Method for determining territorial space planning region boundary based on multi-dimensional data

Abstract

The invention relates to the technical field of data analysis, in particular to a method for determining a territorial space planning region boundary based on multi-dimensional data, and the method comprises the steps: uniformly mapping geographic space, social economic and ecological sensitive attributes through regular space unit grids, and eliminating the problems of scale dislocation and boundary jump of multi-source data; attribute mutual exclusion conflict nodes are accurately positioned based on gradient direction deviation detection, and a dynamic coupling network quantization space conduction relation is constructed by using non-conflict nodes; the dynamic connection weight is globally converged through iterative compression operation, the limitation of a static model is broken through, and local conflict interference is restrained; finally, on the basis of topological deformation driven by weight space gradient, a closed boundary line is output on the premise that space continuity is maintained, the problems of resource continuity splitting and attribute logic fracture are solved, and multi-source coordination adaptation of the planning boundary and the natural-economic-ecological gradient is achieved.

Description

Technical Field

[0001] The present invention relates to the field of data analysis technology, and in particular to a method for determining regional boundaries of land space planning based on multidimensional data. Background Art

[0002] The current regional boundary delineation in national land space planning faces the dual challenges of multidimensional data integration bottlenecks and technical limitations. At the data level, scale mismatches exist between geospatial raster data (such as topography and vegetation cover), socioeconomic point statistics (such as population density and GDP distribution), and ecologically sensitive vector data (such as protected area boundaries). This creates conflicts between the legal nature of statistical units and the continuity of natural units. Manual overlay analysis struggles to quantify the mutually exclusive gradient relationship between ecological protection zones and urban development boundaries, leading to a common disconnect in attribute logic in national land space delineation.

[0003] On a technical level, existing spatial network models often rely on static weights or single-attribute transmission mechanisms, failing to dynamically coordinate the conflicting transmission of multidimensional attributes within spatial units (such as the tension between ecological protection nodes and economic development nodes). This can cause generated boundaries to exhibit geometric jumps in gradient abrupt zones or administrative boundaries to fragment the continuity of resource distribution. As the development of a national land space planning implementation and supervision system progresses, there is an urgent need to address the multi-source dynamic coordination challenge of boundary demarcation to support the modernization and transformation of spatial governance. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for determining the boundaries of national land space planning areas based on multidimensional data to solve the problems raised in the above-mentioned background technology. The core problems to be solved include how to accurately identify spatially exclusive areas with multidimensional attributes to solve the conflicting contradictions between natural geographical distribution, socio-economic activities and ecological protection demands within micro units; how to generate continuous boundary lines based on dynamic network conduction to solve the problem of spatial adaptation mismatch between planning zoning boundaries and geographical gradient changes and resource distribution patterns.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solution: a method for determining the boundaries of land space planning areas based on multidimensional data, the method comprising the following steps:

[0006] S1. Use the regular grid method to generate a continuous and seamless polygonal spatial unit grid to ensure full coverage and uniform geometric rules; define a unique geographic coordinate range using the latitude and longitude coordinates and side lengths of the lower left corner, record the positional relationship of adjacent grids through topological relationship indexes, and establish a traceable spatial continuity benchmark; calculate the unit average value by clipping raster data to generate geographic spatial attributes and quantify the distribution characteristics of natural geographic entities; for point statistical data, if the grid contains only a single statutory statistical unit, the attributes are fully inherited; if it covers multiple units, weighted aggregation is used to generate socioeconomic attributes, preserving the integrity of the statutory statistical units while eliminating the patch effect; based on the vector protected area boundary, the normalized assignment of ecological elements is used to generate ecological sensitivity attributes, accurately characterizing the spatial overlap intensity between protected areas and development zones;

[0007] The geographic spatial attributes, socioeconomic attributes, and ecologically sensitive attributes are aligned according to the spatial unit grid. The principal value merging method is used to preferentially inherit the attributes with the highest proportion for non-spatial data to avoid attribute fragmentation. Spatial continuous variables are converted using the gradient-preserving interpolation method to eliminate value jumps at unit boundaries.

[0008] S2. Calculate the directional deviation of the multi-attribute gradients within the same node. If it exceeds the preset tolerance range, mark the conflict node and accurately locate the mutually exclusive areas of geographic space, socio-economic and ecologically sensitive attributes; extract the spatial adjacency and attribute similarity of non-conflicting nodes through topological relationship indexing, construct an implicit association matrix, and establish a spatial conduction infrastructure; based on the spatial proximity conduction rules and attribute synergy rules, combined with the degree of attribute difference and the spatial distance attenuation factor, generate dynamic connection weights to quantify the spatial attenuation correlation between conflict nodes and non-conflicting nodes; form a dynamic coupling network with network nodes as entities and dynamic connection weights as edges to realize the spatial dynamic conduction of multi-dimensional conflicts.

[0009] S3. Based on the global weight distribution discreteness of the network, perform directional compression adjustment on the dynamic connection weights of the conflicting nodes to gradually suppress the fluctuation interference of local conflicts; continue to reduce the overall fluctuation amplitude of the weights until the distribution discreteness falls into the preset stable fluctuation range in multiple consecutive rounds of iterations, output the converged weights, and generate a spatially coordinated and globally stable weight distribution.

[0010] S4. Based on the topological relationship index, the convergence weight is mapped to the spatial unit grid to form a weight space, and the geometric correspondence between the weight and the space is established; the spatial gradient distribution of the weight value is detected to drive the displacement and deformation of the grid node, so that the boundary line naturally adapts to the geographical gradient change; the topological adjacency relationship between units is maintained to ensure the geometric continuity of the boundary; non-conflicting nodes are used as anchor points to constrain excessive offset and protect the spatial stability of key functional areas; the boundary line of the continuous closed area is output, whose geometric accuracy inherits the grid division rules and the spatial logic integrates the coordination relationship of multiple source attributes, and simultaneously achieves the accuracy of the boundary morphology and the logical consistency of multiple attributes.

[0011] Compared with the prior art, the present invention has the following beneficial effects:

[0012] A spatial benchmark that is continuous across the entire region is established through a regular grid, achieving accurate mapping and smooth alignment of geographic, spatial, socio-economic, and ecologically sensitive attributes, ensuring that multi-source data eliminate boundary jumps and attribute fragments under a unified framework; based on gradient deviation detection and dynamic coupling networks, attribute-exclusive areas are accurately located and the spatial attenuation law of conflicts is quantified, combined with iterative compression optimization to generate a globally coordinated weight distribution; finally, relying on topological deformation driven by weight gradients, while maintaining spatial continuity and stability of key functional areas, outputs boundary lines with precise geometric forms and logical integration of multi-source attributes, thereby improving the scientificity, coordination, and feasibility of national land space planning boundaries. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 Schematic diagram of the method steps of the present invention. DETAILED DESCRIPTION

[0014] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only 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 ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0015] See also Figure 1 The present invention provides a technical solution: a method for determining the boundaries of a national land space planning area based on multidimensional data, comprising the following steps:

[0016] S1. According to the accuracy requirements of national land space planning, the target planning area is divided into a continuous and seamless polygonal (such as quadrilateral or hexagonal) spatial unit grid using the regular grid method (the side length can be dynamically adjusted according to the planning level, such as 100m×100m for urban level and 1km×1km for ecological protection area); each spatial unit is given a unique geographic coordinate range (such as the latitude and longitude of the lower left corner + side length definition), and the position of adjacent units is recorded through the topological relationship index to ensure spatial continuity; the regular grid method is specifically an operating method for dividing the target planning area into a continuous and seamless polygonal spatial unit grid. This method uses geometric figures with preset side lengths (such as quadrilaterals or hexagons) as basic units, defines the geographic coordinate range of each unit by calculating the latitude and longitude coordinates of the lower left corner of the grid and a fixed side length, and establishes a topological relationship index to record the adjacent position relationship between units to ensure the seamless continuity of the grid in space, and the grid side length can be dynamically adjusted according to different planning levels to adapt to the accuracy requirements.

[0017] Clip raster data according to the coordinate range of spatial units, calculate the average value within the unit, and generate geospatial attributes (such as elevation and slope) to represent the quantitative indicators of the spatial distribution and morphology of natural geographical entities;

[0018] Overlay analysis based on point-based statistical data is performed based on the spatial relationship between the spatial unit grid and the predetermined statistical unit. If the spatial unit grid contains only a single statistical unit, the socioeconomic attributes of the statistical unit (such as population density and GDP) are fully inherited. If the spatial unit grid covers multiple statistical units, the socioeconomic attributes are generated through weighted aggregation. The statistical unit is a closed geographic area that carries official statistical data (such as population census areas and economic census areas) and has legally fixed boundaries. Based on the vector protection area boundary, the ecological elements in the spatial unit are normalized and assigned values ​​to generate ecologically sensitive attributes (such as species distribution and vegetation index). This process maps geographic spatial attributes, socioeconomic attributes, and ecologically sensitive attributes to the spatial unit grid.

[0019] For non-spatial data (such as economic statistics in socioeconomic attributes), the principal value merging method is used when converting to spatial units (statistical unit attributes that account for >60% of the unit are preferentially inherited); for spatial continuous variables (such as slope in geographic spatial attributes), the gradient-preserving interpolation method is used to avoid value jumps at unit boundaries. This process aligns geographic spatial attributes, socioeconomic attributes, and ecologically sensitive attributes according to the spatial unit grid; where:

[0020] The principal value merging method is a conversion method for processing non-spatial data during the data alignment process. When the spatial unit grid overlaps with the predetermined statistical unit (a closed geographical partition with a legally fixed boundary), the statistical unit is completely or partially covered by the spatial unit. The spatial proportion of each statistical unit within the spatial unit is calculated. If the coverage area of ​​a single statistical unit exceeds the preset principal value threshold (such as 60%), the socioeconomic attribute value of the statistical unit is directly inherited; otherwise, weighted aggregation processing is required to solve the attribute mapping problem of non-spatial data to the spatial unit grid.

[0021] Gradient-preserving interpolation is a method for processing spatial continuous variables (such as slope and elevation) during data alignment: based on the coordinates of the spatial unit grid boundary, a spatial gradient function (such as a linear or spline interpolation model) is established for the attribute values ​​at the boundaries of adjacent units. By introducing gradient continuity constraints on the unit attributes on both sides of the boundary, the interpolated unit boundary values ​​maintain a smooth transition, avoiding spatial value jumps caused by unit division.

[0022] Step S1 dynamically adjusts the size of the regular grid according to the planning accuracy, defines the unique geographic range of the unit by the coordinate of the lower left corner + the side length, and ensures spatial continuity by topological indexing; uses the grid clipping mean method to generate geographic spatial attributes, principal value merging or weighted aggregation to process socioeconomic attributes, and normalizes the proportion of ecological factors to generate ecologically sensitive attributes. The principal value merging method and gradient-preserving interpolation method are used to eliminate the fragmentation of non-spatial data and the boundary jump of spatial continuous variables, respectively, thereby breaking through the scale mismatch contradiction between geographic spatial raster, socioeconomic point statistics and ecologically sensitive vector data.

[0023] S2. Use the spatial units generated by S1 as network nodes and perform mutual exclusivity analysis on the multi-source attributes (geographic spatial attributes, socioeconomic attributes, and ecological sensitivity attributes) superimposed within each node; by calculating the spatial change direction of different attribute values ​​within the same spatial unit (for example, the contradiction in the spatial distribution trend between high-value areas of ecological sensitivity and high-value areas of development intensity economic attributes), detect the consistency of their gradient directions to perform gradient direction deviation detection; if the directional deviation of the gradients of different attributes within the unit exceeds the preset tolerance range (for example, the ecological protection gradient and the economic development gradient form a significant reverse conflict), the spatial unit is marked as a conflict node.

[0024] Based on the topological relationship index of spatial units (recording the positions of adjacent units), the spatial adjacency and attribute similarity between all non-conflicting nodes are extracted to construct an implicit association matrix. This implicit association matrix generates dynamic connection weights for each conflicting node pointing to adjacent non-conflicting nodes through the spatial proximity conduction rule (distance decay effect) and attribute synergy rule (attribute type correlation). The weight value is determined by the degree of attribute difference between the conflicting and non-conflicting nodes and the spatial distance decay factor, reflecting the potential possibility of conflicting nodes to coordinate attributes through the surrounding stable area. That is, based on the topological relationship index, the spatial distance between the conflicting node and each non-conflicting node is extracted, and the spatial proximity weight component is calculated using the distance decay function (such as the inverse distance weight model). The similarity between the conflicting and non-conflicting nodes in the same attribute dimension (such as economic attributes or ecological attributes) is calculated, and the attribute synergy weight component is generated by normalizing the degree of attribute difference. The spatial proximity weight component and the attribute synergy weight component are weighted and fused to form the final dynamic connection weight, so that the weight value simultaneously reflects the spatial distance decay effect and the attribute difference coordination requirement.

[0025] Taking the spatial unit grid as the network node entity and the generated dynamic connection weight as the edge relationship strength between nodes, a dynamic coupling network with full domain coverage is formed, in which non-conflicting nodes constitute the stable topological skeleton of the network, and conflicting nodes are elastically associated with stable areas through dynamic weights, forming a spatial relationship network that can be adaptively adjusted according to weight changes.

[0026] Step S2 marks conflicting nodes through gradient direction deviation detection; extracts the adjacency relationship and attribute similarity of non-conflicting nodes based on the topological index, and generates dynamic connection weights by combining the spatial distance attenuation factor and the attribute collaboration rule; constructs a dynamic coupling network with non-conflicting nodes as the stable skeleton and conflicting nodes as the elastic association, realizes the spatial attenuation conduction of attribute conflicts, thereby overcoming the static weight limitations of the existing model and quantifying the spatial conduction mechanism of attribute mutual exclusion.

[0027] S3. Based on the dynamic coupling network constructed in S2, an iterative compression operation mechanism is initiated for the dynamic connection weights of the conflicting nodes. This mechanism uses the stable topology formed by non-conflicting nodes as a benchmark and adjusts the dynamic connection weights between the conflicting nodes and adjacent non-conflicting nodes through multiple cycles, where:

[0028] In each iteration, based on the distribution dispersion (variance) test results of the global network weight, a directional compression adjustment is applied to the dynamic connection weights of the conflicting nodes (such as attenuation of high weight values ​​and enhancement of low weight values, where the judgment of high and low weights is determined by a preset weight threshold), so that the overall fluctuation range of all dynamic connection weights continues to decrease;

[0029] When the variance index of the global network weight falls into the preset stable fluctuation range in multiple consecutive iterations (indicating that the network connection status no longer changes significantly), the convergence condition is determined to be met. The final weight value output at this time is called the convergence weight. As a quantitative sign of the completion of the coordination of conflicting node attributes, it will replace the original dynamic connection weight and lock the network structure.

[0030] Step S3 adjusts the weights of conflicting nodes based on non-conflicting nodes: based on the discreteness of the network weight distribution, high / low weight values ​​are compressed through multiple rounds of iterations to continuously reduce the global fluctuation amplitude; when the variance continuously falls into the preset stable interval, the convergence weight is output to eliminate the interference of local conflicts on the network structure, thereby solving the boundary geometric jump caused by local conflicts in the gradient mutation area.

[0031] S4. Input the convergence weight output from S3 into the spatial topology engine. The engine constructs a dynamic deformation field based on the topological relationship index of the spatial unit. First, the convergence weight is mapped to the spatial unit grid to form a weight space. By detecting the spatial gradient distribution of the weight value (for example, high-weight areas point to key coordination paths), the grid nodes are driven to generate displacement deformation along the gradient direction (for example, conflicting nodes move closer to high-cooperation areas).

[0032] During the deformation process, the topological adjacency relationship between units is maintained (seamless coverage characteristics), and non-conflicting nodes are used as deformation anchor points to constrain excessive offset; finally, the geometric boundaries of the deformed spatial units are reconstructed to generate continuous and closed regional boundary lines. In terms of geometric accuracy, this boundary line inherits the division rules of the original spatial units (such as 100m accuracy at the town level), and in terms of spatial logic, it integrates the multi-source coordination relationship of geographic spatial attributes, socio-economic attributes, and ecologically sensitive attributes to form functional zoning boundaries that conform to the constraints of national land space planning.

[0033] Step S4 drives spatial topological deformation with convergence weights: grid nodes are displaced based on the weighted spatial gradient distribution (e.g., conflicting nodes move closer to high-coordination areas), while maintaining adjacency through topological indexes and anchoring the deformation range with non-conflicting nodes; outputs a continuous closed boundary line that matches the original grid with geometric accuracy and spatial logic that integrates natural, social, and ecological attributes, ensuring that functional zoning is compatible with resource distribution patterns, thereby repairing the problem of resource continuity caused by administrative boundaries and achieving multi-attribute logical fusion.

[0034] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely preferred examples of the present invention and are not intended to limit the present invention. Various changes and improvements may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and improvements fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for determining the boundaries of land space planning regions based on multidimensional data, characterized in that: The method steps are as follows: S1. Divide the target planning area into continuous spatial unit grids, where each spatial unit grid has a unique geographic coordinate range; map and align the geographic spatial attributes, socioeconomic attributes, and ecologically sensitive attributes according to the spatial unit grids; S2. Using each spatial unit as a network node, the mutually exclusive attribute values ​​within the same network node are tested for gradient deviation. If the gradient deviation exceeds the tolerance value, the node is marked as a conflicting node. The dynamic connection weights of the conflicting nodes are generated through the implicit association matrix between non-conflicting nodes. Based on this, a dynamic coupling network is constructed with network nodes as entities and dynamic connection weights as edge relationships. S3, performing an iterative compression operation on the dynamic connection weights of the conflicting nodes in the dynamic coupling network until the variances of all dynamic connection weights continuously decrease to a preset stable interval, and generating a converged weight; S4. Input the converged weights into the spatial topology engine, perform deformation according to the gradient distribution of the weight space, and output continuous closed region boundary lines.

2. The method for determining the boundaries of land space planning areas based on multidimensional data according to claim 1, characterized in that: The spatial unit grid is divided by a regular grid method to generate a continuous and seamlessly covered polygonal spatial unit grid, wherein the side length of the spatial unit grid can be dynamically adjusted according to the planning level.

3. The method for determining the boundaries of land space planning areas based on multidimensional data according to claim 1, characterized in that: The unique geographic coordinate range is defined by the latitude and longitude coordinates and side length of the lower left corner of the spatial unit grid, and the positional relationship between adjacent spatial unit grids is recorded through a topological relationship index to ensure spatial continuity.

4. The method for determining the boundaries of land space planning areas based on multidimensional data according to claim 1, characterized in that: The data mapping process specifically includes: Clip raster data according to the spatial unit coordinate range, calculate the average value within the unit, and generate geospatial attributes to characterize the quantitative indicators of the spatial distribution and morphology of natural geographical entities; Overlay analysis based on point-wise statistical data is performed according to the spatial positional relationship between the spatial unit grid and the predetermined statistical units. If the spatial unit grid contains only a single statistical unit, the socioeconomic attributes of the statistical unit are fully inherited; if the spatial unit grid covers multiple statistical units, the socioeconomic attributes are generated through weighted aggregation. The statistical unit is a closed geographical area that carries official statistical data and has legally fixed boundaries. Based on the vector protected area boundary, normalized values ​​are assigned according to the proportion of ecological elements in the spatial unit grid to generate ecologically sensitive attributes.

5. The method for determining the boundaries of land space planning areas based on multidimensional data according to claim 1, characterized in that: The data alignment process specifically includes: When converting non-spatial data to spatial unit grids, the principal value merging method is used to give priority to inheriting the statistical unit attributes with the highest proportion; When converting spatial continuous variables, the gradient-preserving interpolation method is used to avoid value jumps at unit boundaries.

6. The method for determining the boundaries of land space planning areas based on multidimensional data according to claim 1, characterized in that: The gradient direction deviation detection includes calculating the spatial change direction of different attribute values ​​within the same network node, and marking the node as a conflicting node when the gradient direction deviation exceeds a preset tolerance range.

7. The method for determining the boundaries of land space planning areas based on multidimensional data according to claim 1, characterized in that: The implicit association matrix is ​​constructed by extracting the spatial adjacency relationship and attribute similarity between non-conflicting nodes based on the topological relationship index, wherein the generation of dynamic connection weights follows the spatial proximity conduction rule and the attribute synergy rule, and is jointly determined by the degree of attribute difference between the conflicting nodes and the non-conflicting nodes and the spatial distance attenuation factor.

8. The method for determining the boundaries of land space planning areas based on multidimensional data according to claim 1, characterized in that: The iterative compression operation performs directional compression adjustment based on the distribution discreteness of the global network weight, so that the overall fluctuation amplitude of the dynamic connection weight continues to decrease, and reaches the convergence condition when the distribution discreteness falls into the preset stable fluctuation range in multiple consecutive rounds of iterations, and outputs the converged weight.

9. The method for determining the boundaries of land space planning areas based on multidimensional data according to claim 1, characterized in that: The deformation process specifically includes: A dynamic deformation field is constructed based on the topological relationship index, and the convergence weight is mapped to the spatial unit grid to form a weight space. The displacement deformation of the grid nodes is driven by detecting the spatial gradient distribution of the weight value, and the topological adjacency relationship between the units is maintained during the deformation process. At the same time, non-conflicting nodes are used as deformation anchor points to constrain excessive offset.

10. The method for determining the boundaries of land space planning areas based on multidimensional data according to claim 1, characterized in that: The boundary line of the continuous closed area inherits the division rules of the spatial unit grid in terms of geometric accuracy, and integrates the multi-source coordination relationship of geographic space attributes, socio-economic attributes and ecological sensitive attributes in terms of spatial logic.

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