30 results about "Urban expansion" patented technology

The invention discloses a city expansion area transformer substation layout method and system based on an artificial intelligence algorithm. The method comprises the following steps: predicting future electrical load demand data of an area by mining an association rule between geographic space data and historical load data of the city expansion area; analyzing the prediction data by using a large language model, partitioning the city expansion area according to an analysis result, obtaining each sub-area and load difference data thereof, combining the data with historical power distribution network grid structure data of the city expansion area, and processing the data through the large language model to obtain an initial layout scheme of the transformer substation; by simulating the power supply capacity of each sub-region at different load growth rates, the initial layout scheme of the transformer substation is optimized, an optimization scheme is obtained and executed, and the optimal layout of the transformer substation is obtained by comprehensively applying artificial intelligence algorithms such as a large language model and a machine learning algorithm dynamic planning algorithm. Accurate prediction of the electrical load demand of the urban expansion area and effective optimization of the transformer substation layout scheme are realized.

The invention provides a city extension simulation method and device, equipment and a storage medium. Relates to the technical field of city simulation. The method includes: selecting an interaction target based on a neighborhood configuration; performing feature extraction on the interaction target to obtain features of the interaction target; constructing a graph structure based on the characteristics of the interaction target; based on the graph structure, constructing a high-order graph attention network; a vector cellular automaton is integrated in the high-order map attention network to obtain an HGAT-VCA model, and city expansion simulation is realized based on the HGAT-VCA model; wherein the vector cellular automaton is used for calculating the total transition probability and determining the land utilization type. According to the method, the expansion of the urban construction land can be simulated and analyzed from a multi-scale and multi-level angle, the complex process of urban expansion and expansion paths under different scenes can be disclosed, and an existing urban expansion theoretical model is enriched.

The invention relates to the technical field of grid division, and discloses a grid division method and system for a power distribution network region, and the method comprises the steps: achieving the precise analysis of the dynamic adjustment of a city boundary and the land use through the integration of city expansion and land use change data, and the combination of a cabling rate and load density data; the real-time response capability to the multi-region development speed and the power demand is enhanced, the flexibility of the power distribution network is improved, the accuracy of power distribution load trend analysis is enhanced after climate change and historical load data are integrated, so that the substation point location and net rack layout decision is more scientific, and according to the current situation and regional characteristics of the power distribution net rack, the power distribution efficiency is improved. According to the method, the power grid can be finely planned according to the requirements of different regions, such as load saturation and rapid development regions, and the fault response and the overall structure of the power grid are optimized by dynamically adjusting the management direction.

The invention relates to a city expansion mode identification method fusing mathematical morphology and landscape connectivity, and belongs to the crossing field of city planning and geographic information technology. The method comprises the following steps: identifying corridor type expansion based on road data; urban holes are recognized through seed filling and morphological closed operation, and then filling type expansion located in the holes is recognized; calculating the minimum expansion step number based on eight-neighborhood iterative expansion to recognize enclave type expansion; and the connectivity and the linear form are fused to identify gallery type expansion and edge type expansion. Compared with the traditional landscape expansion index LEI, the classification precision of the morphological landscape expansion index MLEI provided by the invention is remarkably improved, and the method is suitable for multi-scale urban evolution analysis.

The invention discloses a city expansion mode recognition method based on algebraic topology and a morphological measure theory, and relates to the technical field of crossing of city planning, geographic information systems and mathematics. Comprising the following steps: constructing a spatio-temporal data cube and a landscape height function, extracting topology invariant features of an urban area by using a continuous coherence algorithm, and generating a persistent graph; then, through calculating generalized curvature measurement and generating a distribution histogram, extracting geometric features of the urban plaque boundary; the topological features and the geometric features are fused to construct a topological-measurement fingerprint; and finally, using a k-nearest neighbor algorithm to classify the city expansion modes in the metric space. According to the method, the dependence of a traditional method on empirical parameters is fundamentally overcome, the method has extremely high tolerance on data noise and form irregularity, and a more powerful and reliable analysis tool is provided for urban expansion mode recognition.

The invention discloses a remote sensing image unsupervised change detection method and system based on structure perception and noise enhancement. According to the method, shared feature representation is extracted from a dual-temporal remote sensing image, a structure perception contrast learning mechanism is introduced to enhance the perception capability of a model for real geographic structure change, noise disturbance consistency constraint is designed to avoid an optimization shortcut problem, and a frequency attention decoding mechanism is adopted to finely depict a change region boundary. The system comprises a preprocessing module, a feature coding module, a structure sensing module, a noise disturbance module, a frequency attention decoding module and an unsupervised optimization module. According to the method, under the condition that manual labeling is not needed, the problems that an existing unsupervised change detection method is short in optimization, insufficient in semantic representation capacity, not fine in boundary description and the like are effectively solved, the accuracy and robustness of change detection are improved, and the method is suitable for the fields of urban expansion monitoring, disaster assessment, environment change analysis and the like.

The invention relates to the technical field of remote sensing image processing, and particularly discloses a dynamic multi-scale change detection method based on ground feature units and change tendency. The innovation point of the invention lies in that a dynamic adaptive scale mechanism of the ground feature unit and the change tendency is constructed, and the change tendency is judged through a three-level fusion mechanism of high-confidence region screening and area weighted statistics, multi-index collaborative judgment and multi-feature clustering. According to the method, the internal scale of a surface feature unit is modulated based on a surface feature unit scale modulation factor of the change tendency, parameters such as weights are dynamically adjusted based on the change tendency, and fine classification combining a multi-statistic threshold value and a hierarchical decision is used, so that the adaptability, classification precision and spatial integrity of change detection in multiple scenes are remarkably improved, and the accuracy of change detection is improved. The method can be widely applied to the fields of land utilization dynamic monitoring, ecological environment evaluation, urban expansion analysis and the like.

Embodiments of the present application provide a method and device for quantifying the stress effect of urban expansion on green space. The method comprises: obtaining urban expansion data and green space data of a plurality of urban areas; the urban expansion data comprises a plurality of urban expansion characteristics, and the green space data comprises a plurality of green space characteristics; clustering the plurality of urban areas according to various green space characteristics in the green space data of the plurality of urban areas; for any one cluster, training a machine learning model using the urban expansion data and the green space data of the urban areas in the cluster to obtain a green space data prediction model corresponding to the cluster; and using an interpreter model to interpret the prediction model to determine the positive and negative and strength of the stress effect of various urban expansion characteristics on green space characteristics under the cluster. In this way, the stress effect of urban expansion on green space can be effectively quantified by combining machine learning and an interpreter.

The invention belongs to the technical field of land resource monitoring, and particularly relates to a land utilization change monitoring method based on deep learning. The monitoring method comprises the following steps: S1, deep learning network training data set selection; S2, deep learning environment configuration; S3, deep learning network training; S4, image prediction; according to the method, a U-Net and DeepLabV3 + semantic segmentation model is selected, a Sentinel-2 satellite image is utilized, a deep learning technology is combined, a land utilization intelligent classification and dynamic monitoring framework is constructed, the land utilization change condition of a research area is monitored, driving factors are analyzed, and the dynamic classification and dynamic monitoring of land utilization are realized. Urban expansion and ecological element space-time evolution rules are revealed by finding out reasons for change of ground feature type proportions, and a scientific basis is provided for territorial space governance; according to the method, the technical advantages of deep learning in high-resolution remote sensing interpretation are verified, and a generalizable normal form is provided for middle city land utilization monitoring.

The invention relates to the technical field of building identification, and particularly discloses a satellite image building intelligent identification method and system based on deep learning and GIS, and the method comprises the steps: obtaining high-resolution satellite image data through a remote sensing satellite, capturing geographic space data corresponding to a satellite image from the Internet through employing a crawler technology, and carrying out the recognition of the high-resolution satellite image data; the collected data are input into a GIS database; the method comprises the following steps: performing coordinate mapping and region division on a satellite image by using a GIS system, and generating an image segmentation region with a geographic position identifier in combination with existing urban planning and infrastructure data; according to the invention, the improved Mask R-CNN network and the depth point convolutional network are introduced, so that the positioning, segmentation and classification of the building can be carried out more accurately. The spatial analysis function of the GIS system is combined with deep learning, so that the generation of the building distribution diagram is accurate, and the urban expansion and building growth trend can be effectively predicted.

This application relates to an adaptive path planning method and apparatus for urban wastewater systems. The method includes: collecting target information of the urban wastewater system and determining a set of evolutionary paths that meet preset planning conditions based on the target information; generating a set of candidate schemes for the urban wastewater system based on a coordinated planning scheme for the spatial layout and scale of the urban wastewater system; simulating the renewal and growth process of the urban wastewater system and constructing an adaptive path set for the urban wastewater system based on the adjustment process; planning path planning schemes and paths that meet preset optimal conditions based on average life cycle cost, and executing the path planning schemes and paths that meet the preset optimal conditions. This solves the problem that related urban wastewater system planning technologies can only produce single-stage schemes and cannot consider the adaptation costs to future environmental changes within the system's life cycle, thus failing to support long-term system planning decisions under uncertain conditions such as urban expansion.

The present invention relates to a cross-model, cross-region and cross-time evaluation method for urban expansion simulation and deduction, and the method includes: extracting an urban land use classification map and a driving factor map; constructing a CA model based on multiple CA modeling methods to determine a land conversion probability map; simulating the land use change conditions of each research region during the calibration period and the verification period based on the CA model to obtain a calibration simulation result and a verification simulation result; conducting an overall evaluation on the calibration simulation result and the verification simulation result; extracting the equal-area regions such as the urban-rural fringe in the real land pattern of each research region as a mask, and cropping the calibration simulation result and the verification simulation result to obtain an equivalent region simulation result; conducting a unified evaluation of the equivalent region simulation result across models, regions and time to obtain a unified evaluation result, and comparing it with the overall evaluation result. Compared with the prior art, the present invention has the advantages that the evaluation accuracy can better reflect the actual performance of the model, etc.

The invention discloses a city expansion space-time prediction method and device, computer equipment and a storage medium, and relates to the field of remote sensing time sequence data analysis and geographic space artificial intelligence. The method comprises the following steps: acquiring city expansion data; performing serialization processing and spatio-temporal information embedding on the city expansion data to obtain time series data; inputting the time sequence data into a ViT encoder, and extracting global semantic features; inputting the global semantic features into a CNN decoder, and obtaining pixel-level city expansion prediction corresponding to a specified year; wherein a learnable time embedding vector is introduced into the CNN decoder, and is used for guiding the prediction of a specified year. According to the method, the strong global receptive field of the ViT encoder is utilized to capture long-distance spatial dependence, meanwhile, the CNN decoder is utilized to efficiently reconstruct high-resolution spatial details, in addition, a space-time embedding mechanism is introduced to effectively process complex spatial interaction and time dynamics, and high-precision, long-term and stable prediction is achieved.

The invention provides a city expansion simulation method and device based on ecological protection and electronic equipment, and the method comprises the steps: obtaining ecological system service data of a target region, determining a city expansion mode and an ecological system service protection mode of the target region, and determining a multi-dimensional impact factor of the target region; inputting the ecological system service data into a pre-constructed ecological system service evaluation model to obtain an ecological system service evaluation result of the target area output by the ecological system service evaluation model; and inputting the ecosystem service evaluation result, the city expansion mode, the ecosystem service protection mode and the multi-dimensional influence factor into a pre-constructed city expansion simulation model to obtain a city expansion simulation result of the target area output by the city expansion simulation model. In the urban expansion simulation process, influence factors in multiple aspects are considered, the accuracy of urban expansion simulation is improved, and the method is suitable for complex application scenes.

The invention provides a live-action three-dimensional model dynamic updating and calibrating method and system based on orthoimages, and the method comprises the steps: obtaining a plurality of stages of orthoimages, a plurality of stages of thermal infrared images and land surface temperature data of a target region, the target region comprising a city expansion region; based on the time sequence difference between the multi-stage orthoimage and the multi-stage thermal infrared image, extracting a land cover change area from the urban expansion area; according to the surface temperature data, identifying a local thermal anomaly region from the urban expansion region; based on the surface coverage change area and the local thermal anomaly area, in combination with a pre-trained space-time transformation model, outputting change probability information representing a surface feature change trend; and taking the change probability information as a driving signal, adjusting the Gaussian sputtering model, and updating and calibrating the geometric structure of the live-action three-dimensional model in the latest period according to the adjusted Gaussian sputtering model. According to the method, the synchronous characterization precision of the three-dimensional model of the urban expansion area on the surface heat island effect and the building change is improved.

This invention provides a method, system, computer equipment, and storage medium for correcting nighttime light remote sensing data, belonging to the field of urban environmental monitoring technology. The method includes: using a multi-source remote sensing sensor to statistically analyze the impermeable and non-impermeable layers in a target area, and calculating a multi-source data consistency index (CI); using the CI to correct the impermeable layer classification results of the target area; and based on the corrected impermeable layer classification results, counting the number N of impermeable layer pixels within the target area. b Number of pixels N in impermeable layer n , using N b and N n Calculate the proportion P of the impermeable layer within a unit pixel. b Using the nighttime light remote sensing brightness value NTL and the proportion of impermeable layer per unit pixel P b A calibration model was constructed to effectively reduce light spillover interference in impermeable areas, significantly improve the accuracy of impermeable layer data, and provide high-precision data support for urban expansion monitoring, light pollution assessment, and socio-economic activity analysis.

The invention provides a city built-up area identification method based on multi-source noctilucent remote sensing data, and relates to the technical field of remote sensing, and the method comprises the steps: obtaining DMSP-OLS and NPP-VIIRS night light data and Landsat remote sensing image data, extracting a normalized water vapor index and a normalized water body index, and carrying out the feature fusion of the extracted water vapor index and normalized water body index with the night light data to obtain corrected light data; with land utilization data as a reference, calculating an optimal threshold value of the corrected light data, identifying an area higher than the threshold value as a city built-up area, and generating a distribution map; and inputting the historical distribution map into a preset space-time diagram neural network model, and outputting a future city built-up area distribution map to predict city expansion. The method effectively reduces the interference of water vapor and water on night light data, improves the recognition accuracy of the urban built-up area, and achieves the dynamic prediction of urban expansion.

The application relates to the technical field of city planning, and discloses a micro-size city expansion measurement method based on a central grid area index, which comprises the following steps: constructing a basic grid, performing step-by-step spatial expansion on the basic grid to form multiple grids of different scales; dividing the research area into uniform grids by using the grids of different scales, then calculating the central grid area index CGAI corresponding to each grid under each scale, and formulating a screening rule based on the central grid area index CGAI, so as to screen the grids, and the remaining grids constitute the CGAI distribution map of the research area corresponding to the grid. The CGAI distribution maps of different scales are reasonably evaluated, and the CGAI distribution map with the highest evaluation result is taken as the final result output.

The invention discloses a remote sensing change detection method for urban expansion monitoring. The method comprises the following steps: step 1, collecting and sorting image samples containing urban expansion change; 2, performing image preprocessing on the images in the data set to enhance data diversity and improve the robustness of the model in a complex scene; 3, performing target labeling on the preprocessed image by adopting a LabelImg labeling tool, and dividing a data set into a training set, a verification set and a test set to ensure that the model can be trained and evaluated on different data sets; step 4, constructing a multi-dimensional feature alignment self-supervising pre-training network MDFANet used for urban expansion monitoring; and 5, inputting the preprocessed and labeled city expansion change data set into the MDFANet model for training, and learning the space and frequency domain features of the city building through forward propagation and reverse optimization, thereby improving the detection precision and generalization ability.

This invention provides a quantitative method for assessing the impact of urbanization expansion patterns on flood factors in small and medium-sized watersheds. This method is based on a coupling of three modules: quantification and clustering identification of urban expansion patterns, cross-validation mechanism of hydrological models and empirical formulas, and a pattern-process response analysis framework. This invention innovatively constructs a comprehensive quantitative index system for urban land use expansion patterns from three dimensions: level, rate, and pattern. By fully utilizing limited rainfall data and watershed geographic information, and through cross-validation using two methods based on different principles, it significantly improves the reliability and accuracy of flood parameter estimation in data-free areas.

The present invention discloses a method and system for substation layout in urban expansion areas based on artificial intelligence algorithms. By mining the correlation law between the geospatial data and historical load data in urban expansion areas, the future power load demand data in these areas is predicted; the large language model is used to analyze the predicted data, so as to divide the urban expansion areas according to the analysis results, obtain each sub-area and its load difference data, combine it with the historical distribution network grid structure data of the urban expansion areas, and after being processed by the large language model, obtain the initial substation layout plan; by simulating the power supply capacity of each sub-area under different load growth rates, the initial substation layout plan is optimized to obtain an optimized plan and execute it. By comprehensively applying artificial intelligence algorithms such as large language models and machine learning algorithms (dynamic programming algorithms, etc.), the accurate prediction of power load demand in urban expansion areas and the effective optimization of substation layout plans are realized.

This invention provides an urban planning and building design system based on seismic resilience, environmental sustainability, and urban expansion problems. Using real-time seismic risk assessment, adaptive structural design, and environmentally friendly materials, the system maximizes land use, infrastructure footprints, and building performance in urban areas. Technical specializations which include base isolators, lightweight hybrid structures, and prefabricated modular units provide structural flexibility and a high level of disaster resistance at a lower construction time and cost. It has also integrated IoT-ready sensors and digital twin technology to enable real-time monitoring and predictive maintenance thus increasing effectiveness in disaster preparedness and recovery. It uses low-carbon building materials, passive energy designs, and renewing natural resources such as green corridors and permeable pavements to prioritize environmental sustainability while reducing urban emissions and enhancing urban ecosystems. It is applicable in different urban settings and can apply to inner cities to the most concentrated and vulnerable areas concerning the impact of disasters. This invention supports sustainability all over the world in improving the safety and resilience of urban centers and contributes to the sustainability of an urban environment.

The invention provides a noctilucent remote sensing data correction method and system, computer equipment and a storage medium, and belongs to the technical field of urban environment monitoring, and the method comprises the steps: carrying out the statistics of an impervious layer and a non-impervious layer of a target region through a multi-source remote sensing sensor, and calculating a multi-source data consistency index CI; checking an impervious layer classification result of the target area through a multi-source data consistency index CI of the target area; according to the corrected classification result of the impervious layer of the target area, the number Nb of impervious layer pixels and the number Nn of non-impervious layer pixels in the target area are counted respectively, and the proportion Pb of the impervious layer in a unit pixel is calculated through Nb and Nn; a correction model is constructed by using the noctilucent remote sensing brightness value NTL and the impervious layer proportion Pb in a unit pixel, so that light overflow interference of an impervious layer area is effectively reduced, the accuracy of impervious layer data is remarkably improved, and high-precision data support is provided for urban expansion monitoring, light pollution evaluation and social and economic activity analysis.

The application relates to the technical field of remote sensing image processing, and particularly discloses a dynamic multi-scale change detection method based on a ground object unit and a change tendency. The application is characterized in that a dynamic self-adaptive scale mechanism of the ground object unit and the change tendency is constructed, the change tendency is judged through high-confidence-area screening and area-weighted statistics, multi-index collaborative judgment and a multi-feature clustering three-level fusion mechanism, the internal scale of the ground object unit is modulated based on a scale modulation factor of the ground object unit based on the change tendency, parameters such as weights are dynamically adjusted based on the change tendency, and fine classification is achieved by combining a multi-statistical threshold and hierarchical decision-making, so that the adaptability, classification accuracy and spatial integrity of the change detection in multiple scenes are significantly improved, and the application can be widely used in fields such as land use dynamic monitoring, ecological environment evaluation and urban expansion analysis.

The invention discloses a city texture analysis and prediction method based on a road network, which is used for quantifying the influence of government policies on city development and predicting the future space-time evolution of a city. According to the method, the concept of city texture is introduced, road network data and a space-time analysis technology are combined, and the dynamic characteristics of city development can be accurately captured. According to the method, the neural network and the linear prediction model are utilized to perform quantitative analysis on the development levels of different regions of the city, identify the specific influence of policy intervention on city development, perform scientific prediction on future city expansion and infrastructure construction, and provide powerful support for city planning and policy making.

The present invention belongs to the technical field of three-dimensional modeling, and discloses a method and system for constructing a low-altitude economical three-dimensional scene in combination with digital stretching technology; the method comprises: collecting image information of urban facilities on a preset flight path; the image information is collected by a five-eye camera device carried by an unmanned aerial vehicle; the image information comprises a first view, a second view, a third view, a fourth view, a fifth view, ambient brightness, a shooting angle, a shooting distance and a depth map; the image information is preprocessed to obtain preprocessed image information; based on the preprocessed image information, the urban facilities are three-dimensionally constructed in combination with digital stretching technology to obtain a three-dimensional structural model of the urban facilities; based on the three-dimensional structural model, planning layout information of the urban facilities is generated; the accuracy of the overall layout planning during urban expansion is effectively improved.

The invention provides a lightweight meteorological downscaling method and device suitable for any multiple, equipment and a storage medium. Relates to the technical field of meteorological marine science. The method includes: selecting an interaction target based on a neighborhood configuration; performing feature extraction on the interaction target to obtain features of the interaction target; constructing a graph structure based on the characteristics of the interaction target; based on the graph structure, constructing a high-order graph attention network; a vector cellular automaton is integrated in the high-order map attention network to obtain an HGAT-VCA model, and lightweight meteorological downscaling suitable for any multiple is realized based on the HGAT-VCA model; wherein the vector cellular automaton is used for calculating the total transition probability and determining the land utilization type. According to the method, the expansion of the urban construction land can be simulated and analyzed from a multi-scale and multi-level angle, the complex process of urban expansion and expansion paths under different scenes can be disclosed, and an existing urban expansion theoretical model is enriched.

The application relates to a city expansion mode recognition method fusing mathematical morphology and landscape connectivity, and belongs to the cross field of city planning and geographic information technology. The method comprises the following steps: recognizing corridor expansion based on road data; recognizing city holes through seed filling and morphological closing operation, and then recognizing infill expansion located in the holes; recognizing enclaves through the calculation of the minimum expansion step number based on eight-neighborhood iterative expansion; and fusing connectivity and linear morphology to recognize corridor expansion and edge expansion. The method provided by the application, namely morphological landscape expansion index MLEI, has significantly improved classification accuracy compared with traditional landscape expansion index LEI, and is suitable for multi-scale city evolution analysis.