Method for determining regional boundaries of territorial space planning based on multidimensional data

Abstract

The present application relates to the technical field of data analysis, in particular to a method for determining the regional boundary of territorial space planning based on multidimensional data, in which the geographical space, social economy and ecological sensitive attributes are uniformly mapped through the rule space unit grid to eliminate the scale misplacement and boundary jump problems of multi-source data; the attribute mutual exclusion conflict nodes are accurately positioned based on the gradient direction deviation detection, and the dynamic coupling network is constructed by using the non-conflict nodes to quantify the spatial transmission relationship; the dynamic connection weight is globally converged through the iterative compression operation to break through the limitation of the static model and inhibit the local conflict interference; finally, the closed boundary line is output under the premise of maintaining the spatial continuity by relying on the topological deformation driven by the weight spatial gradient, the problems of resource continuity fragmentation and attribute logic fracture are solved, and the multi-source coordinated adaptation of the planning boundary and the natural-economy-ecological gradient is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data analysis, in particular to a method for determining the regional boundary of land space planning based on multi-dimensional data. BACKGROUND

[0002] The current regional boundary demarcation of land space planning faces the dual challenges of multi-dimensional data fusion bottleneck and technical means limitation. At the data level, there are problems of scale mismatch, statutory unit conflict and natural unit continuity between geographical space grid data (such as terrain, vegetation coverage), social and economic point statistical data (such as population density, GDP distribution) and ecological sensitive vector data (such as protected area boundary): artificial superposition analysis is difficult to quantify the mutual exclusion gradient relationship between ecological protection area and urban development boundary, resulting in the logical fracture of attribute in land space demarcation.

[0003] At the technical level, existing spatial network models mostly rely on static weights or single attribute transmission mechanism, which cannot dynamically coordinate the conflict transmission of multi-dimensional attributes in spatial units (such as the tension relationship between ecological protection nodes and economic development nodes), resulting in geometric jumps or administrative boundary fragmentation of resource continuity distribution in the gradient mutation area of the generated boundary line. With the construction of land space planning implementation supervision system, it is urgent to solve the problem of multi-source dynamic coordination of boundary demarcation to support the modernization transformation demand of spatial governance. SUMMARY

[0004] The purpose of the present application is to provide a method for determining the regional boundary of land space planning based on multi-dimensional data to solve the problems raised in the background art, wherein the core problems to be solved include how to accurately identify the spatial mutual exclusion area of multi-dimensional attributes to solve the conflict contradiction of natural geographical distribution, social and economic activities and ecological protection demand in micro units; how to generate a continuous boundary line based on dynamic network transmission to solve the spatial adaptation error of planning partition line and geographical gradient change, resource distribution rule.

[0005] To achieve the above purpose, the present application provides the following technical scheme: a method for determining the regional boundary of land space planning based on multi-dimensional data, the method steps comprising:

[0006] S1, a regular grid method is used to generate a continuous seamless polygon space grid, ensuring global coverage without omission and uniform geometry. The left lower corner latitude and longitude coordinates and the side length define a unique geographical coordinate range. The topological relationship index records the position relationship of adjacent grids, establishing a traceable spatial continuity reference. The average value of the unit is calculated by cutting the grid data to generate geographical spatial attributes, quantifying the distribution characteristics of natural geographical entities. For point-like statistical data, if the grid contains only a single legal statistical unit, the attribute is inherited completely, if it covers multiple units, the social and economic attributes are generated by weighted aggregation, preserving the integrity of the legal statistical unit while eliminating the fragmentation effect. Based on the vector protected area boundary, the ecological sensitive attribute is generated by normalizing the proportion of ecological elements, accurately representing the spatial overlap intensity of protected areas and development areas;

[0007] The geographical spatial attributes, social and economic attributes, and ecological sensitive attributes are aligned by spatial unit grid. Non-spatial data uses the main value merging method to preferentially inherit the highest proportion of attributes, avoiding attribute fragmentation. Spatial continuous variables are converted by gradient preservation interpolation method to eliminate the numerical jump of unit boundary.

[0008] S2, calculate the gradient direction deviation of multiple attributes in the same node. If it exceeds the preset tolerance range, mark the conflict node and accurately locate the mutually exclusive area of geographical space, social economy and ecological sensitive attributes. Extract the spatial adjacency relationship and attribute similarity of non-conflict nodes through topological relationship index to construct an implicit correlation matrix and establish a spatial transmission infrastructure. Based on the spatial proximity transmission rule and attribute coordination rule, combined with the attribute difference degree and spatial distance attenuation factor, generate dynamic connection weight to quantify the spatial attenuation correlation between conflict nodes and non-conflict nodes. Form a dynamic coupling network with network nodes as entities and dynamic connection weight as edges, realize the spatialization and dynamic transmission of multi-dimensional conflict.

[0009] S3, according to the discrete degree of global weight distribution of the network, the dynamic connection weight of the conflict node is adjusted by directional compression, gradually suppressing the fluctuation interference of local conflict. Continuously reduce the overall fluctuation amplitude of the weight until the distribution discrete degree falls into the preset stable fluctuation interval in continuous multiple iterations, output the convergent weight, and generate a spatially coordinated and globally stable weight distribution.

[0010] S4, based on the topological relationship index, map the convergent weight to the weight space formed by the spatial unit grid to establish the geometric correspondence between the weight and the space. Detect the weight value spatial gradient distribution to drive the grid node displacement and deformation, so that the boundary line naturally adapts to the geographical gradient change. Maintain the topological adjacency relationship between units to ensure the geometric continuity of the boundary. Use non-conflict nodes as anchor points to constrain excessive displacement and protect the spatial stability of key functional areas. Output the continuous closed area boundary line, which inherits the grid division rule in geometric precision and integrates the multi-source attribute coordination relationship in spatial logic, simultaneously realizing the accuracy of boundary form and the consistency of multi-attribute logic.

[0011] Compared with the prior art, the present application has the beneficial effects that:

[0012] By establishing a global continuous spatial reference through a regular grid, accurate mapping and smooth alignment of geographic space, social economy and ecological sensitive attributes are realized, and boundary jumps and attribute fragmentation are eliminated in a unified framework; based on gradient deviation detection and dynamic coupling network, attribute repulsion areas are accurately located and the spatial decay law of conflicts is quantified, and a globally coordinated weight distribution is generated through iterative compression optimization; finally, relying on the topological deformation driven by the weight gradient, the boundary line with accurate geometric shape and logically fused multi-source attributes is output under the premise of maintaining spatial continuity and stability of key functional areas, improving the scientificity, coordination and implementability of the land space planning boundary. BRIEF DESCRIPTION OF DRAWINGS

[0013] Figure 1 The present application is a method step schematic diagram. DETAILED DESCRIPTION

[0014] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0015] Please refer to Figure 1 The present application provides a technical solution: a method for determining the boundary of a land space planning area based on multi-dimensional data, comprising the following method steps:

[0016] S1, according to the accuracy requirement of land space planning, a regular grid method is used to divide the target planning area into continuous and seamless polygon (such as quadrilateral or hexagon) spatial unit grids (the side length is dynamically adjustable according to the planning level, such as 100m x 100m for urban level and 1km x 1km for ecological protection area); wherein each spatial unit is given a unique geographic coordinate range (such as the lower left corner longitude and latitude + side length definition), and the position of adjacent units is recorded through topological relationship index, ensuring spatial continuity; wherein the regular grid method is specifically an operation method for dividing the target planning area into continuous and seamless polygon spatial unit grids, which takes a geometric figure (such as a quadrilateral or a hexagon) with a preset side length as a basic unit, defines the geographic coordinate range of each unit by calculating the left lower corner longitude and latitude coordinates and a fixed side length, and establishes a topological relationship index to record the adjacent position relationship between units, ensuring seamless continuity of the grid in space, and the grid side length can be dynamically adjusted according to different planning levels to meet the accuracy requirement.

[0017] The grid data is clipped according to the spatial unit coordinate range, the average value in the unit is calculated, and the geographic spatial attribute (such as elevation, slope) is generated to represent the quantitative index of the spatial distribution and form of the natural geographic entity.

[0018] Based on the overlay analysis of point statistical data, the spatial unit grid and the spatial position relationship of the predetermined statistical unit are executed, wherein if only a single statistical unit is included in the spatial unit grid, the social and economic attributes (such as population density, GDP) of the statistical unit are completely inherited; if multiple statistical units are covered in the spatial unit grid, the social and economic attributes are generated by weighted aggregation, wherein the statistical unit is a closed geographical partition with legally fixed boundary, such as a population census area and an economic census area; based on the vector protected area boundary, the ecological sensitive attribute (such as species distribution, vegetation index) is generated by normalized assignment according to the proportion of ecological elements in the spatial unit; the process maps the geographic spatial attribute, the social and economic attribute and the ecological sensitive attribute according to the spatial unit grid;

[0019] For non-spatial data (such as economic statistical values in social and economic attributes), the main value merging method (the attribute of the statistical unit with a proportion of more than 60% in the unit is preferentially inherited) is used when converting to the spatial unit; for spatial continuous variables (such as slope in geographic spatial attribute), the gradient preserving interpolation method is used to avoid numerical jump at the unit boundary; the process aligns the geographic spatial attribute, the social and economic attribute and the ecological sensitive attribute according to the spatial unit grid; wherein:

[0020] The main value merging method is a conversion method for non-spatial data in the data alignment process. When the spatial unit grid is overlaid with the predetermined statistical unit (a closed geographical partition with legally fixed boundary), the statistical unit is completely or partially covered by the spatial unit, and the spatial proportion of each statistical unit in the spatial unit is calculated; if there is a single statistical unit whose coverage area proportion exceeds the preset main value threshold (such as 60%), the social and economic 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] The gradient preserving interpolation method is a processing method for spatial continuous variables (such as slope, elevation) in the data alignment process: based on the spatial unit grid boundary coordinates, the spatial gradient function (such as linear or spline interpolation model) of the attribute value at the boundary of adjacent units is established, the gradient continuity constraint of the attribute of the units on both sides of the boundary is introduced, the numerical value of the unit boundary after interpolation is kept smooth transition, and the spatial numerical jump phenomenon caused by unit division is avoided.

[0022] Step S1 dynamically adjusts the size of the regular grid according to the planning accuracy, defines the unique geographical range of the unit through the lower left corner coordinate + side length, and ensures spatial continuity through topological indexing; the grid clipping average method is used to generate geographical spatial attributes, the main value merging or weighted aggregation processing is used for social and economic attributes, the ecological element proportion normalization is used to generate ecological sensitive attributes, and the main value merging method and the gradient reservation interpolation method are used to eliminate non-spatial data fragmentation and spatial continuous variable boundary jump, thereby breaking through the scale mismatching contradiction between geographical spatial grid, social and economic point statistics and ecological sensitive vector data.

[0023] S2, the spatial unit generated in S1 is taken as a network node, and mutual exclusion analysis is performed on the superimposed multi-source attributes (geographical spatial attributes, social and economic attributes and ecological sensitive attributes) in each node; the consistency of the gradient direction is detected by calculating the spatial variation direction of different attribute values in the same spatial unit (for example, the contradiction in the spatial distribution trend between the high value area of ecological sensitivity and the high value area of development intensity economic attribute), so as to perform gradient direction deviation detection; if the pointing deviation of different attribute gradients in 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 (recording the position of adjacent units) of the spatial unit, the spatial adjacency relationship and attribute similarity between all non-conflict nodes are extracted, and an implicit correlation matrix is constructed; the implicit correlation matrix generates a dynamic connection weight of each conflict node pointing to adjacent non-conflict nodes through the spatial proximity transmission rule (distance decay effect) and the attribute cooperativity rule (attribute type correlation), wherein the weight value is determined by the attribute difference degree of the conflict node and the non-conflict node, the spatial distance decay factor, and reflects the potential possibility of the conflict node coordinating attributes through the surrounding stable area; that is, based on the topological relationship index, the spatial distance between the conflict node and each non-conflict node is extracted, and the spatial proximity weight component is calculated by using the distance decay function (such as the inverse distance weight model); the similarity of the conflict node and the non-conflict node in the same attribute dimension (such as economic attribute or ecological attribute) is calculated, and the attribute cooperativity weight component is generated by normalizing the attribute difference degree; the spatial proximity weight component and the attribute cooperativity weight component are weighted and fused to form the final dynamic connection weight, so that the weight value reflects the spatial distance decay effect and the attribute difference coordination demand at the same time;

[0025] The spatial unit grid is taken as the network node entity, and the generated dynamic connection weight is taken as the edge relationship strength between the nodes, to form a globally covered dynamic coupling network, wherein the non-conflict nodes constitute the stable topological skeleton of the network, and the conflict nodes are elastically associated with the stable area through the dynamic weight, to form a spatial relationship network that realizes self-adaptive adjustment according to the weight change.

[0026] Step S2 detects the conflict nodes by gradient direction deviation; extracts the adjacency relationship of non-conflict nodes and attribute similarity based on the topology index, generates dynamic connection weight combined with spatial distance attenuation factor and attribute coordination rule; constructs a dynamic coupling network with non-conflict nodes as stable skeleton and conflict nodes in elastic association, realizes spatial attenuation conduction of attribute conflict, so as to overcome the limitation of static weight of existing model and quantify the spatial conduction mechanism of attribute mutual exclusion.

[0027] S3, on the basis of the dynamic coupling network constructed in S2, the iteration compression operation mechanism is started for the dynamic connection weight of the conflict nodes, which takes the stable topology formed by the non-conflict nodes as the reference, adjusts the dynamic connection weight between the conflict nodes and the adjacent non-conflict nodes through multiple rounds of circulation, wherein:

[0028] In each iteration, according to the detection result of the distribution dispersion (variance) of the global weight of the network, directional compression adjustment is applied to the dynamic connection weight of the conflict nodes (such as high weight value attenuation and low weight value enhancement, wherein the determination of high and low weight is determined by a pre-set weight threshold), so that the overall fluctuation amplitude of all dynamic connection weights is continuously reduced.

[0029] When the variance index of the global weight of the network in continuous multiple iterations falls into the pre-set stable fluctuation interval (indicating that the network connection state no longer changes significantly), it is determined that the convergence condition is reached, and the output of the final weight value is called the convergence weight, which is used as the quantitative symbol of the completion of attribute coordination of the conflict nodes, and will replace the original dynamic connection weight and lock the network structure.

[0030] Step S3 adjusts the weight of the conflict nodes based on the non-conflict nodes: according to the distribution dispersion of the network weight, the high / low weight value is compressed through multiple iterations, and the global fluctuation amplitude is continuously reduced; when the variance continuously falls into the pre-set stable interval, the convergence weight is output, and the interference of local conflict on the network structure is eliminated, so as to solve the boundary geometric jump caused by local conflict in the gradient mutation area.

[0031] S4, the convergence weight output by S3 is input into the spatial topology engine, which constructs a dynamic deformation field based on the topology relationship index of the spatial unit. First, the convergence weight is mapped to the weight space of the spatial unit grid, and the spatial gradient distribution of the weight value (such as the high weight area pointing to the key coordination path) is detected to drive the grid nodes to produce displacement deformation along the gradient direction (for example, the conflict nodes move towards the high coordination area);

[0032] The topological adjacency relationship between the units is maintained during the deformation process (seamless coverage characteristic), and the non-conflict nodes are relied on as deformation anchors to constrain excessive deviation; finally, through the reconstruction of the geometric boundary of the spatial unit after deformation, a continuous and closed regional boundary line is generated, which inherits the division rules of the original spatial unit in geometric accuracy (such as 100m accuracy in urban areas), and integrates the multi-source coordinated relationship of geographic space attributes, social and economic attributes and ecological sensitive attributes in spatial logic, forming a functional zoning boundary that meets the constraints of national spatial planning.

[0033] Step S4 drives the spatial topology deformation with convergence weights: relying on the weight spatial gradient distribution displacement grid nodes (such as the conflict nodes moving towards the high coordination area), while maintaining the adjacency relationship through the topology index and anchoring the deformation range with the non-conflict nodes; output the continuous and closed boundary line with geometric accuracy matching the original grid and spatial logic integrating natural-social-ecological attributes, ensuring the adaptation of functional zoning and resource distribution rules, thereby repairing the problem of administrative boundary fragmentation of resource continuity and realizing the multi-attribute logical fusion.

[0034] The basic principles, main features and advantages of the present application are shown and described above. Those skilled in the art should understand that the present application is not limited by the above examples, and the above examples and descriptions in the specification are only preferred examples of the present application and are not intended to limit the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for determining the boundaries of a land spatial planning area based on multidimensional data, characterized in that, The method and steps are as follows: S1. Divide the target planning area into a continuous spatial unit grid, where each spatial unit grid has a unique geographic coordinate range; map and align the geographic spatial attributes, socio-economic attributes, and ecological sensitivity attributes according to the spatial unit grid. S2. Using each spatial unit as a network node, perform gradient direction deviation detection on the mutually exclusive attribute values ​​within the same network node. If the gradient direction deviation exceeds the tolerance value, it is marked as a conflict node. Through the implicit association matrix between non-conflicting nodes, generate the dynamic connection weights of the conflict nodes, and use this to construct a dynamically coupled network with network nodes as entities and dynamic connection weights as edge relationships. 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. The generation of dynamic connection weights follows the spatial proximity transmission rule and attribute synergy rule, and is jointly determined by the degree of attribute difference between conflicting and non-conflicting nodes and the spatial distance attenuation factor. S3. Perform iterative compression operation on the dynamic connection weights of conflicting nodes in the dynamic coupling network until the variance of all dynamic connection weights continuously decreases to the preset stable interval, and generate convergent weights. S4. Input the convergence weights into the spatial topology engine, perform deformation according to the gradient distribution of the weight space, and output a continuous closed region boundary line. The deformation process specifically includes: A dynamic deformation field is constructed based on topological relationship index. The convergence weights are mapped to spatial cell grids to form a weight space. The displacement deformation of grid nodes is driven by detecting the spatial gradient distribution of weight values. The topological adjacency relationship between cells is maintained during the deformation process. At the same time, non-conflict nodes are used as deformation anchor points to constrain excessive offset.

2. The method for determining the boundary of a land spatial planning area based on multidimensional data according to claim 1, characterized in that, The spatial unit grid is divided using a regular grid method to generate a continuous and seamless 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 boundary of a land spatial planning area based on multidimensional data according to claim 1, characterized in that, The unique geographic coordinate range is defined by the latitude and longitude coordinates of the lower left corner of the spatial unit grid and the side length, 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 boundary of a land spatial planning area based on multidimensional data according to claim 1, characterized in that, The data mapping process specifically includes: Raster data is clipped according to the coordinate range of spatial units, the average value within the unit is calculated, and geospatial attributes are generated to form quantitative indicators that characterize the spatial distribution and morphology of natural geographic entities. The overlay analysis based on point statistical data is performed according to the spatial positional relationship between the spatial unit grid and the predetermined statistical unit. If the spatial unit grid contains only a single statistical unit, the socio-economic attributes of that statistical unit are fully inherited. If the spatial unit grid covers multiple statistical units, the socio-economic attributes are generated through weighted aggregation. The statistical unit is a closed geographical partition that carries official statistical data and has legally fixed boundaries. Based on the vector protected area boundary, ecological sensitivity attributes are generated by normalizing the values ​​of ecological elements within the spatial unit grid according to their proportions.

5. The method for determining the boundary of a land spatial planning area based on multidimensional data according to claim 1, characterized in that, The data alignment process specifically includes: When converting non-spatial data to spatial cell grids, the principal value merging method is used to prioritize inheriting the statistical cell attributes with the highest proportion. When transforming spatially continuous variables, gradient-preserving interpolation is used to avoid numerical jumps at cell boundaries.

6. The method for determining the boundary of a land spatial planning area 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 it as a conflict node when the gradient pointing deviation exceeds a preset tolerance range.

7. The method for determining the boundary of a land spatial planning area based on multidimensional data according to claim 1, characterized in that, The iterative compression operation performs directional compression adjustment based on the distribution dispersion of the network's global weights, continuously reducing the overall fluctuation amplitude of the dynamic connection weights. When the distribution dispersion falls into a preset stable fluctuation range in multiple consecutive iterations, the convergence condition is met, and the converged weights are output.

8. The method for determining the boundary of a land spatial planning area based on multidimensional data according to claim 1, characterized in that, The boundary line of the continuous closed region inherits the division rules of the spatial unit grid in terms of geometric precision, and integrates the multi-source coordination relationship of geographic spatial attributes, socio-economic attributes and ecological sensitivity attributes in terms of spatial logic.

Images