Data processing method and system applied to territorial space planning

Abstract

The invention relates to the technical field of data processing, in particular to a data processing method and system applied to territorial space planning. The method comprises the following steps: carrying out regional image acquisition on a territorial planning region to obtain a territorial planning region image, generating dense point cloud data based on the territorial planning region image, and constructing a planning region overall model by generating the dense point cloud data; carrying out regional building dense region identification on the overall model, presetting dense building nodes, and constructing a node network; classifying nodes based on the land planning area image to obtain node classification data; and performing limited development area analysis on the node classification data according to the overall model and the area image, performing area connectivity analysis by using a node network, and performing territorial planning on the overall model based on the limited development area and connectivity data to obtain a territorial space planning scheme. Territorial planning is realized by adopting data processing, so that the planning rationality is improved, and the planning analysis efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a data processing method and system applied to national land space planning. Background Art

[0002] Traditional land space planning methods mainly rely on two-dimensional maps and manual experience, which makes it difficult to accurately evaluate complex three-dimensional spatial relationships and dynamic change processes, resulting in a lack of scientific basis for planning schemes. At present, some advanced technologies have been applied in the field of land space planning, such as geographic information systems, remote sensing technology, three-dimensional modeling technology, and some spatial analysis methods. Geographic information system technology is mainly used for the management, analysis, and visualization of spatial data. Remote sensing technology can provide high-resolution image data for land use classification, vegetation coverage monitoring, etc. Geographic information system technology has improved the efficiency and accuracy of planning to a certain extent. Therefore, the current technology has some defects, lacks the ability to analyze regional dynamic changes, and cannot reflect and predict regional development trends in real time; regional three-dimensional modeling and ecological analysis are insufficient, and the complex relationship between buildings, land use and ecosystems cannot be fully considered; lack of refined analysis of densely built areas and node networks, resulting in deviations between planning results and actual conditions. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a data processing method and system applied to national land space planning to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above purpose, a data processing method applied to national land space planning includes the following steps: Step S1: acquiring a regional image of the land planning area to obtain a land planning area image; generating a dense point cloud according to the land planning area image to obtain regional dense point cloud data; constructing a three-dimensional model of the regional dense point cloud data based on the land planning area image to obtain a three-dimensional model of the planning area and a three-dimensional model of regional buildings; integrating the three-dimensional model of the planning area and the three-dimensional model of regional buildings to obtain an overall model of the planning area; Step S2: Identify the regional building-dense areas on the overall model of the planning area to obtain the regional building-dense areas; preset the building-dense nodes on the overall model of the planning area based on the regional building-dense areas to obtain the dense building nodes; construct the node network on the overall model of the planning area based on the dense building nodes to obtain the regional dense building network; classify the dense building nodes based on the land planning area image to obtain the node classification data; Step S3: Perform restricted development area analysis on the node classification data according to the overall model of the planning area and the national land planning area image to obtain the planned restricted development area; perform regional connectivity analysis on the overall model of the planning area according to the regional dense building network to obtain the planning area connectivity data; perform national land planning on the overall model of the planning area based on the planned restricted development area and the planning area connectivity data to obtain a national land space planning scheme.

[0005] The present invention constructs a three-dimensional model of the planning area and the building by acquiring regional images and generating dense point cloud data, and finally integrates it into a unified overall model. The benefit of this step is to elevate the traditional two-dimensional planning to the three-dimensional level, which can express the morphology and spatial relationship of the objects more intuitively and finely, and provide more accurate basic data for subsequent analysis and planning. Compared with planning based on two-dimensional maps alone, the three-dimensional model can more realistically reflect information such as surface undulations and building heights, avoid planning deviations caused by information loss, and thus improve the scientificity and accuracy of planning; on the basis of the overall model, densely built areas are identified, nodes are preset, and networks are constructed. This makes planning no longer just focus on plots, but go deep into the building level to analyze building density, distribution patterns, and interconnected relationships. By constructing a node network, the urban spatial structure can be better understood, key nodes and important connection paths can be identified, more refined guidance can be provided for traffic planning, infrastructure construction, etc., and the foundation can be laid for subsequent restricted development area analysis and connectivity analysis, and finally the rationality and effectiveness of planning can be improved; restricted development area analysis and regional connectivity analysis are performed, and a national land space planning scheme is finally generated. By combining the overall model, node classification data, and regional images, we can more comprehensively consider factors such as topography, ecological environment, and building distribution, scientifically delineate restricted development areas, and evaluate regional connectivity based on the node network to ensure the rationality and feasibility of the planning scheme. This comprehensive analysis method effectively avoids the limitations of a single factor dominating the planning and improves the integrity and coordination of the planning.

[0006] Preferably, the present invention further provides a data processing system applied to national land space planning, which is used to execute the data processing method applied to national land space planning as described above, and the data processing system applied to national land space planning includes: The planning area model construction module is used to obtain regional images of the national land planning area to obtain the national land planning area image; generate dense point clouds based on the national land planning area image to obtain regional dense point cloud data; construct a three-dimensional model of the regional dense point cloud data based on the national land planning area image to obtain a three-dimensional model of the planning area and a three-dimensional model of regional buildings; integrate the three-dimensional model of the planning area and the three-dimensional model of regional buildings to obtain an overall model of the planning area; The regional node setting module is used to identify the regional building-dense areas of the overall model of the planning area to obtain the regional building-dense areas; preset the building-dense nodes of the overall model of the planning area based on the regional building-dense areas to obtain the dense building nodes; construct the node network of the overall model of the planning area based on the dense building nodes to obtain the regional dense building network; classify the dense building nodes based on the land planning area image to obtain the node classification data; The national land space planning module is used to perform restricted development area analysis on the node classification data according to the overall model of the planning area and the image of the national land planning area to obtain the planned restricted development area; to perform regional connectivity analysis on the overall model of the planning area according to the regional dense building network to obtain the connectivity data of the planning area; to perform national land planning on the overall model of the planning area based on the planned restricted development area and the connectivity data of the planning area to obtain the national land space planning scheme.

[0007] To summarize, the present invention provides a data processing method and system applied to national land space planning. The data processing system applied to national land space planning consists of a planning area model construction module, a regional node setting module, and a national land space planning module. It can implement any data processing method applied to national land space planning described in the present invention, and is used to combine the operations between computer programs running on each module to implement any data processing method applied to national land space planning. The internal structures of the system cooperate with each other, which can greatly reduce repetitive work and manpower investment, and can quickly and effectively provide a more accurate and efficient data processing process applied to national land space planning, thereby simplifying the operating procedures of the data processing system applied to national land space planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings: Figure 1 A schematic diagram of the steps of a data processing method for national land space planning according to the present invention; Figure 2 for Figure 1 Detailed step flow diagram of step S2; Figure 3 for Figure 2 Detailed step flow chart of step S25.

[0009] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0010] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0011] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0012] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0013] To achieve this, please refer to Figures 1 to 3 The present invention provides a data processing method for national land space planning, comprising the following steps: Step S1: acquiring a regional image of the land planning area to obtain a land planning area image; generating a dense point cloud according to the land planning area image to obtain regional dense point cloud data; constructing a three-dimensional model of the regional dense point cloud data based on the land planning area image to obtain a three-dimensional model of the planning area and a three-dimensional model of regional buildings; integrating the three-dimensional model of the planning area and the three-dimensional model of regional buildings to obtain an overall model of the planning area; Step S2: Identify the regional building-dense areas on the overall model of the planning area to obtain the regional building-dense areas; preset the building-dense nodes on the overall model of the planning area based on the regional building-dense areas to obtain the dense building nodes; construct the node network on the overall model of the planning area based on the dense building nodes to obtain the regional dense building network; classify the dense building nodes based on the land planning area image to obtain the node classification data; Step S3: Perform restricted development area analysis on the node classification data according to the overall model of the planning area and the national land planning area image to obtain the planned restricted development area; perform regional connectivity analysis on the overall model of the planning area according to the regional dense building network to obtain the planning area connectivity data; perform national land planning on the overall model of the planning area based on the planned restricted development area and the planning area connectivity data to obtain a national land space planning scheme.

[0014] In the embodiment of the present invention, please refer to Figure 1 FIG. 1 is a schematic diagram of a step flow chart of a data processing method for land space planning according to the present invention. In this example, the data processing method for land space planning includes the following steps: Step S1: acquiring a regional image of the land planning area to obtain a land planning area image; generating a dense point cloud according to the land planning area image to obtain regional dense point cloud data; constructing a three-dimensional model of the regional dense point cloud data based on the land planning area image to obtain a three-dimensional model of the planning area and a three-dimensional model of regional buildings; integrating the three-dimensional model of the planning area and the three-dimensional model of regional buildings to obtain an overall model of the planning area; In an embodiment of the present invention, by determining the parameters of aerial photography or satellite images according to planning requirements, data collection and preprocessing are performed to generate an orthophoto map of the planning area. Then, high-density point cloud data is generated using the image, and the point cloud is classified based on the image to extract the building point cloud. Next, the building point cloud is denoised, segmented, and simplified, and the regional dense point cloud and the building point cloud are used to construct three-dimensional models of terrain and buildings with real textures, respectively. After that, feature points are extracted for matching and registration, and the building model is accurately positioned in the three-dimensional model of the planning area. Finally, the building model that has undergone coordinate transformation is fused with the terrain model to generate an overall model of the planning area containing elements such as terrain and buildings.

[0015] Step S2: Identify the regional building-dense areas on the overall model of the planning area to obtain the regional building-dense areas; preset the building-dense nodes on the overall model of the planning area based on the regional building-dense areas to obtain the dense building nodes; construct the node network on the overall model of the planning area based on the dense building nodes to obtain the regional dense building network; classify the dense building nodes based on the land planning area image to obtain the node classification data; In an embodiment of the present invention, a building model is extracted from the overall model of the planning area, the building density is calculated, and the building-dense areas are identified by regional growth or density clustering algorithms. Then, the boundaries of each building-dense area are extracted, and its center point is calculated. Next, nodes are preset at the center point according to preset rules, and fine-tuned according to regional characteristics. After that, the spatial relationship between nodes is analyzed, and a node network is constructed using a network construction algorithm and optimized. Finally, based on the national land planning area image, the ecological and environmental indicators in the node impact area are extracted, the nodes are evaluated for ecological environment, and the nodes are divided into high-ecological nodes and low-ecological nodes according to the ecological scores.

[0016] Step S3: Perform restricted development area analysis on the node classification data according to the overall model of the planning area and the national land planning area image to obtain the planned restricted development area; perform regional connectivity analysis on the overall model of the planning area according to the regional dense building network to obtain the planning area connectivity data; perform national land planning on the overall model of the planning area based on the planned restricted development area and the planning area connectivity data to obtain a national land space planning scheme.

[0017] In an embodiment of the present invention, by extracting the elevation data in the overall model of the planning area, a digital elevation model and a slope map are generated, and the terrain change trend data are obtained by overlay analysis based on the buffer zone and the slope map of the high ecological node. An ecological buffer zone is set according to the node type and the sensitivity of the surrounding environment, and the buffer zones of adjacent nodes are merged. Restricted development rules are formulated based on the terrain change trend and the ecological buffer zone, and GIS is used to identify and mark restricted development areas. Regional connectivity is evaluated through a network analysis algorithm, and connectivity indicators are calculated. Finally, based on the planned restricted development area, connectivity data, regional resource and environmental carrying capacity, development potential and functional positioning, the spatial layout is optimized, the functional zones are divided, and the scheme evaluation is carried out to finally obtain a national land space planning scheme.

[0018] The present invention constructs a three-dimensional model of the planning area and the building by acquiring regional images and generating dense point cloud data, and finally integrates it into a unified overall model. The benefit of this step is to elevate the traditional two-dimensional planning to the three-dimensional level, which can express the morphology and spatial relationship of the objects more intuitively and finely, and provide more accurate basic data for subsequent analysis and planning. Compared with planning based on two-dimensional maps alone, the three-dimensional model can more realistically reflect information such as surface undulations and building heights, avoid planning deviations caused by information loss, and thus improve the scientificity and accuracy of planning; on the basis of the overall model, densely built areas are identified, nodes are preset, and networks are constructed. This makes planning no longer just focus on plots, but go deep into the building level to analyze building density, distribution patterns, and interconnected relationships. By constructing a node network, the urban spatial structure can be better understood, key nodes and important connection paths can be identified, more refined guidance can be provided for traffic planning, infrastructure construction, etc., and the foundation can be laid for subsequent restricted development area analysis and connectivity analysis, and finally the rationality and effectiveness of planning can be improved; restricted development area analysis and regional connectivity analysis are performed, and a national land space planning scheme is finally generated. By combining the overall model, node classification data, and regional images, we can more comprehensively consider factors such as topography, ecological environment, and building distribution, scientifically delineate restricted development areas, and evaluate regional connectivity based on the node network to ensure the rationality and feasibility of the planning scheme. This comprehensive analysis method effectively avoids the limitations of a single factor dominating the planning and improves the integrity and coordination of the planning.

[0019] Preferably, step S1 comprises the following steps: Step S11: acquiring a regional image of the national land planning area to obtain a national land planning regional image; In the embodiment of the present invention, the coverage, resolution, image overlap and other parameters of the aerial or satellite image are determined according to the spatial scope and target accuracy requirements of the national land planning. A drone or satellite platform is used to carry a high-resolution camera or sensor to collect data according to a preset route or shooting plan. The acquired raw image data is pre-processed by geometric correction, radiation correction, splicing, cropping and other pre-processing operations to generate an orthophoto map of the national land planning area that meets the accuracy requirements.

[0020] Step S12: generating a dense point cloud according to the land planning area image to obtain regional dense point cloud data; In the embodiment of the present invention, the pre-processed land planning area image is imported into the point cloud generation software. According to the accuracy requirements and computer performance, various parameters for point cloud generation are set, such as image matching algorithm, point cloud density, filtering parameters, etc. The software is run to perform dense matching and point cloud generation to obtain high-density planning area point cloud data. The generated point cloud data is subjected to quality checks, such as point cloud density, accuracy, integrity, etc.

[0021] Step S13: classifying the regional dense point cloud data based on the national land planning regional image to obtain regional classified point cloud data; In an embodiment of the present invention, a suitable point cloud classification method is selected according to the data characteristics and classification purpose, such as rule-based classification, random forest classification, deep learning classification, etc. According to the needs of national land planning, the classification category of the point cloud data is determined, such as buildings, vegetation, roads, water bodies, etc. The feature information of the point cloud data is extracted, such as point cloud height, intensity, normal vector, color, geometric shape, etc., to provide a basis for classification. For some classification methods, such as random forest and deep learning, it is necessary to train the classifier using existing label data. Using the selected classification method and the trained classifier, the regional dense point cloud data is classified to obtain point cloud data of objects of different categories.

[0022] Step S14: extracting regional building point cloud data from the regional classification point cloud data to obtain regional building point cloud data; In the embodiment of the present invention, point cloud data marked as "building" are screened out from the overall point cloud data by using methods such as Boolean operations or spatial queries based on the classification results. For the screened building point cloud data, algorithms such as statistical filtering and radius filtering are used to remove noise point clouds caused by factors such as occlusion and reflection. For areas with multiple buildings, algorithms based on plane fitting, regional growth, cluster analysis, etc. are used to segment the building point cloud data into individual building point clouds. Algorithms such as triangular mesh simplification and voxel simplification are used to simplify the segmented individual building point clouds, reduce the amount of data, improve the efficiency of subsequent processing, and retain the main geometric features of the building.

[0023] Step S15: constructing a three-dimensional model of the regional terrain according to the regional dense point cloud data to obtain a three-dimensional model of the planning area; constructing a three-dimensional model of the regional buildings according to the regional building point cloud data to obtain a three-dimensional model of the regional buildings; In an embodiment of the present invention, the regional dense point cloud data is filtered to remove non-ground points such as vegetation and buildings, and retain the ground point cloud data. The Delaunay triangulation algorithm is used to generate a TIN model based on the ground point cloud data to represent the undulating shape of the surface. The TIN model is texture mapped using an orthophoto or oblique image to generate a three-dimensional terrain model with real color and texture; based on the processed building point cloud data, the Delaunay triangulation, Poisson reconstruction and other algorithms are used to construct a three-dimensional point cloud model of the building. The reconstructed building point cloud model is simplified and regularized to remove redundant details and generate a concise and regular building model. The simplified building model is texture mapped using an orthophoto or oblique image to generate a three-dimensional building model with real color and texture.

[0024] Step S16: positioning the building model of the planned area three-dimensional model based on the regional building point cloud data to obtain building model positioning data; In the embodiment of the present invention, feature points, such as corner points, edge points, etc., are extracted from the building point cloud data and the three-dimensional model of the planning area, and matched using feature descriptors. The matching feature point pairs are used to calculate the rough transformation matrix of the building point cloud data relative to the three-dimensional model of the planning area using algorithms such as singular value decomposition. The result after rough registration is iteratively optimized using an iterative closest point algorithm, etc., to obtain accurate building model positioning data.

[0025] Step S17: integrating the regional building three-dimensional model and the planning area three-dimensional model according to the building model positioning data to obtain an overall model of the planning area.

[0026] In the embodiment of the present invention, the coordinates of the regional building 3D model are transformed according to the building model positioning data, and the model is placed in the correct position of the planning area 3D model. The regional building 3D model after the coordinate transformation is merged with the planning area 3D model to generate an overall model of the planning area including terrain, buildings and other elements.

[0027] The present invention obtains regional images of national land planning, laying the foundation for all subsequent analyses. High-resolution, high-quality regional images can provide rich geographic information, including topography, vegetation coverage, land use types, etc., and provide necessary data support for subsequent point cloud generation, three-dimensional modeling and various spatial analyses, ensuring the accuracy and scientificity of planning. Dense point cloud data is generated based on regional images, and two-dimensional images are converted into three-dimensional spatial data, providing richer spatial information. Dense point clouds can accurately express the three-dimensional form and spatial distribution of objects, providing a more refined data basis for subsequent three-dimensional modeling and analysis. Compared with traditional two-dimensional data, point cloud data can more accurately express information such as surface undulations and building heights; regional dense point cloud data is classified, and point cloud data is distinguished according to different types of objects, such as buildings, vegetation, and ground. This helps to extract specific types of object information from point cloud data in the future, such as extracting building point clouds for building models and extracting ground point clouds for building terrain models, thereby improving the efficiency and accuracy of data processing; extracting regional building point cloud data from the classified point cloud data provides special data for the subsequent construction of a refined three-dimensional building model. Extracting building point cloud data can effectively remove interference from other types of land objects, making the construction of building models more accurate and better reflecting the geometric shape, spatial position and height information of buildings; using regional dense point cloud data and regional building point cloud data to construct the planning area 3D model and regional building 3D model respectively. This step converts point cloud data into a more intuitive and easier to understand 3D model, which can more clearly show the topography and building distribution of the planning area, and provide a more intuitive data basis for subsequent model integration and spatial analysis; based on the regional building point cloud data, the building model is positioned in the planning area 3D model, and the building model is accurately positioned in the planning area 3D model. This ensures that the spatial relationship between the building model and the terrain model is accurate, providing accurate location information for subsequent model integration and spatial analysis; the terrain model and the building model are integrated to form a unified 3D scene, which can more comprehensively and realistically show the overall appearance of the planning area.

[0028] Preferably, step S13 comprises the following steps: Step S131: performing image semantic data segmentation on the land planning area image to obtain image semantic data; In an embodiment of the present invention, an image semantic segmentation model suitable for a land planning scenario is selected, such as U-Net, DeepLab, PSPNet, etc. An open source pre-trained model can be selected according to project requirements, or model training and fine-tuning can be performed using own data. The land planning area image is pre-processed, such as image scaling, normalization, and other operations, to meet the input requirements of the selected semantic segmentation model. The pre-processed image is semantically segmented using the selected model to predict the semantic category to which each pixel belongs, such as buildings, roads, vegetation, water bodies, etc.

[0029] Step S132: semantically classify and segment the land planning area image based on the image semantic data to obtain image classification semantic segmentation data; In an embodiment of the present invention, adjacent pixels of the same category are aggregated together according to the category label of each pixel in the image semantic data to form different semantic regions. The boundary of each semantic region is extracted using an edge detection algorithm, such as the Canny operator, or a contour extraction algorithm. The extracted region boundary is converted into a vector format, such as a polygon or a line segment, and the semantic category information of each vector object is recorded. The vectorized semantic region data is superimposed on the original land planning region image to generate image classification semantic block data containing semantic category annotations.

[0030] Step S133: performing data mapping on the land planning area image based on the regional dense point cloud data to obtain point cloud mapping image data; In an embodiment of the present invention, by using camera parameters, internal parameters and external parameters, the regional dense point cloud data is projected onto the land planning regional image plane to obtain the corresponding pixel coordinates of each point cloud on the image. The point cloud information projected onto each pixel, such as three-dimensional coordinates, color, intensity, etc., is extracted, and the information is stored in the corresponding pixel position to form point cloud mapping image data. For areas without point cloud projection on the image, an interpolation algorithm, such as inverse distance weighted interpolation, can be used to fill in to obtain more complete point cloud mapping image data.

[0031] Step S134: performing point cloud region classification on the point cloud mapping image data according to the image classification semantic block data to obtain classified point cloud region data; In an embodiment of the present invention, the point cloud mapping image data and the image classification semantic block data are spatially superimposed so that each pixel contains both point cloud information and semantic category labels. According to the semantic category label of each pixel, the point cloud mapping image data is segmented into different point cloud regions, and the point clouds in each region have the same semantic category.

[0032] Step S135: performing point cloud classification and cutting on the regional dense point cloud data based on the classified point cloud regional data to obtain regional classified point cloud data.

[0033] In the embodiment of the present invention, a point cloud index is established based on the point cloud information stored in each pixel in the point cloud mapping image data, so as to quickly find the original point cloud data corresponding to each pixel. The classified point cloud region data is traversed, and all point clouds in the region are found using the point cloud index according to the boundary information and semantic category labels of each region, and they are segmented from the original regional dense point cloud data. The segmented point cloud data is stored as a new point cloud file, and a semantic category label is added to each point cloud, and finally the point cloud data classified according to the semantic category is obtained.

[0034] The present invention can identify different objects or regions in the image through this step, and assign corresponding semantic labels to each object or region. This semantic segmentation technology helps to understand the image content and provide basic data for subsequent image classification and point cloud processing; image segmentation is performed based on image semantic data, and the image is divided into multiple small blocks with specific semantic classification. This segmentation technology can better process complex images and provide more refined image information for subsequent point cloud data mapping and classification; the point cloud data is mapped with the image to generate a point cloud mapping image. Through point cloud mapping, the point cloud data can be associated with the image pixels so that the spatial position of the point cloud corresponds to the image content, thereby providing key information for subsequent point cloud classification and processing; based on the image classification semantic segmentation data, the point cloud mapping image is classified into point cloud regions. This step can classify the point cloud data according to the semantic information of the image, identify different categories of spatial regions, such as buildings, vegetation, roads, etc., and lay the foundation for subsequent point cloud cutting and processing; the regional dense point cloud data is cut according to the classification results to obtain regional classified point cloud data of different categories. This step can effectively separate different types of spatial data and provide a more refined and accurate point cloud dataset for subsequent spatial analysis, modeling, and applications.

[0035] Preferably, step S2 comprises the following steps: Step S21: Identify the densely built areas in the overall model of the planning area to obtain the densely built areas in the area; Step S22: locating the center of the building-dense area to obtain the center point of the building area; Step S23: Presetting dense building nodes for the overall model of the planning area according to the center point of the building area to obtain dense building nodes; Step S24: constructing a node network for the overall model of the planning area based on the dense building nodes to obtain a regional dense building network; Step S25: Classify the dense building nodes based on the national land planning area image to obtain node classification data, which includes high-ecological nodes and low-ecological nodes.

[0036] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S2 in FIG. 1 , in this embodiment, step S2 includes the following steps: Step S21: Identify the densely built areas in the overall model of the planning area to obtain the densely built areas in the area; In an embodiment of the present invention, all building models are extracted from the overall model of the planning area according to semantic information or geometric features. The planning area is divided into regular grids or an adaptive density clustering algorithm is used to count the number of buildings, floor area or volume in each grid or cluster, and calculate the building density. According to planning requirements or empirical values, a building density threshold is set to distinguish between densely built areas and non-dense areas. Based on the density threshold, a regional growing algorithm or a density clustering algorithm is used to merge adjacent grids or clusters whose density exceeds the threshold to form a connected densely built area.

[0037] Step S22: locating the center of the building-dense area to obtain the center point of the building area; In the embodiment of the present invention, the boundary of each densely built area is extracted by using image processing or three-dimensional model processing algorithm. According to the area boundary, the center point of each densely built area is determined by using methods such as geometric center calculation, centroid calculation or minimum bounding box center calculation.

[0038] Step S23: Presetting dense building nodes for the overall model of the planning area according to the center point of the building area to obtain dense building nodes; In the embodiment of the present invention, node preset rules are formulated according to planning requirements or experience, for example, one node is preset at the center of each building area, or multiple nodes are preset according to the size and shape of the area. According to the preset rules, the node is placed at the center of the building area, or fine-tuned according to the characteristics of the area, for example, the node is placed at a location with convenient transportation or near a landmark building in the area.

[0039] Step S24: constructing a node network for the overall model of the planning area based on the dense building nodes to obtain a regional dense building network; In the embodiment of the present invention, by analyzing the spatial relationship between nodes, such as distance, visibility, accessibility, etc., it is determined whether there is a potential connection between nodes. Using algorithms such as Delaunay triangulation, minimum spanning tree, k-nearest neighbor graph, etc., according to the connectivity between nodes, a node network is constructed to connect related nodes. According to actual conditions, the constructed node network is optimized, for example, by deleting redundant edges, adding necessary edges, adjusting node positions, etc.

[0040] Step S25: Classify the dense building nodes based on the national land planning area image to obtain node classification data, which includes high-ecological nodes and low-ecological nodes.

[0041] In an embodiment of the present invention, the influence area of ​​the node is determined by taking each node as the center according to a preset search radius or other spatial division method. Based on the national land planning area image, the ecological environment indicators in the node influence area are extracted, such as vegetation coverage, water area, green space rate, air quality, etc. According to the extracted ecological indicators, the ecological environment of the node influence area is evaluated by using methods such as hierarchical analysis method and fuzzy comprehensive evaluation method, and the ecological score of each node is calculated. According to the ecological score of the node, a classification threshold is set to divide the node into a high ecological node and a low ecological node.

[0042] The present invention analyzes the overall model of the planning area and identifies the densely built area, which is an important observation of the urban spatial layout. This step helps to understand the development level and building distribution of the region. It provides key information for subsequent planning and design. This is of great significance in optimizing urban space and infrastructure development. Centering the densely built area can quickly determine the geometric center or functional core of the area, providing an important reference point for subsequent functional analysis, traffic planning, resource allocation, etc., which helps to optimize the urban spatial organization and improve the efficiency of urban operation. Presetting dense building nodes according to the regional center point is not simply abstracting the building as a point, but simulating the density and distribution law of the building according to the influence of the regional center, which lays the foundation for the subsequent construction of a more realistic and refined urban model. Constructing a regional dense building network based on dense building nodes is not simply connecting each node, but showing the spatial connection between urban buildings through the network structure, which provides an intuitive basis for analyzing urban structure, traffic flow, information transmission, etc., and helps to deeply understand the spatial organization form and operation law of the city. Classifying nodes based on regional images of national land planning and distinguishing between high-ecological nodes and low-ecological nodes is not a simple spatial analysis, but rather taking ecological factors into consideration. This can assess the impact of urban construction on the ecological environment and provide data support for optimizing urban layout, improving urban ecological benefits, and promoting sustainable urban development.

[0043] Preferably, step S25 comprises the following steps: Step S251: acquiring building density for dense building nodes to obtain node building density data; presetting the influence range of a single building for dense building nodes to obtain single building influence range data; Step S252: Calculate the dense building node influence range of the single building influence range data based on the node building density data to obtain the node influence range data; Step S253: searching for ecological data of the affected area on the land planning area image according to the node affected area data to obtain ecological data of the affected area; Step S254: Compare the ecological data of the affected area with the preset regional ecological data. If the ecological data of the affected area is greater than the preset regional ecological data, the dense building node is marked as a high-ecological node; if the ecological data of the affected area is less than or equal to the preset regional ecological data, the dense building node is marked as a low-ecological node.

[0044] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 2 Detailed step flow diagram of step S25 in FIG. 1 , in this embodiment, step S25 includes the following steps: Step S251: acquiring building density for dense building nodes to obtain node building density data; presetting the influence range of a single building for dense building nodes to obtain single building influence range data; In an embodiment of the present invention, by using existing building vector data or three-dimensional models, the building area ratio of each dense building node within a certain range around the node, for example, a circular area with a radius of 500 meters with the node as the center, is counted to obtain node building density data. According to urban planning specifications or empirical values, the influence range of a single building on the surrounding environment is preset. For example, the influence range of a building with a building height of 30 meters can be set as a circular area with a radius of 100 meters.

[0045] Step S252: Calculate the dense building node influence range of the single building influence range data based on the node building density data to obtain the node influence range data; In the embodiment of the present invention, the influence range of a single building preset for each node is superimposed according to the building density of the node. For example, if the building density of the node is 30%, the radius of the influence range of the single building is reduced to 0.3 times as the actual influence range of the node. The superimposed influence range is corrected in consideration of the actual situation. For example, the boundary of the influence range is adjusted according to the blocking effect of factors such as roads and green spaces on the influence range.

[0046] Step S253: searching for ecological data of the affected area on the land planning area image according to the node affected area data to obtain ecological data of the affected area; In an embodiment of the present invention, by using the land planning area image, index data related to the ecological environment, such as normalized vegetation index, surface temperature, impervious surface ratio, etc., are extracted, and raster data is generated. According to the influence range data of each node, the extracted ecological data is clipped to obtain the ecological data within the influence area of ​​each node. According to the needs, the ecological data within the influence area is statistically analyzed, for example, the average normalized vegetation index, the maximum surface temperature, the impervious surface ratio, etc. are calculated to obtain the comprehensive ecological index of the influence area of ​​each node.

[0047] Step S254: Compare the ecological data of the affected area with the preset regional ecological data. If the ecological data of the affected area is greater than the preset regional ecological data, the dense building node is marked as a high-ecological node; if the ecological data of the affected area is less than or equal to the preset regional ecological data, the dense building node is marked as a low-ecological node.

[0048] In an embodiment of the present invention, the threshold of the expected ecological environment index of the region is preset according to the planning objectives or relevant standards, for example, the average normalized difference vegetation index is greater than 0.6, the maximum surface temperature is less than 35 degrees Celsius, etc. The ecological data of the area affected by each node is compared with the ecological data of the corresponding preset area to determine whether the node reaches the expected ecological environment level. According to the comparison results, the nodes that meet the preset ecological indicators are marked as high ecological nodes, and the nodes that do not meet the preset ecological indicators are marked as low ecological nodes.

[0049] The present invention lays a data foundation for the subsequent calculation of the actual influence range of each node by obtaining the node building density and presetting the influence range of a single building. The influence range of a single building can be preset to consider factors such as building type, height, surrounding environment, etc. according to actual conditions, so that the analysis is closer to reality. The influence range of a single building is corrected according to the node building density, and more accurate node influence range data is calculated, avoiding the rough processing method of simply treating all nodes as having the same influence range, so that the analysis result is more reliable. Based on the node influence range data, the ecological data of the corresponding area in the land planning area image is accurately retrieved, avoiding the inefficient practice of large-scale ecological data analysis of the entire area, and improving the efficiency and accuracy of data processing. By comparing the ecological data of the affected area with the preset regional ecological data, the degree of influence of each node on its surrounding ecological environment can be judged. The node whose ecological data of the affected area is higher than the preset value is marked as a high ecological node, indicating that the ecological environment quality around the node is relatively good, otherwise it is marked as a low ecological node, providing a reference basis for urban planning, associating urban building density with ecological environment data, providing a new perspective and method for evaluating the impact of urban development on the ecological environment, and helping to promote the coordinated development of urban construction and ecological environment.

[0050] Preferably, step S253 includes the following steps: Step S2531: performing geographic model registration on the land planning area image to obtain a regional model registration image; In an embodiment of the present invention, several pairs of control points with the same name are selected on a land planning area image and geographic reference data, such as a topographic map, orthophoto, etc. The control points should be clearly identifiable, evenly distributed, and have a high positioning accuracy. According to the distribution and number of control points, a suitable geometric transformation model is selected, such as an affine transformation, a polynomial transformation, a projection transformation, etc. The optimal transformation parameters are calculated using the selected control points and transformation model so that the land planning area image is spatially aligned with the geographic reference data. According to the calculated transformation parameters, the land planning area image is resampled and converted into the same coordinate system and resolution as the geographic reference data to obtain a regional model registration image.

[0051] Step S2532: marking the influence range of the regional model registration image based on the node influence range data to obtain an influence region marked image; In an embodiment of the present invention, the node influence range data is converted from its original coordinate system to the coordinate system of the regional model registration image. The converted node influence range vector data is rasterized to generate a binary image with the same resolution as the regional model registration image, wherein the pixel value within the influence range is 1 and the pixel value outside the range is 0. The rasterized node influence range image is superimposed on the regional model registration image to obtain an influence region marking image.

[0052] Step S2533: extracting ecological image features according to the impact area mark image to obtain ecological image feature data; In the embodiment of the present invention, image bands that are sensitive to the ecological environment are selected according to the research purpose and data characteristics, such as near-infrared bands, red bands, blue bands, etc. The selected bands are used to calculate characteristic indicators that can reflect the ecological environment status, such as normalized vegetation index, enhanced vegetation index, soil-adjusted vegetation index, ratio vegetation index, etc. The calculated characteristic indicators are stored as new image bands to generate image data containing multiple ecological image features.

[0053] Step S2534: performing data mapping on the impact area mark image based on the ecological image feature data to obtain a feature data mapping image; In the embodiment of the present invention, the ecological image feature data is cropped according to the impact area marking image to obtain the feature data in each node impact area. The feature data of all node impact areas are spliced ​​together to generate a new feature data mapping image. For pixels in the feature data mapping image that do not belong to any node impact area, background filling is performed, for example, filling with a value of 0.

[0054] Step S2535: perform an impact range ecological data search on the feature data mapping image to obtain the impact area ecological data.

[0055] In the embodiment of the present invention, regional statistical analysis is performed on the feature data mapping image according to the boundary of the influence range of each node, for example, the average value, maximum value, minimum value, standard deviation, etc. of each ecological image feature in the influence area of ​​each node is calculated. The statistical results are organized into a table or other data structure to obtain the ecological data of the influence area of ​​each node.

[0056] The present invention performs geographic model registration on the land planning regional image, which is equivalent to aligning the two-dimensional image with the real geographic coordinate system, providing an accurate spatial reference for subsequent spatial analysis and data extraction, and ensuring the accuracy and reliability of the analysis results. Marking the influence range of the regional model registration image based on the node influence range data is equivalent to visualizing the abstract node influence range on the map, providing a clear spatial range for the subsequent accurate extraction of ecological data, and avoiding invalid analysis of irrelevant areas. Extracting ecological image features based on the marked image of the influence area is equivalent to identifying and extracting key information related to the ecological environment from the image, such as vegetation coverage, water area, soil type, etc., providing quantitative ecological indicators for subsequent analysis, making the evaluation results more objective and comparable. Data mapping of the marked image of the influence area based on the ecological image feature data is equivalent to combining the extracted ecological indicators with geographic spatial information to form a visualized ecological data layer, which provides an intuitive visual presentation for subsequent analysis and facilitates the identification and analysis of ecological characteristics of different regions. Retrieving ecological data of the influence range of the feature data mapping image is equivalent to accurately extracting ecological data within the influence range of the node from the data layer, providing a direct data basis for the subsequent evaluation of the impact of the node on the ecological environment, making the evaluation results more convincing. By combining image processing technology with geographic information system technology, accurate extraction of ecological data within the influence range of the node is achieved, providing important data support for urban planning and ecological environmental protection.

[0057] Preferably, step S3 comprises the following steps: Step S31: Perform terrain change superposition analysis on high ecological nodes according to the overall model of the planning area to obtain terrain change trend data; In an embodiment of the present invention, elevation data is extracted from the overall model of the planning area to generate a digital elevation model. Based on the digital elevation model data, a spatial analysis algorithm is used to calculate the terrain slope and generate a slope map. A circular buffer zone is generated based on a preset buffer radius with a high ecological node as the center. The high ecological node buffer zone and the slope map are spatially superimposed and analyzed, and the slope change trend of the terrain in the buffer zone is statistically analyzed, for example, the average slope, maximum slope, slope change rate, etc. are calculated, and terrain change trend data is generated.

[0058] Step S32: setting ecological buffer areas for high ecological nodes and low ecological nodes according to the national land planning area image to obtain node ecological buffer area data; In an embodiment of the present invention, different ecological buffer zone radii are set according to the node type, including high ecological nodes or low ecological nodes, and the sensitivity of the surrounding environment. For example, the buffer zone radius of a high ecological node can be set larger. With the node as the center, a circular, elliptical or other shaped buffer zone is generated according to the set buffer zone radius. The buffer zones of adjacent nodes can be merged according to the actual situation to avoid the situation where the buffer zones overlap or are too fragmented.

[0059] Step S33: marking the restricted development area on the overall model of the planned area based on the terrain change trend data and the buffer range data to obtain the planned restricted development area; In an embodiment of the present invention, by formulating restricted development rules based on terrain change trend data and ecological buffer zone data, for example, areas with slopes greater than a certain value and areas located in high ecological node buffer zones are prohibited from carrying out development and construction activities. The spatial query and overlay analysis functions of the geographic information system are used to apply the restricted development rules to the overall model of the planning area to identify all areas that meet the restricted development conditions. The identified restricted development areas are marked, for example, by color differentiation or adding annotation information to the overall model of the planning area.

[0060] Step S34: performing regional connectivity analysis on the overall model of the planning area according to the regional dense building network to obtain connectivity data of the planning area; In the embodiment of the present invention, a suitable network analysis algorithm is selected, such as shortest path analysis, service area analysis, network connectivity analysis, etc., to evaluate the connectivity of the region. According to the analysis objectives and actual conditions, network analysis parameters are set, such as analysis radius, impedance factor, connectivity index, etc. The selected network analysis algorithm and parameter settings are used to calculate the connectivity indexes between each node or region in the planning area, such as distance, time, cost, etc.

[0061] Step S35: Based on the planned restricted development area and the connectivity data of the planned area, the overall model of the planned area is subjected to national land planning to obtain a national land space planning scheme.

[0062] In an embodiment of the present invention, the spatial layout is optimized based on the planned restricted development area and the connectivity data of the planned area. For example, the restricted development area is avoided for construction land planning, and the transportation network construction is strengthened to improve regional connectivity. According to the regional resource and environmental carrying capacity, development potential and functional positioning, the planned area is divided into different functional zones, such as ecological protection areas, agricultural development areas, and urban construction areas. The formulated national land space planning scheme is evaluated, such as environmental impact assessment and social and economic benefit assessment.

[0063] The present invention can identify the trend of terrain changes, such as slope, altitude, etc., by superimposing and analyzing the terrain changes on high ecological nodes, so as to provide a basis for the subsequent demarcation of restricted development areas, avoid large-scale development and construction in ecologically sensitive areas, and effectively protect the ecological environment. Setting ecological buffer areas of different sizes for high ecological nodes and low ecological nodes embodies the principle of differentiated protection, gives greater protection to areas with high ecological value, and moderately develops areas with relatively low ecological value, taking into account the needs of ecological protection and urban development. Based on terrain change trend data and ecological buffer area data, restricted development areas, such as areas with steep slopes and high ecological values, can be accurately demarcated, and they can be removed from the developable areas, avoiding damage to the ecological environment from the source and ensuring regional ecological security. By performing connectivity analysis on the regional dense building network, the urban development axis and ecological corridor can be identified, providing a reference for the subsequent optimization of urban spatial layout, ensuring that urban planning takes into account the connection between regions, and creating an efficient and functional urban network. Avoid disorderly urban expansion and promote harmonious coexistence between cities and nature. Based on restricted development areas and regional connectivity data for national land planning, a more scientific and reasonable land use plan can be formulated. Taking ecological factors as an important consideration in national land space planning provides new ideas and methods for formulating more scientific and reasonable planning schemes, which helps promote sustainable urban development.

[0064] Preferably, step S31 includes the following steps: Step S311: Acquire historical terrain data of high ecological nodes to obtain node historical terrain data; In the embodiment of the present invention, by clarifying the spatial scope of the required historical terrain data, with the high ecological node as the center, the appropriate buffer zone, time span and resolution are determined to select the appropriate data source. The historical terrain data is obtained from reliable data sources, such as public topographic maps, historical remote sensing images, LiDAR data, etc. The obtained historical terrain data is preprocessed, such as coordinate system conversion, geometric correction, data format conversion, etc., to ensure the consistency and accuracy of the data.

[0065] Step S312: performing time series segmentation according to the node historical terrain data to obtain node time series terrain data; In an embodiment of the present invention, the historical terrain data is segmented by selecting an appropriate time interval according to the research purpose and data characteristics, for example, 5 years, 10 years or other time intervals are used as a period. With the high ecological node as the center, the historical terrain data of each time period is clipped according to the preset buffer range to obtain the terrain data of each node in different time periods. The clipped node historical terrain data is organized in chronological order to form node time series terrain data, for example, stored in the form of multi-phase DEM data or point cloud data.

[0066] Step S313: constructing a time series terrain model for the node time series terrain data to obtain a node time series terrain model; In the embodiment of the present invention, a suitable temporal terrain model construction method is selected according to the data type and accuracy requirements, such as a TIN-based model, a grid-based model, a point cloud-based model, etc. Model parameters are set according to data characteristics and modeling methods, such as grid size, interpolation method, model accuracy, etc. The temporal terrain data of each node is modeled using the selected modeling method and parameter settings to generate a three-dimensional model sequence that can reflect the changes in the terrain of the node over time.

[0067] Step S314: performing time series superposition analysis on the overall model of the planning area based on the node time series terrain model to obtain a time series evolution terrain model; In the embodiment of the present invention, the temporal terrain model of each node is aligned with the overall model of the planning area to ensure that they are in the same spatial reference system. According to the time series, the temporal terrain model of each node is fused with the overall model of the planning area to generate a temporal evolution terrain model containing multiple time nodes. Methods such as model splicing and data fusion can be used to ensure the integrity and continuity of the model.

[0068] Step S315: Analyze the change trend of high ecological nodes based on the time-series evolution terrain model to obtain terrain change trend data.

[0069] In the embodiment of the present invention, by using the time series evolution terrain model, the difference method, trend analysis method and other methods are used to analyze the terrain changes in the high ecological node area, such as elevation changes, slope changes, volume changes, etc. The detected terrain changes are statistically analyzed, for example, the change rate, change direction, change amplitude, etc. are calculated, and the trend of terrain changes, such as erosion, accumulation, stability, etc., is identified.

[0070] The present invention lays the foundation for analyzing terrain changes by collecting historical terrain information of high ecological nodes. By obtaining data from different periods, planners can understand the terrain evolution process of the area. Historical terrain data includes geomorphic changes, hydrological patterns, and land use patterns, providing valuable background information for subsequent analysis and decision-making. It helps to identify patterns and trends in terrain changes. Through time series analysis, planners can discover important terrain changes and assess the potential impact of these changes on ecosystems and the environment. This method provides a powerful tool for understanding the dynamic changes of terrain within urban areas. Models are created based on time-series segmented data to visualize and quantify terrain changes. The time series terrain model is able to show the changes and evolution of terrain features in different periods. This model provides a direct visual reference for subsequent analysis and comparison. By superimposing the time series terrain model with the overall model of the planning area, the impact of terrain changes on urban space can be analyzed. This step reveals how terrain changes affect ecological nodes, water flow patterns, and regional connectivity. Help make decisions that take terrain change factors into account, such as flood control, land use, and infrastructure planning. Based on the time-series evolution terrain model, the terrain change trend of high ecological node areas, such as slope change and elevation change, can be analyzed to predict the future direction of terrain evolution and provide more forward-looking guidance for the demarcation of restricted development areas, such as demarcating areas where severe soil erosion is expected to occur into restricted development areas. By analyzing historical terrain data and incorporating the time dimension into terrain analysis, more scientific and forward-looking decision-making support can be provided for national land space planning.

[0071] Preferably, step S32 includes the following steps: Step S321: acquiring node ecological data of high ecological nodes and low ecological nodes to obtain node ecological data; performing initial setting of node ecological buffer zones for high ecological nodes and low ecological nodes according to the node ecological data to obtain node initial ecological buffer zones; In an embodiment of the present invention, ecological and environmental data around high-ecological nodes and low-ecological nodes are collected, such as land use type, vegetation coverage, water body distribution, species diversity, ecological service functions, etc. Data sources may include remote sensing images, field surveys, ecological and environmental databases, etc. Collect ecological and environmental data around high-ecological nodes and low-ecological nodes, such as land use type, vegetation coverage, water body distribution, species diversity, ecological service functions, etc. Data sources may include remote sensing images, field surveys, ecological and environmental databases, etc. According to the ecological value assessment results of the nodes, different initial ecological buffer zone radii are set. For example, the higher the ecological value of the node, the larger the initial buffer zone radius should be.

[0072] Step S322: performing node ecological data mapping on the land planning area image based on the node ecological data to obtain a node ecological data image; In an embodiment of the present invention, by converting the node ecological data from different sources and different formats into raster data, it is ensured that the data can be spatially superimposed and analyzed. All node ecological data are uniformly projected to the same coordinate system and resolution as the land planning area image. The processed node ecological data is mapped to the land planning area image to generate a thematic layer containing node ecological information, such as a land use type map, a vegetation coverage map, a water body distribution map, etc.

[0073] Step S323: performing node ecological characteristic data analysis on the node ecological data image to obtain node ecological characteristic data, which includes node hydrological data and node biodiversity data; In an embodiment of the present invention, the hydrological characteristics of the area around the node, such as the distribution of rivers, lakes, and wetlands, water system connectivity, water conservation capacity, etc., are analyzed based on water body distribution maps, DEM data, etc., to obtain node hydrological data. Based on land use type maps, vegetation coverage maps, species distribution data, etc., the biodiversity characteristics of the area around the node, such as species richness, number of endemic species, habitat types, etc., are analyzed to obtain node biodiversity data.

[0074] Step S324: performing node ecological sensitivity assessment according to the node hydrological data and the node biodiversity data to obtain node ecological sensitivity assessment data; In an embodiment of the present invention, a node ecological sensitivity evaluation index system is constructed according to the characteristics of the study area and the planning objectives, and key indicators that can reflect the hydrological characteristics and biodiversity characteristics are selected. The weights of each indicator are determined by using methods such as hierarchical analysis method and expert scoring method to reflect the degree of its contribution to ecological sensitivity. Using the selected indicators, weights and node ecological characteristic data, the ecological sensitivity score of each node is calculated by using weighted summation, fuzzy comprehensive evaluation and other methods, and classified into different levels, such as high sensitivity area, medium sensitivity area, low sensitivity area, etc.

[0075] Step S325: performing buffer expansion processing on the initial ecological buffer zone of the node based on the node ecological sensitivity assessment data to obtain an expanded ecological buffer zone; In an embodiment of the present invention, a buffer zone expansion rule is formulated based on the node ecological sensitivity assessment results. For example, different expansion distances or expansion ratios can be set according to the sensitivity level. The higher the sensitivity, the greater the expansion. Using GIS spatial analysis tools, the initial ecological buffer zone of the node is expanded according to the set expansion rules. The expansion method can select a simple buffer zone analysis, or can use methods such as cost distance analysis and corridor analysis to better consider the influence of factors such as topography and hydrology.

[0076] Step S326: Integrate the regional data of the node initial ecological buffer area and the extended ecological buffer area to obtain the node ecological buffer area data.

[0077] In the embodiment of the present invention, a layer containing all buffer zone information is generated by merging the data of the node initial ecological buffer zone and the extended ecological buffer zone. Attribute information is added to the merged buffer zone layer, such as the node number, the buffer zone type, including the initial buffer zone or the extended buffer zone, and the ecological sensitivity level.

[0078] The present invention obtains node ecological data and sets the initial ecological buffer zone of the node based on the data, which reflects the differentiated protection ideas for nodes with different ecological values ​​and provides a basis for subsequent refined adjustments. The node ecological data is mapped to the image of the national land planning area to visualize the abstract data and provide an intuitive basis for subsequent spatial analysis and feature extraction. The node ecological data image is analyzed to extract key ecological feature data, such as hydrological data and biodiversity data, to provide quantitative indicators for evaluating the ecological sensitivity of the node, so that the evaluation results are more objective and comparable. According to indicators such as hydrological data and biodiversity data, the ecological sensitivity of the node is evaluated, and the areas where the ecosystem is relatively fragile and need to be protected can be identified, providing a basis for the expansion of the buffer zone. Based on the node ecological sensitivity evaluation data, the initial ecological buffer zone is expanded, which reflects the enhanced protection of ecologically sensitive areas and can more effectively reduce the negative impact of urban development on the ecological environment. The initial ecological buffer zone and the extended ecological buffer zone are integrated to form the final node ecological buffer zone data, which provides an important ecological constraint boundary for national land space planning and ensures the coordination of urban development and ecological protection. It provides a comprehensive framework for urban planning and balances the needs of ecological protection and urban development. The integrated data can be used to formulate land use strategies, design green spaces and implement ecological protection measures, thereby promoting the harmonious coexistence of cities and the natural environment. The refined setting of ecological buffer zones has been achieved, providing technical support for the construction of a national spatial pattern that takes into account both urban development and ecological security.

[0079] Preferably, the present invention further provides a data processing system applied to national land space planning, which is used to execute the data processing method applied to national land space planning as described above, and the data processing system applied to national land space planning includes: The planning area model construction module is used to obtain regional images of the national land planning area to obtain the national land planning area image; generate dense point clouds based on the national land planning area image to obtain regional dense point cloud data; construct a three-dimensional model of the regional dense point cloud data based on the national land planning area image to obtain a three-dimensional model of the planning area and a three-dimensional model of regional buildings; integrate the three-dimensional model of the planning area and the three-dimensional model of regional buildings to obtain an overall model of the planning area; The regional node setting module is used to identify the regional building-dense areas of the overall model of the planning area to obtain the regional building-dense areas; preset the building-dense nodes of the overall model of the planning area based on the regional building-dense areas to obtain the dense building nodes; construct the node network of the overall model of the planning area based on the dense building nodes to obtain the regional dense building network; classify the dense building nodes based on the land planning area image to obtain the node classification data; The national land space planning module is used to perform restricted development area analysis on the node classification data according to the overall model of the planning area and the image of the national land planning area to obtain the planned restricted development area; to perform regional connectivity analysis on the overall model of the planning area according to the regional dense building network to obtain the connectivity data of the planning area; to perform national land planning on the overall model of the planning area based on the planned restricted development area and the connectivity data of the planning area to obtain the national land space planning scheme.

[0080] To summarize, the present invention provides a data processing method and system applied to national land space planning. The data processing system applied to national land space planning consists of a planning area model construction module, a regional node setting module, and a national land space planning module. It can implement any data processing method applied to national land space planning described in the present invention, and is used to combine the operations between computer programs running on each module to implement any data processing method applied to national land space planning. The internal structures of the system cooperate with each other, which can greatly reduce repetitive work and manpower investment, and can quickly and effectively provide a more accurate and efficient data processing process applied to national land space planning, thereby simplifying the operating procedures of the data processing system applied to national land space planning.

[0081] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A data processing method for national land space planning, characterized in that: The following steps are involved: Step S1: acquiring a regional image of the national land planning area to obtain a national land planning regional image; Generate dense point cloud based on the land planning area image to obtain regional dense point cloud data; construct a three-dimensional model of the regional dense point cloud data based on the land planning area image to obtain a three-dimensional model of the planning area and a three-dimensional model of regional buildings; integrate the three-dimensional model of the planning area and the three-dimensional model of regional buildings to obtain an overall model of the planning area; Step S2: Identify the densely built areas in the overall model of the planning area to obtain the densely built areas in the area; Based on the regional building-dense areas, the overall model of the planning area is pre-set with building-dense nodes to obtain dense building nodes; Based on dense building nodes, the node network of the overall model of the planning area is constructed to obtain a regional dense building network; Based on the national land planning area image, the dense building nodes are classified to obtain node classification data; Step S3: performing restricted development area analysis on the node classification data according to the overall model of the planning area and the national land planning area image to obtain the planned restricted development area; Conduct regional connectivity analysis on the overall model of the planning area based on the regional dense building network to obtain the connectivity data of the planning area; Based on the planning restricted development area and the planning area connectivity data, the overall model of the planning area is used for national land planning to obtain the national land space planning scheme.

2. The data processing method for national land space planning according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: acquiring a regional image of the national land planning area to obtain a national land planning regional image; Step S12: generating a dense point cloud according to the land planning area image to obtain regional dense point cloud data; Step S13: classifying the regional dense point cloud data based on the national land planning regional image to obtain regional classified point cloud data; Step S14: extracting regional building point cloud data from the regional classification point cloud data to obtain regional building point cloud data; Step S15: constructing a three-dimensional model of the regional terrain according to the regional dense point cloud data to obtain a three-dimensional model of the planning area; constructing a three-dimensional model of the regional buildings according to the regional building point cloud data to obtain a three-dimensional model of the regional buildings; Step S16: positioning the building model of the planned area three-dimensional model based on the regional building point cloud data to obtain building model positioning data; Step S17: integrating the regional building three-dimensional model and the planning area three-dimensional model according to the building model positioning data to obtain an overall model of the planning area.

3. The data processing method for national land space planning according to claim 2 is characterized in that: Step S13 includes the following steps: Step S131: performing image semantic data segmentation on the land planning area image to obtain image semantic data; Step S132: semantically classify and segment the land planning area image based on the image semantic data to obtain image classification semantic segmentation data; Step S133: performing data mapping on the land planning area image based on the regional dense point cloud data to obtain point cloud mapping image data; Step S134: performing point cloud region classification on the point cloud mapping image data according to the image classification semantic block data to obtain classified point cloud region data; Step S135: performing point cloud classification and cutting on the regional dense point cloud data based on the classified point cloud regional data to obtain regional classified point cloud data.

4. The data processing method for national land space planning according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: Identify the densely built areas in the overall model of the planning area to obtain the densely built areas in the area; Step S22: locating the center of the building-dense area to obtain the center point of the building area; Step S23: Presetting dense building nodes for the overall model of the planning area according to the center point of the building area to obtain dense building nodes; Step S24: constructing a node network for the overall model of the planning area based on the dense building nodes to obtain a regional dense building network; Step S25: Classify the dense building nodes based on the national land planning area image to obtain node classification data, which includes high-ecological nodes and low-ecological nodes.

5. The data processing method for national land space planning according to claim 4 is characterized in that: Step S25 includes the following steps: Step S251: acquiring building density for dense building nodes to obtain node building density data; presetting the influence range of a single building for dense building nodes to obtain single building influence range data; Step S252: Calculate the dense building node influence range of the single building influence range data based on the node building density data to obtain the node influence range data; Step S253: searching for ecological data of the affected area on the land planning area image according to the node affected area data to obtain ecological data of the affected area; Step S254: Compare the ecological data of the affected area with the preset regional ecological data. If the ecological data of the affected area is greater than the preset regional ecological data, the dense building node is marked as a high-ecological node; if the ecological data of the affected area is less than or equal to the preset regional ecological data, the dense building node is marked as a low-ecological node.

6. The data processing method for national land space planning according to claim 5 is characterized in that: Step S253 includes the following steps: Step S2531: performing geographic model registration on the land planning area image to obtain a regional model registration image; Step S2532: marking the influence range of the regional model registration image based on the node influence range data to obtain an influence region marked image; Step S2533: extracting ecological image features according to the impact area mark image to obtain ecological image feature data; Step S2534: performing data mapping on the impact area mark image based on the ecological image feature data to obtain a feature data mapping image; Step S2535: perform an impact range ecological data search on the feature data mapping image to obtain the impact area ecological data.

7. The data processing method for national land space planning according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: Perform terrain change superposition analysis on high ecological nodes according to the overall model of the planning area to obtain terrain change trend data; Step S32: setting ecological buffer areas for high ecological nodes and low ecological nodes according to the national land planning area image to obtain node ecological buffer area data; Step S33: marking the restricted development area on the overall model of the planned area based on the terrain change trend data and the buffer range data to obtain the planned restricted development area; Step S34: performing regional connectivity analysis on the overall model of the planning area according to the regional dense building network to obtain connectivity data of the planning area; Step S35: Based on the planned restricted development area and the connectivity data of the planned area, the overall model of the planned area is subjected to national land planning to obtain a national land space planning scheme.

8. The data processing method for national land space planning according to claim 7 is characterized in that: Step S31 includes the following steps: Step S311: Acquire historical terrain data of high ecological nodes to obtain node historical terrain data; Step S312: performing time series segmentation according to the node historical terrain data to obtain node time series terrain data; Step S313: constructing a time series terrain model for the node time series terrain data to obtain a node time series terrain model; Step S314: performing time series superposition analysis on the overall model of the planning area based on the node time series terrain model to obtain a time series evolution terrain model; Step S315: Analyze the change trend of high ecological nodes based on the time-series evolution terrain model to obtain terrain change trend data.

9. The data processing method for national land space planning according to claim 7 is characterized in that: Step S32 includes the following steps: Step S321: acquiring node ecological data of high ecological nodes and low ecological nodes to obtain node ecological data; performing initial setting of node ecological buffer zones for high ecological nodes and low ecological nodes according to the node ecological data to obtain node initial ecological buffer zones; Step S322: performing node ecological data mapping on the land planning area image based on the node ecological data to obtain a node ecological data image; Step S323: performing node ecological characteristic data analysis on the node ecological data image to obtain node ecological characteristic data, which includes node hydrological data and node biodiversity data; Step S324: performing node ecological sensitivity assessment according to the node hydrological data and the node biodiversity data to obtain node ecological sensitivity assessment data; Step S325: performing buffer expansion processing on the initial ecological buffer zone of the node based on the node ecological sensitivity assessment data to obtain an expanded ecological buffer zone; Step S326: Integrate the regional data of the node initial ecological buffer area and the extended ecological buffer area to obtain the node ecological buffer area data.

10. A data processing system applied to national land space planning, characterized in that: Used to execute the data processing method applied to national land space planning as claimed in claim 1, the data processing system applied to national land space planning comprises: The planning area model construction module is used to obtain regional images of the national land planning area to obtain the national land planning area image; generate dense point clouds based on the national land planning area image to obtain regional dense point cloud data; construct a three-dimensional model of the regional dense point cloud data based on the national land planning area image to obtain a three-dimensional model of the planning area and a three-dimensional model of regional buildings; integrate the three-dimensional model of the planning area and the three-dimensional model of regional buildings to obtain an overall model of the planning area; The regional node setting module is used to identify the regional building-dense areas of the overall model of the planning area to obtain the regional building-dense areas; preset the building-dense nodes of the overall model of the planning area based on the regional building-dense areas to obtain the dense building nodes; construct the node network of the overall model of the planning area based on the dense building nodes to obtain the regional dense building network; classify the dense building nodes based on the land planning area image to obtain the node classification data; The national land space planning module is used to perform restricted development area analysis on the node classification data according to the overall model of the planning area and the image of the national land planning area to obtain the planned restricted development area; to perform regional connectivity analysis on the overall model of the planning area according to the regional dense building network to obtain the connectivity data of the planning area; to perform national land planning on the overall model of the planning area based on the planned restricted development area and the connectivity data of the planning area to obtain the national land space planning scheme.

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