42 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 discloses a territorial space planning risk assessment method based on multi-dimensional analysis, relates to the technical field of Internet, and aims to obtain and pre-process data from different sources, ensure the consistency and reliability of the data, simulate city expansion, predict future expansion areas and trends, and combine ecological environment data to evaluate the risk of territorial space planning. According to the method, an ecological sensitive area is further marked, necessary space information is provided for subsequent risk assessment, in a multi-dimensional risk assessment model, a comprehensive risk assessment index ZR is obtained by comprehensively considering multiple aspects of biological habitats, ecological connectivity and water resource flow, the possible influence of urban expansion on the ecological environment is comprehensively reflected, and the risk assessment efficiency is improved. Through comparison with a preset risk assessment threshold value Zth, the risk level of urban expansion is effectively assessed, potential ecological crisis is found in time, the model is optimized through continuous verification and assessment, and sustainability of urban expansion and protection of the ecological environment are ensured.

The invention discloses an urban and rural planning management method and system based on artificial intelligence, and belongs to the technical field of urban and rural planning management, and the method comprises the steps of data collection, data preprocessing, urban expansion prediction, air quality prediction and urban and rural planning management. According to the invention, urban and rural planning management is carried out by combining two aspects of urban expansion prediction and air quality prediction, and the planning management dimension is deepened; according to the method, city expansion prediction is carried out by adopting a convolutional long-short-term parallel bidirectional gating circulation network, space and time level features of city expansion are better extracted, the analysis capability of complex data is improved through bidirectional gating circulation, and the overall accuracy of the method is improved; according to the method, air quality prediction is carried out by adopting the time sequence convolution bidirectional long-short-term memory network combined with improved parameter optimization, short-term air change characteristics can be extracted, long-term trend air quality changes can be analyzed, the model performance is optimized by adopting an improved whale optimization search algorithm, and the efficiency and accuracy of urban and rural planning management are improved.

The invention discloses a land utilization simulation method based on a convolutional neural network, and relates to the technical field of land utilization change simulation, and the method comprises the steps: collecting land utilization data, social economy and natural geography driving factors of a research region, carrying out the data preprocessing, training the land utilization data through employing an improved LeNet-5 convolutional neural network, and carrying out the simulation of the land utilization change. The method comprises the following steps: estimating growth probabilities of different land utilization types, quantifying a neighborhood effect through a Gaussian function, calibrating parameters by using a multi-objective optimization algorithm, describing a city spreading trend through the Gaussian function, capturing a macroscopic city expansion process, and comprehensively considering the growth probabilities, the neighborhood effect and the city spreading trend. According to the land utilization change simulation method based on the convolutional neural network and the multi-objective optimization, the precision and the reliability of land utilization change simulation are remarkably improved by combining microscopic and macroscopic spatial features.

The invention discloses a town development boundary identification method based on deep learning, and relates to the technical field of town development, and the method comprises the steps: employing a CNN (convolutional neural network) to extract topographic features and ecological sensitive region information in a remote sensing image, employing a geographic detector to quantify the interaction relation between spatial heterogeneity and factors, and obtaining a town development boundary identification result; according to the method, key driving factors are screened out, reasonable weights are given, in addition, the extension trend of an ecological sensitive area is dynamically predicted through time sequence modeling in combination with a long and short term memory (LSTM) network, and future urban extension is simulated in combination with a neighborhood rule and an extension probability function of MCCA, so that the dynamic adaptability of the development boundary recognition method is enhanced, and the development efficiency is improved. Meanwhile, dynamic balance optimization among ecological protection, economic benefits and land utilization efficiency is realized by adopting a GAN (Generative Adversarial Network) through adversarial training of a generator and a discriminator, the rigid constraint of ecological protection and the elastic requirements of economic development are met, and the limitation of static analysis in a traditional method is solved.

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 relates to an adaptive path planning method and device for an urban sewage system, and the method comprises the steps: collecting the target information of the urban sewage system, and determining an evolution path set meeting a preset planning condition according to the target information; generating a candidate scheme set of the urban sewage system according to the spatial layout and scale collaborative planning scheme of the urban sewage system; simulating an updating and increasing process of the urban sewage system, and constructing an adaptive path set of the urban sewage system according to an adjusting process; and according to the average life cycle cost, planning a path planning scheme and a path satisfying a preset optimal condition, and executing the path planning scheme and the path satisfying the preset optimal condition. Therefore, the problem that long-term system planning decisions under uncertain conditions such as urban expansion cannot be supported due to the fact that the urban sewage system planning technology in the related technology can only produce a single-stage scheme and cannot consider the adaptive cost of the system to future environment change in the life cycle is solved.

The invention discloses a town space regulation and control method for promoting cooperation of carbon reduction, pollution reduction, green expansion and growth, and the method comprises the steps: extracting a town current situation boundary, determining a town current situation built-up area range, simulating a town space expansion inertia boundary, determining a town expansion inertia space, and determining economic, social and ecological value models of an agricultural land. And delimiting an urban space treatment regulation and control area. According to the method, a spatial undifferentiated curve of land resources serving as farmland and urban construction land utilization is theoretically defined, and a spatial reference system for urban space governance regulation and control multi-scheme coordinated with carbon reduction, pollution reduction, green expansion and growth is found in practice; the urban space governance regulation and control model and method are provided from multiple aspects, and a regulation and control scheme is provided for regional space governance and high-quality development.

The invention discloses a method and system for analyzing the influence of urban expansion on the carbon sequestration capacity of vegetation, and the method comprises the steps: calculating the net primary productivity kNPP of nuclear vegetation and the potential net primary productivity PNPP of the vegetation in a research region, and calculating the influence HNPP of human activities on the net primary productivity of the vegetation through the difference between the kNPP and the PNPP; a plurality of annular buffer areas are constructed based on the geometric center of the built area range of the research area, fitting curves of the radiuses of the buffer areas and HNPP values in the buffer areas are obtained through fitting of a generalized additive model, and the influence distance DHNPP of urban expansion on the vegetation carbon sequestration capacity is recognized according to changes and inflection points of the curves; the method comprises the following steps: selecting elements related to urban development and environmental quality, establishing a relationship between each element and DHNPP through an XGBoost model, and analyzing and calculating an influence weight of each element on the DHNPP by using SHAP; according to the method, the new concept of the influence distance of urban expansion on the carbon sequestration capacity of the vegetation is introduced, the space influence range of urban expansion on the net primary productivity of the surrounding vegetation is quantified, and the research blank in the field is filled.

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 historical data-based town spatial extension simulation test analysis method, which comprises the following steps of: obtaining historical town spatial extension data, and obtaining a town spatial extension conversion rule; a cellular automaton is used for obtaining a cellular transformation rule according to the city expansion transformation rule, a cellular automaton model is constructed, multiple subject policies are obtained through collection, and a multi-agent model is constructed through a multi-agent system; fusing the cellular automaton model and the multi-agent model to obtain a CA-MAS combined model, and establishing a town space expansion simulation model; carrying out superposition test on a town space expansion simulation result and a town space expansion actual condition, and continuously optimizing a town space expansion simulation model; the optimal scene and the optimal strategy of urban development are obtained through scene analysis, the cellular automaton model and the multi-agent model are fused, the influence of urban natural expansion and human policies on urban expansion is considered at the same time, and the accuracy of the urban dynamic expansion simulation model is improved.

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 provides a city expansion simulation method based on plaque and grid rule multi-scale fusion, and relates to the technical field of geographic information science, and the method comprises the steps: carrying out the classification and numbering of original land utilization data, and carrying out the superposition, and obtaining a city expansion plaque distribution diagram; processing the space factor by using a space analysis tool to obtain driving variable raster data, and further performing plaque averaging processing and sampling on the driving variable raster data to obtain sample data and plaque averaged driving variable raster data; training by using the sample data and the driving variable raster data of plaque averaging, and constructing a plaque potential rule; in the simulation iteration, the simulated land utilization raster data is subjected to matrix processing, and a two-dimensional convolution algorithm is adopted to construct a grid interaction rule; based on the plaque potential rule, the grid interaction rule and the simulation space constraint, the plaque state transition probability is calculated, and the plaque area rule is constructed, so that the city expansion simulation result is determined, and the city expansion simulation precision is effectively improved.

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 invention provides an urban expansion simulation method based on multi-scale fusion of patch and grid rules, which relates to the field of geographic information science and technology, and comprises: classifying and numbering original land use data, and obtaining an urban expansion patch distribution map after superposition; processing spatial factors with spatial analysis tools to obtain driving variable grid data, and then performing patch average processing and sampling on the driving variable grid data to obtain sample data and patch average driving variable grid data; training with the sample data and the patch average driving variable grid data to construct patch potential rules; in simulation iterations, matrixing the simulated land use grid data, and constructing grid interaction rules with a two-dimensional convolution algorithm; and calculating the probability of patch state transition and constructing patch area rules based on the patch potential rules, the grid interaction rules and the simulated space constraints, and then determining the urban expansion simulation results, thereby effectively improving the simulation accuracy of 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 embodiment of the invention provides a method and device for quantifying the stress effect of city expansion on a green space. The method comprises the following steps: acquiring city expansion data and green space data of a plurality of city regions; the city expansion data comprises a plurality of city expansion features, and the green space data comprises a plurality of green space features; clustering the plurality of city regions according to various green space features in the green space data of the plurality of city regions; for any clustering cluster, training a machine learning model by using the city expansion data and the green space data of the city region in the clustering cluster to obtain a green space data prediction model corresponding to the clustering cluster; and explaining the prediction model by using an interpreter model, and determining the positive and negative values and the strength of the stress effect of various city expansion features on the green spatial features under the clustering cluster. In this way, the stress effect of city expansion on the green space can be effectively quantified through the combination of machine learning and the interpreter.

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.