the region range, and carrying out the data screening; step 2, establishing an urban land utilization identification unit in an urban land utilization identification model based on a BERT model text classification algorithm, and associating the screened POI data with the urban land utilization identification unit; step 3, carrying out geographic text mining on the POI data; and step 4, performing urban land utilization identification of the urban land utilization identification model based on the BERT model text classification algorithm to obtain a high-precision urban land utilization identification result. According to the method, high-precision urban land utilization identification can be timely and accurately carried out by utilizing the available POI data.

This invention discloses a method for regulating the thermal environment of urban morphology under the coupling of human and bird's-eye view perspectives. It is based on the radiative transfer equation method and Landsat-8 remote sensing imagery to retrieve land surface temperature; it integrates multimodal data for refined extraction of urban feature information; it constructs a three-dimensional comprehensive representation system of urban morphology under the coupling of bird's-eye and human perspective perspectives; it establishes urban land surface temperature prediction models at different spatial scales, integrating two-dimensional urban morphology, three-dimensional urban morphology from bird's-eye view, and three-dimensional urban morphology from human perspective; and it explores the optimal configuration method for regulating the surface thermal environment of urban morphology at both fine and coarse scales. This invention can provide a scientific basis for the quantitative regulation and spatial planning optimization of the urban thermal environment, achieving refined cooling and thermal environment improvement effects through urban spatial structure optimization and greening configuration adjustment.

This invention discloses a method for assessing urban land green efficiency based on cross-regional technology flow maps. The method acquires the technology input factors, technology output factors, and land input factors of the target city and its related cities through data collection and processing rules, forming a basic data set. Based on cross-regional technology association determination rules, it identifies the correspondence between different cities in terms of technology transfer, technology output, and technology absorption, generating a cross-regional technology flow map. Based on this map, it constructs a set of land green efficiency influencing factors. By introducing the impact of cross-regional technology flow into the correspondence between land input and output, it achieves a comprehensive assessment of the target city's land green efficiency. This invention can reflect the impact of cross-regional technology flow on urban land green utilization efficiency, improves the objectivity and comparability of assessment results, and is applicable to urban green development assessment and land resource management decision-making scenarios.

The utility model relates to the technical field of building engineering foundation pit supporting design and construction, in particular to a shallow pit first and deep pit second construction device for a deep and shallow combined foundation pit, which comprises a partition plate, a first square hole, a first square block, a special-shaped groove and a special-shaped block. The first square block is fixedly connected to the bottom of the partition plate, the special-shaped groove is formed in one side of the partition plate, the special-shaped block is formed in the other side of the partition plate, the foundation pit is divided into two areas through mutual cooperation, after shallow pit part construction is completed, deep pit part construction is started, after pit division construction is adopted, the foundation pit construction time of the shallow pit part is greatly shortened, and construction efficiency is improved. The exposure time of the shallow pit part foundation pit is shortened, the safety risk of the foundation pit is reduced, the overall construction period of a project and the construction stage of the shallow pit part foundation pit are shortened, the deep pit part can be used as a material storage yard and a processing yard, and the problems of urban land shortage, narrow site in a red line and the like are solved.

The present application relates to the technical field of energy management, and especially relates to a water, electricity and gas comprehensive energy management platform for smart city, which comprises an environment sensing module, which is used to determine a typhoon radiation influence area in a target city when the target city enters a typhoon mode; a grid re-partitioning module, which is used to divide a plurality of modified city land blocks; a function area marking module, which is used to obtain a function area mark of a dominant category of each modified city land block; an energy distribution module, which is used to obtain a typhoon day unit electricity minimum reserve benchmark value corresponding to each modified city land block; a hoarding behavior identification module, which is used to determine whether there is energy hoarding behavior, and determine a hoarding behavior category when there is; and an abnormal expansion determination module, which is used to perform abnormal expansion determination to obtain an abnormal expansion determination result when a second duration risk level is obtained. The present application realizes intelligent management of city energy distribution by hoarding analysis and abnormal expansion determination on the typhoon radiation influence area.

The invention provides an urban photovoltaic potential assessment method and system based on land utilization, and relates to the technical field of photovoltaic potential assessment. The method comprises the following steps: constructing an urban three-dimensional building model based on multi-source data; performing solar radiation simulation on the urban three-dimensional building model through light environment simulation software, calculating space-time solar radiation distribution of the urban building roof under the condition of considering the shielding effect of surrounding buildings, and obtaining a space-time solar radiation distribution diagram of the urban building roof; obtaining a city land utilization property current situation map based on the AOI data of the city; and obtaining the photovoltaic potential of the urban building roof according to the urban land utilization property current situation map and the space-time solar radiation distribution map of the urban building roof. According to the method, the influence of the total area of the building roof and the shielding effect of surrounding buildings on solar radiation of the building roof and the influence of the structural characteristic difference of the building body on the available coefficient of the building roof are fully considered, and the accuracy of the photovoltaic potential evaluation result of the urban-scale building roof is improved.

The application belongs to the technical field of deep learning and remote sensing image processing, and specifically discloses a city land use mapping method based on multi-modal data collaborative perception, which comprises the following steps: performing parcel division on high-resolution remote sensing images of a research area to obtain irregular city parcels and remote sensing spectral features thereof; setting street sampling points along a road network, extracting street perception features and extracting interest point semantic features; constructing a heterogeneous graph structure based on the street sampling points, the street perception features and the interest point semantic features falling into the same parcel, and then extracting parcel-level multi-source geographic features; inputting the remote sensing spectral features and the parcel-level multi-source geographic features into a full sparse topic model for semantic alignment and fusion to generate fused parcel feature representations; and performing classification based on the fused parcel feature representations to output city land use mapping results. The application can realize high-precision and high-robustness city land use recognition under the condition that multi-source data is unevenly distributed or sparse.

The invention provides a land utilization planning deep learning method based on a high-resolution low-altitude micrometeorological field. The method comprises the following steps: A1, preparing land planning related data; a2, generating a comfort index and preliminarily planning a layout; a3, training a microclimate generation model; a4, optimizing a land allocation scheme; a5, evaluating and adjusting; a6, iterative optimization is carried out until the standard is reached; and A7, verifying and outputting. According to the method, an end-to-end deep neural network framework is adopted, and a plurality of sub-modules such as microclimate field generation, human body thermal comfort calculation, conditional land layout generation and feedback optimization are integrated, so that automatic generation of a land utilization layout map from basic earth surface information to comfort guidance is realized; the technical problems that in an existing urban land utilization planning method, microclimate simulation cost is high, comfort evaluation and space design are disjointed, and automation and feedback mechanisms are lacked are solved.

The invention discloses a transformer substation group flood early warning grading method based on a time dimension. The method is characterized by comprising the following steps: 1, constructing underlying surface models of transformer substations with different voltage grades by using a digital elevation model, building vector data format data, urban land utilization information and urban rainwater pipe network data; 2, collecting data such as river channels and sluices in a city, and determining the boundary of the underlying surface model; 3, obtaining a forecast rainfall process according to the weather forecast model, and calculating to obtain a forecast flood calculation result from 24 hours to 72 hours; 4, a flood forecast calculation result is corrected; 5, determining the flood influence degree according to the corrected early warning flood calculation result, and determining the flood early warning grade of the transformer substation according to the flood influence degree; and 6, correcting the flood early warning level of the transformer substation. According to the invention, fine management of the flood season of the transformer substation group is realized in flood early warning grade division of the transformer substation group, and technical support is provided for flood control and waterlogging prevention decision.

The application discloses a kind of urban land reserve scale prediction system and method, it is related to urban planning and land management technical field, the application is by collecting urban land reserve total, annual increase and consumption area, land structure distribution and planning target structure etc. Data, calculate the dynamic coefficient reflecting the dynamic change of reserve scale and the structure adaptation coefficient for measuring the adaptation degree of structure and historical target. After the dynamic coefficient and the structure adaptation coefficient are standardized, the comprehensive index is calculated by combining weight, according to which the annual adjustment ratio is determined, and then the benchmark forecast reserve is calculated. Combined with the historical actual adjustment ratio, generate the forecast reserve of conservative, benchmark and active three kinds of scenes, determine the final forecast reserve by comparing with the average value of history. The application comprehensively dynamic trend and structure adaptability, supports multi-scenario analysis, and can provide scientific, flexible decision-making basis for urban land reserve planning.

The application provides a kind of urban land use automatic classification method and system, method includes: based on open source crowd sourcing geographic data production wide range land use training sample;Uncertainty perception context deep learning semantic segmentation network of end-to-end is constructed, and pixel-level land use classification product is obtained;Based on the segmentation of the remote sensing image to be classified by the bottom-up multi-scale segmentation algorithm, the pixel-level land use classification result is fused with the object-level segmentation boundary based on the mode vote, and the object-level land use classification product is obtained.The application is based on deep learning to automatically classify land use types, improve the classification accuracy, and save the labor cost.

The invention discloses an urban land unit greening priority evaluation method giving consideration to benefits and fairness, and the method comprises the steps: taking a potential newly-added green land as a configurable space resource from a supply side on a unified urban land unit scale, and combining land data and population space distribution, thereby achieving the evaluation of the greening priority of the urban land unit. The method comprises the following steps of: depicting a service propagation process from a newly-added green land to surrounding residents by utilizing an exponential spatial attenuation kernel function, converting the improvement of reachable green land into population weighted welfare increment through a welfare function with a marginal income decreasing characteristic, and generating a priority index capable of being directly sorted for each land unit according to the welfare increment; according to the method, the scale of the newly-added green land, the population exposure level, the current green land service insufficiency degree and the marginal welfare decline effect are considered in the same framework at the same time, and limited greening resources can be preferentially allocated to the region which is dense in population, relatively deficient in green land service and high in greening potential under the stock-oriented city updating situation.