A rural landscape map data production method and system based on scale adaptation

Abstract

The application discloses a kind of based on scale self-adapting rural landscape map data production method and system, and the application relates to map data processing technical field, comprising the following steps: obtaining the multidimensional original data of rural area to be divided, using high-definition remote sensing image to divide basic analysis cell, and analyzing its basic parameter set;Local heterogeneity index is calculated, and the cell that meets homogeneous condition is selected as growth base point randomly, and iteration aggregation forms functional bearing unit;Set ontology range, influence radiation zone and macroscopic attribution area three space observation levels, set theoretical weight in combination with data confidence, determine the function performance value of each function attribute;The discrete degree of function performance value is analyzed, and the core function is judged;Draw the core function zoning spectrum map with functional bearing unit as mapping unit, and the rural landscape map data drawn by the application can scientifically divide and effectively manage rural landscape, meet the needs of ecological protection and resource utilization.

Description

Technical Field

[0001] This invention relates to the field of map data processing technology, specifically to a method and system for creating rural landscape map data based on scale adaptation. Background Technology

[0002] With the development of remote sensing and geographic information system technologies, multi-source spatial data such as high-resolution imagery and digital elevation models provide a rich data foundation for the functional identification of rural areas. Utilizing computer systems to automatically process and analyze this spatial data is the main technical approach to achieving precise delineation of rural functional zones at a fine scale.

[0003] Currently, most common computerized functional zoning methods are based on pixel classification or object-oriented segmentation techniques from remote sensing imagery, combined with simple spatial overlay of topographic factors and socioeconomic data. However, these methods generally suffer from the following technical problems in practical processing: First, there is insufficient ability to quantify the micro-heterogeneity within rural areas. Rural landscapes are characterized by fragmented patches and overlapping land types. Existing methods typically perform feature calculations based on preset fixed windows or simple pixel neighborhoods, making it difficult to adaptively extract and quantify the diversity of local topographic relief and land use types on high-resolution images. This results in the extracted initial analysis units (such as patches or pixel clusters) failing to accurately reflect the true geographical entity boundaries.

[0004] Second, the regional aggregation process lacks a dynamic and adaptive control mechanism. When merging initial units into larger functional zones, traditional methods often use globally uniform similarity thresholds or scale parameters. This ignores the dynamic nature of surface heterogeneity as it changes with regional area and local variance, easily leading to "over-aggregation" (forcibly merging plots with different functions) or "under-aggregation" (excessively dividing the same functional zone) in heterogeneous and complex areas, affecting the spatial consistency and accuracy of the zoning results.

[0005] Third, functional zoning under multi-scale spatial observation lacks a data confidence-adaptive fusion strategy. Existing methods often assign fixed weights or directly superimpose data at different scales when assessing functions in different spatial ranges (such as local, neighboring, and regional), without considering the differences in confidence caused by data accuracy and area effects at different observation scales. This makes the final core functional determination results susceptible to noise interference from large-scale, low-confidence data, reducing the reliability of the output small-scale functional zoning maps.

[0006] The aforementioned technical issues collectively result in significant deficiencies in the accuracy, robustness, and adaptability to complex environments of current computer-based functional zoning methods when processing high-resolution rural geographic data, thus limiting their technical support capabilities in areas such as ecological protection and refined land resource management. Summary of the Invention

[0007] The purpose of this invention is to provide a method and system for producing rural landscape map data based on scale adaptation. It aims to solve the technical bottlenecks of existing technologies in quantifying local heterogeneity, dynamically aggregating spatial units, and fusing multi-scale observation data through refined spatial data processing and adaptive analysis. This will improve the accuracy, robustness, and scene adaptability of functional zoning results and meet the actual needs of automated processing of high-resolution rural geographic data.

[0008] To achieve the above objectives, the present invention provides the following technical solution: A method for creating rural landscape map data based on scale adaptation, executed by a computer system, is used to create rural landscape map data containing core functional zoning maps. The specific steps include: Obtain multi-dimensional raw data of the rural areas to be divided; The computer system divides the rural area to be classified into several basic analysis cells based on the high-definition remote sensing images in the original data, and analyzes the characteristic parameters of each basic analysis cell to form a basic parameter set. Based on the aforementioned set of basic parameters, the local heterogeneity index of each basic analysis cell is calculated. Based on the local heterogeneity index, basic analysis cells that meet the homogeneity condition are randomly selected as growth base points, and adjacent basic analysis cells are iteratively aggregated until aggregation can no longer continue, so as to generate several functional carrying units. Three spatial observation levels are set: the scope of the main body, the radiation zone of influence, and the macroscopic belonging area. Based on the confidence level of the data used in each spatial observation level, the theoretical weight of each spatial observation level is set, and the functional effectiveness values ​​of each functional attribute of the functional carrying unit at different spatial observation levels are determined. For the same functional attribute of the same functional carrier unit, analyze the dispersion of its functional effectiveness value at different spatial observation levels, and determine the core function of the functional carrier unit based on the functional attribute with the largest functional effectiveness value. Based on the core function adjudication results of the functional carrying units, a core functional zoning map with the functional carrying units as mapping units is drawn, and rural landscape map data containing the core functional zoning map is output.

[0009] Furthermore, the multi-dimensional raw data specifically includes high-resolution remote sensing images, regional elevation values, remote sensing vegetation indices, and meteorological data. Based on the high-resolution remote sensing images, the functional attributes of each basic analysis cell are determined, and the functional attributes are cultivation, forest protection, animal husbandry, water resource supply, or construction use. The specific method for dividing the entire rural area to be divided into several basic analysis cells is as follows: based on the high-definition remote sensing image, determine the minimum bounding rectangle of the rural area to be divided, select the raster resolution, divide the minimum bounding rectangle into several raster, and take the rural area to be divided in each raster as a basic analysis cell. The characteristic parameters include the topographic heterogeneity coefficient and the land use heterogeneity coefficient.

[0010] Furthermore, for any basic analysis cell, the specific steps for analyzing its terrain heterogeneity coefficient are as follows: taking the basic analysis cell as a reference, calculate the distance between the center of the basic analysis cell and the centers of the remaining basic analysis cells, take the remaining basic analysis cells whose distance is not greater than a preset neighborhood distance threshold as its neighborhood cells, summarize the basic analysis cell and its neighborhood cells to form a neighborhood window based on the basic analysis cell, and calculate the elevation standard deviation of all basic analysis cells in the neighborhood window as the terrain heterogeneity coefficient of the basic analysis cell; The specific formula used to calculate the topographic heterogeneity coefficient of each basic analysis cell is as follows: In the formula, Let be the topographic heterogeneity coefficient of the i-th basic analysis cell. Let the elevation value be the j-th basic analysis cell within the neighborhood window formed based on the i-th basic analysis cell. This represents the average elevation of all basic analysis cells within a neighborhood window formed using the i-th basic analysis cell as the reference, where i is the index of the basic analysis cell and j is the index of the basic analysis cell within the neighborhood window. This represents the total number of basic analysis cells in the neighborhood window.

[0011] Furthermore, for any basic analysis cell, the specific steps for analyzing its land use heterogeneity coefficient are as follows: calculate the proportion of basic analysis cells belonging to various functional attributes within its neighborhood window; calculate the land use heterogeneity coefficient of the basic analysis cell using the Shannon diversity analysis method; and the specific formula used to calculate the land use heterogeneity coefficient of each basic analysis cell is as follows: In the formula, Let be the land use heterogeneity coefficient of the i-th basic analysis cell. This represents the proportion of basic analysis cells belonging to the k-th functional attribute within a neighborhood window formed based on the i-th basic analysis cell, where k is the index of the functional attribute type. This represents the total number of functional attributes.

[0012] Furthermore, the specific steps for calculating the local heterogeneity index of each basic analysis cell are as follows: The local heterogeneity index of each basic analysis cell is calculated comprehensively based on its characteristic parameters. The specific formula used to calculate the local heterogeneity index is as follows: In the formula, Let be the local heterogeneity index of the i-th basic analysis cell. and Let i be the normalized values ​​of the topographic heterogeneity coefficient and the land use heterogeneity coefficient of the i-th basic analysis cell. and Here are the weighting coefficients, where ; The normalized value of the topographic heterogeneity coefficient of the i-th basic analysis cell is obtained by normalizing the topographic heterogeneity coefficient of each basic analysis cell using the max-min normalization algorithm. The method for obtaining the normalized value of the land use heterogeneity coefficient is similar.

[0013] Further, several functional carrying units are generated through an adaptive aggregation growth algorithm. Specific steps include: marking all basic analysis cells in the entire rural area to be divided as unassigned; randomly selecting a basic analysis cell satisfying homogeneity as a growth base point from among the unassigned basic analysis cells, whereby the homogeneity condition specifically means that the local heterogeneity index of the basic analysis cell is not greater than a preset growth variation threshold; for any growth base point, constructing a set including it; using the growth base point as a reference, determining all unassigned basic analysis cells directly adjacent to it; constructing a detection region based on the growth base point; for any basic analysis cell within the detection region other than the growth base point, calculating the absolute deviation of its local heterogeneity index, using the following formula: In the formula, To measure the absolute bias of the local heterogeneity index of the u-th basic analysis cell within the detection region. The local heterogeneity index of the u-th basic analysis cell in the detection region. is the average local heterogeneity index of all basic analysis cells in the detection area, excluding the growth basal point; u is the index of other basic analysis cells in the detection area, excluding the growth basal point. Based on the area of ​​the detection region and the standard deviation of the local heterogeneity index, the dynamic growth threshold of the detection region is calculated. The specific formula used to calculate the dynamic growth threshold is as follows: In the formula, The dynamic growth threshold of the detection area. To adjust the parameters, The standard deviation of the local heterogeneity index of the detection area. The area of ​​the detection zone; If the absolute deviation of the local heterogeneity index of the u-th basic analysis cell within the detection area is not greater than the dynamic growth threshold of the detection area, then the u-th basic analysis cell is included in the set of growth base points. The basic analysis cells contained in the set are merged, and the merged area is used as a new growth base point. The operation of determining the detection area and making inclusion judgment is repeated until the set no longer includes new basic analysis cells. The basic analysis cell regions within the set are merged to generate a functional carrying unit. A new growth base point is re-determined among all unassigned basic analysis cells. The above process is repeated to obtain several functional carrying units.

[0014] Furthermore, the specific steps for setting the three spatial observation levels of the main body range, the influence radiation zone, and the macro-attachment area are as follows: the main body range is specifically the area range of the functional carrying unit itself, the influence radiation zone is the buffer zone formed by the functional carrying unit extending outward by a preset distance, and the macro-attachment area specifically refers to the complete administrative village range to which the functional carrying unit belongs. The specific steps for determining the functional effectiveness value of a functional carrier unit at different spatial observation levels are as follows: For any functional carrier unit, determine the number of basic analysis cells belonging to various functional attributes within the functional carrier unit at different spatial observation levels. Use this as the initial functional effectiveness value for each functional attribute of the functional carrier unit at different spatial observation levels. Based on the initial functional effectiveness value and combined with the theoretical weights of each spatial observation level, determine the functional effectiveness value of each functional attribute of the functional carrier unit at different spatial observation levels. The specific formula used to calculate the functional effectiveness value of each functional attribute of the functional carrier unit at different spatial observation levels is as follows: In the formula, For the f-th functional unit, the functional effectiveness value of the k-th functional attribute is given within the spatial observation level of the ontology. Let f be the functional effectiveness value of the k-th functional attribute of the f-th functional unit within the spatial observation level of the influence radiation belt. Let f be the functional effectiveness value of the k-th functional attribute within the macroscopic spatial observation level of the functional unit. For the f-th functional unit, the number of basic analysis cells of the k-th functional attribute within the ontology scope space. For the f-th functional unit, the number of cells with the k-th functional attribute in the influence radiation zone is analyzed. For the f-th functional unit, the number of the k-th functional attribute basic analysis cell within the macroscopic affiliation region. , and Here, f represents the theoretical weight, and f is the index of the functional unit. The confidence level of the data used at each space observation level is specifically characterized by the spatial area between different space observation levels, and the reciprocal of the spatial area ratio coefficient between different space observation levels is used as the theoretical weight.

[0015] Furthermore, for any functional carrying unit, the specific steps for analyzing the dispersion of its various functional attributes are as follows: Calculate the dispersion coefficient by considering the proportion of the maximum functional efficiency value and the number of non-zero functional efficiency values ​​within the same spatial observation level. The dispersion coefficient characterizes the dispersion of each functional attribute of the functional carrying unit. The specific formula used to calculate the dispersion coefficient is as follows: In the formula, Let f be the dispersion coefficient of the f-th functional unit. Let f be the number of functional attributes of the f-th functional unit with a non-zero functional effectiveness value at the r-th spatial observation level. , which is the standard deviation of the proportion of the maximum functional efficiency value of the f-th functional unit in each spatial observation level; The specific steps for determining the core function of each functional unit are as follows: determine whether the dispersion coefficient of each functional unit is greater than the preset dispersion threshold. If the value is greater than the preset discrete threshold, the functional carrying unit will be split up, and an adaptive aggregation growth algorithm will be used to redetermine the functional carrying unit based on the discreteness coefficient. If the value is less than the preset discrete threshold, the functional attribute with the highest functional performance value within the spatial observation level of the body range will be taken as the core function of the functional carrying unit.

[0016] Furthermore, the specific steps for re-determining the functional carrying units by splitting them into sub-units and using an adaptive aggregation and growth algorithm based on the degree of dispersion include: The functional carrying unit is divided into several independent basic analysis cells, and all independent basic analysis cells are marked as unassigned. Randomly select a basic analysis cell from the unassigned basic analysis cells as a growth base point. Based on the growth base point, determine all unassigned basic analysis cells that are directly adjacent to it, and construct a detection region based on the growth base point. For any basic analysis cell in the detection area other than the growth base point, calculate the absolute deviation of its dispersion coefficient. The absolute deviation of the dispersion coefficient is specifically expressed as the difference between the dispersion coefficient of the basic analysis cell and the average dispersion coefficient of all basic analysis cells in the detection area other than the growth base point. Based on the standard deviation of the dispersion coefficient of the detection area, the dispersion growth threshold is calculated; If the absolute deviation of the dispersion coefficient of the basic analysis cell within the detection area is not greater than the dispersion growth threshold, then the basic analysis cell is included in the set where the growth base point is located, merged into a new growth base point, and the above process is repeated until no new cells are added, so as to re-form the functional carrying unit. New growth base points are identified in all unassigned basic analysis cells, and the above process is repeated until several newly formed functional carrier units are obtained.

[0017] On the other hand, the present invention also provides a scale-adaptive rural landscape map data production system for performing the above-described scale-adaptive rural landscape map data production method, characterized in that it includes: The data acquisition module is used to acquire multi-dimensional raw data of the rural areas to be divided. The basic parameter set processing module is used to divide the rural area to be divided into several basic analysis cells based on the high-definition remote sensing images in the multi-dimensional raw data, and to parse the feature parameters of each basic analysis cell to form a basic parameter set. The heterogeneity calculation module is used to calculate the local heterogeneity index of each basic analysis cell based on the aforementioned set of basic parameters. The aggregation generation module is used to select basic analysis cells that meet the homogeneity conditions as growth base points based on the local heterogeneity index, and iteratively aggregate adjacent basic analysis cells until aggregation can no longer continue, so as to generate several functional carrying units. The multi-level functional analysis module is used to set three spatial observation levels: the body range, the influence radiation zone, and the macroscopic belonging area. It combines the confidence level of the data used in each spatial observation level to set the theoretical weight of each spatial observation level and determine the functional effectiveness value of each functional attribute of the functional carrying unit at different spatial observation levels. The core function adjudication module is used to analyze the dispersion of the functional effectiveness value of the same functional attribute of the same functional carrier unit at different spatial observation levels, and adjudicate the core function of the functional carrier unit based on the functional attribute with the largest functional effectiveness value. The mapping output module is used to draw a core functional zoning map with the functional carrying unit as the mapping unit based on the core functional adjudication result of the functional carrying unit, and output rural landscape map data containing the core functional zoning map.

[0018] Compared with the prior art, the beneficial effects of the present invention are: This scheme achieves refined functional zoning of rural areas through comprehensive analysis of multi-dimensional raw data. By dividing the entire rural area to be divided into basic analysis cells of uniform size, the scheme enables the feature parameters of each subdivided area to be fully analyzed, thereby forming a set of basic parameters. The introduction of the local heterogeneity index can effectively identify and quantify the differences in landscape features within each basic analysis cell, revealing the structural differences within the region. This provides a practical basis for the adaptive aggregation growth algorithm, improves the scientific nature of functional zoning, and enhances its dynamic adaptability, enabling rural functional zoning to respond in real time to the impact of environmental changes and human activities. By establishing three spatial observation levels—the scope of the entity, the influence radiation zone, and the macroscopic affiliation area—this scheme further enhances the accuracy and comprehensiveness of functional assessment. For each functional unit, by combining data confidence levels and theoretical weights within different levels, the scheme can accurately calculate functional effectiveness values ​​at different spatial observation levels. It fully considers the functional performance of rural areas at different spatial scales, making the determination of functional attributes more scientific and reliable. In particular, when analyzing the dispersion of functional effectiveness values ​​at different levels, the scheme effectively identifies and highlights core functions, ensuring that the functional division of each functional unit not only conforms to the actual situation but also has strong relevance and practicality, meeting the needs of ecological protection and resource utilization, and promoting the sustainable development of the rural economy. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the overall method flow of the present invention; Figure 2 The fitted curve is the topographic heterogeneity coefficient minus the land use heterogeneity coefficient. Figure 3 This is a schematic diagram of the distribution of the local heterogeneity index. Figure 4 This is a schematic diagram of the overall system structure of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0021] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0022] Example: Please see Figures 1-3 The present invention provides a technical solution: A method for creating rural landscape map data based on scale adaptation, executed by a computer system, is used to create rural landscape map data containing core functional zoning maps. The specific steps include: Step 1: Obtain multi-dimensional raw data of the rural areas to be divided.

[0023] The multi-dimensional raw data specifically includes high-resolution remote sensing images, regional elevation values, remote sensing vegetation indices, and meteorological data. Based on the high-resolution remote sensing images, the functional attributes of each basic analysis cell are determined. The functional attributes are cultivation, forest protection, animal husbandry, water resource supply, or construction use. High-resolution remote sensing images can be acquired through satellite remote sensing or drone aerial photography. For example, commercial satellites such as WorldView and Sentinel can be used, or drones can be used to take pictures of a region. The acquired remote sensing images are then processed with image enhancement, defogging, or orthorectification to improve image quality and ensure the accuracy of geographical location. Regional elevation values ​​can be obtained using digital elevation models (DEMs), which are typically acquired through lidar technology or stereo image analysis. The acquired elevation data is then interpolated and resampled to ensure consistency with the data resolution of the remote sensing image.

[0024] Remote sensing vegetation indices use spectral data from remote sensing images, such as red and near-infrared bands, to calculate vegetation indices. Commonly used indicators include the normalized difference vegetation index (NDI). Meteorological data can be obtained from weather stations, meteorological satellites, or meteorological data service platforms. It includes information such as temperature, precipitation, humidity, and wind speed. Meteorological data from different time periods are integrated into the same database and aligned with remote sensing images and other data in time and space.

[0025] Step 2: Based on the high-resolution remote sensing images in the original data, the rural area to be divided is divided into several basic analysis cells, and the characteristic parameters of each basic analysis cell are analyzed to form a basic parameter set.

[0026] The specific method for dividing the entire rural area to be divided into several basic analysis cells is as follows: Based on the high-resolution remote sensing image, determine the minimum bounding rectangle of the rural area to be divided, select the raster resolution, divide the minimum bounding rectangle into several raster cells, and take the rural area to be divided in each raster cell as a basic analysis cell; the specific method is as follows: acquire high-resolution remote sensing image, select suitable satellite or UAV image to ensure that the image covers the rural area to be divided, use GIS software such as ArcGIS or QGIS to load the high-resolution remote sensing image, perform image preprocessing such as denoising and enhancement to ensure image quality, and extract the boundary of the rural area to be divided based on the pixel values ​​in the image using image segmentation techniques (such as threshold segmentation and region growing method).

[0027] Based on the extracted boundaries, the minimum bounding rectangle is calculated. Geometric algorithms (such as the Convex Hull algorithm) can be used to generate the minimum rectangle that completely encloses the area to be subdivided. An appropriate raster resolution, such as 10 meters, 20 meters, or 30 meters, is selected according to research needs and image resolution. The raster resolution should match the characteristics and details of the area to be subdivided to ensure the accuracy of the analysis. In GIS software, based on the determined raster resolution, the minimum bounding rectangle is uniformly divided into multiple raster units, such as 10m×10m or 5m×5m. Each raster unit should be the same size, forming a regular raster grid. Each raster unit is checked one by one to determine whether it contains the rural area to be subdivided. If the raster contains part or all of the area to be subdivided, the raster is considered a basic analysis cell. A unique identifier is assigned to each basic analysis cell, and the functional attributes of the basic analysis cell are generated.

[0028] The characteristic parameters include topographic heterogeneity coefficient and land use heterogeneity coefficient (in a specific embodiment, the fitting curve of topographic heterogeneity coefficient-land use heterogeneity coefficient is as follows). Figure 2 (As shown).

[0029] For any basic analysis cell, the specific steps to analyze its topographic heterogeneity coefficient are as follows: taking the basic analysis cell as the reference, calculate the distance between the center of the basic analysis cell and the centers of the remaining basic analysis cells, take the remaining basic analysis cells whose distance is not greater than the preset neighborhood distance threshold as its neighborhood cells, summarize the basic analysis cell and its neighborhood cells to form a neighborhood window with the basic analysis cell as the reference, calculate the elevation standard deviation of all basic analysis cells in the neighborhood window, and use it as the topographic heterogeneity coefficient of the basic analysis cell; The specific formula used to calculate the topographic heterogeneity coefficient of each basic analysis cell is as follows: In the formula, Let be the topographic heterogeneity coefficient of the i-th basic analysis cell. Let the elevation value be the j-th basic analysis cell within the neighborhood window formed based on the i-th basic analysis cell. This represents the average elevation of all basic analysis cells within a neighborhood window formed using the i-th basic analysis cell as the reference, where i is the index of the basic analysis cell and j is the index of the basic analysis cell within the neighborhood window. This represents the total number of basic analysis cells in the neighborhood window.

[0030] It should be noted that the topographic heterogeneity coefficient of the i-th basic analysis cell... Reflects the first The complexity of the terrain surrounding a basic analysis cell is indicated by the value of the topography. The larger the value, the more significant the elevation difference and the greater the topographic relief around the area. This may mean that the terrain is diverse and rich in features, that is, the basic analysis cell and its neighborhood have large elevation variations and large topographic relief, and there may be more complex ecological environment and geomorphological features. The smaller the value of the topographic heterogeneity coefficient, the smaller the elevation variation around the area, which may be flat terrain. By calculating the distance between the center of the basic analysis cell and other cell centers, basic analysis cells whose distance from them is no greater than a preset neighborhood distance threshold are selected to form a neighborhood window. This setting can ensure the relevance of elevation calculation and avoid the influence of distant cells on terrain analysis. The degree of terrain undulation can be quantified by calculating the difference between the elevation of each basic analysis cell in the neighborhood window and the average elevation.

[0031] The specific steps for analyzing the land use heterogeneity coefficient of any basic analysis cell are as follows: Calculate the proportion of basic analysis cells belonging to various functional attributes within the neighborhood window based on that cell; calculate the land use heterogeneity coefficient of that basic analysis cell using the Shannon diversity analysis method; and use the formulas for calculating the land use heterogeneity coefficient of each basic analysis cell as follows: In the formula, Let be the land use heterogeneity coefficient of the i-th basic analysis cell. This represents the proportion of basic analysis cells belonging to the k-th functional attribute within a neighborhood window formed based on the i-th basic analysis cell, where k is the index of the functional attribute type. This represents the total number of functional attributes.

[0032] It should be noted that the land use heterogeneity coefficient of the i-th basic analysis cell Reflects the first A basic analysis of the diversity of land use types surrounding a cell. A higher value indicates a more diverse distribution of land use types in the surrounding area, meaning that multiple land use types exist within the basic analysis cell and its neighborhood, indicating that the land use in the region is relatively complex and may involve different ecological functions and resource utilization patterns. A smaller value indicates that the land use type in the area is more uniform, which is useful for subsequent regional merging analysis.

[0033] The proportion of basic analysis cells belonging to the k-th functional attribute within the neighborhood window formed based on the i-th basic analysis cell. This represents the proportion of a certain type of land use within the neighborhood window. The higher the proportion, the more important that type of land use is in the region, while the lower the proportion, the less important that type of land use is. This formula uses the Shannon diversity index to quantify land use diversity. The Shannon diversity index is a commonly used indicator in ecology, effectively reflecting the distribution of different land use types. By calculating the proportion of each land use type, it comprehensively considers the impact of different types on the overall land use diversity. A logarithmic function is used. This can enhance the sensitivity of the diversity index to differences in proportion, making the impact of categories with extremely small proportions on the diversity index more pronounced. In cluster analysis, the land use heterogeneity coefficient can serve as an important characteristic variable. This coefficient can be used to identify areas with similar land use patterns, distinguish different land use types and their spatial distribution patterns. By clustering and merging areas with high land use heterogeneity coefficients, a basis can be provided for land management and planning, helping to formulate more scientific land use strategies to promote sustainable development. Areas with high land use heterogeneity may require more ecological protection measures, while areas with low heterogeneity can be considered for more development and utilization to optimize resource allocation.

[0034] Step 3: Based on the aforementioned set of basic parameters, calculate the local heterogeneity index of each basic analysis cell.

[0035] The specific steps for calculating the local heterogeneity index of each basic analysis cell are as follows: The local heterogeneity index of each basic analysis cell is calculated comprehensively based on its characteristic parameters. The specific formula used to calculate the local heterogeneity index is as follows: In the formula, Let be the local heterogeneity index of the i-th basic analysis cell. and Let i be the normalized values ​​of the topographic heterogeneity coefficient and the land use heterogeneity coefficient of the i-th basic analysis cell. and Here are the weighting coefficients, where ; The normalized value of the topographic heterogeneity coefficient of the i-th basic analysis cell is obtained by normalizing the topographic heterogeneity coefficient of each basic analysis cell using the max-min normalization algorithm. The method for obtaining the normalized value of the land use heterogeneity coefficient is similar.

[0036] It should be noted that, as Figure 3 As shown, the local heterogeneity index of the i-th basic analysis cell. The local heterogeneity index reflects the overall heterogeneity level of the cell. A higher local heterogeneity index means that the topography and land use type of the basic analysis cell are more complex, and it is generally the boundary of regional division. A lower heterogeneity index may indicate the uniformity of regional functional attributes.

[0037] Normalization is used to eliminate the influence between different units of measurement, so that the values ​​of topographic heterogeneity coefficient and land use heterogeneity coefficient can be compared within the same range. The min-max normalization algorithm usually scales the values ​​to the range of [0,1], making the analysis results more comparable. Topographic features often have a profound impact on the hydrology, climate, soil type, and biodiversity of ecosystems. More complex topography can typically support more ecological niches and promote biodiversity. Therefore, topographic heterogeneity is considered a key factor in many ecological studies. Land use directly affects the ecological function and resource allocation of land. Different land use patterns can lead to differences in environmental quality. Although land use heterogeneity is also important, in many cases its impact on ecosystems may be lower than that of topographic heterogeneity. Therefore, setting... .

[0038] Step 4: Based on the local heterogeneity index, randomly select basic analysis cells that meet the homogeneity condition as growth base points, and iteratively aggregate adjacent basic analysis cells until aggregation can no longer continue, so as to generate several functional carrying units.

[0039] Several functional carrier units are generated through an adaptive aggregation growth algorithm. The specific steps include: marking all basic analysis cells in the entire rural area to be divided as unassigned; randomly selecting a basic analysis cell satisfying a homogeneity condition from among the unassigned cells as a growth base point, where the homogeneity condition specifically means that the local heterogeneity index of the basic analysis cell is not greater than a preset growth variation threshold; for any growth base point, constructing a set including it; using this growth base point as a reference, determining all unassigned basic analysis cells directly adjacent to it; constructing a detection region based on this growth base point, where the detection region specifically includes the growth base point and all its directly adjacent unassigned basic analysis cells; for any basic analysis cell within the detection region other than the growth base point, calculating the absolute deviation of its local heterogeneity index, using the following formula: In the formula, To measure the absolute bias of the local heterogeneity index of the u-th basic analysis cell within the detection region. The local heterogeneity index of the u-th basic analysis cell in the detection region. is the average local heterogeneity index of all basic analysis cells in the detection area, excluding the growth basal point; u is the index of other basic analysis cells in the detection area, excluding the growth basal point. Based on the area of ​​the detection region and the standard deviation of the local heterogeneity index, the dynamic growth threshold of the detection region is calculated. The specific formula used to calculate the dynamic growth threshold is as follows: In the formula, The dynamic growth threshold of the detection area. To adjust the parameters, The standard deviation of the local heterogeneity index of the detection area. The area of ​​the detection zone; It should be noted that the dynamic growth threshold Designed to be consistent with the standard deviation of the local heterogeneity index and area This allows the growth process to be adaptive, adjusting the growth threshold in a timely manner according to the actual situation of the detection area, ensuring that the polymerization process can flexibly cope with changes under different environmental conditions; The standard deviation of the local heterogeneity index reflects the degree of heterogeneity of the basic analytical cells within the detection region. A larger standard deviation indicates higher heterogeneity within the region, meaning that the basic analytical cells around the growth point exhibit more differences in local heterogeneity. Therefore, appropriately increasing the growth threshold is advisable. This can effectively prevent aggregation to unsuitable areas, thereby improving the quality of aggregation; Calculate absolute deviation The purpose is to assess the similarity to the growth base point through the local heterogeneity index, to ensure that the basic analysis cells of the aggregation have a certain degree of homogeneity in terms of function and ecological characteristics, and the dynamic growth threshold can flexibly adjust the acceptable range of deviation by taking into account the standard deviation, thereby ensuring the rationality of the aggregation. The area of ​​the detection region affects the scale and range of polymerization. The larger the area, the more ecological features and environmental conditions it may contain. Therefore, an appropriate dynamic growth threshold is needed to control the expansion of growth sites and ensure that the polymerization process remains effective and consistent over a larger area. The introduction of adjustment parameters is to adjust the dynamic growth threshold. It offers further adjustability; different aggregation targets or research objectives may require different degrees of growth restriction, which can be achieved by adjusting... This allows for strict control over the degree of growth, thereby enabling adaptation to different ecosystem conditions; adjusting parameters The value is usually set based on expert experience and typically ranges from 0.1 to 0.4.

[0040] Setting a dynamic growth threshold provides a balance between stability and flexibility in the growth process. A lower growth threshold helps ensure rapid polymerization but may lead to a decrease in polymerization quality; while a higher growth threshold can improve polymerization quality but may lead to an excessively slow polymerization process. By combining the standard deviation of local heterogeneity and the area of ​​the region, the designed dynamic growth threshold can maintain an appropriate growth rate and quality under different environmental conditions.

[0041] If the absolute deviation of the local heterogeneity index of the u-th basic analysis cell within the detection area is not greater than the dynamic growth threshold of the detection area, then the u-th basic analysis cell is included in the set of growth base points. The basic analysis cells contained in the set are merged, and the merged area is used as a new growth base point. The operation of determining the detection area and making inclusion judgment is repeated until the set no longer includes new basic analysis cells. The basic analysis cell regions within the set are merged to generate a functional carrying unit. A new growth base point is re-determined among all unassigned basic analysis cells. The above process is repeated to obtain several functional carrying units.

[0042] Step 5: Set three spatial observation levels: the body range, the influence radiation zone, and the macroscopic affiliation area. Based on the confidence level of the data used in each spatial observation level, set the theoretical weight of each spatial observation level and determine the functional effectiveness values ​​of each functional attribute of the functional carrying unit at different spatial observation levels.

[0043] The specific steps for setting the three spatial observation levels of the main body range, the influence radiation zone and the macro-attachment area are as follows: The main body range is specifically the area range of the functional carrier unit itself, the influence radiation zone is the buffer zone formed by the functional carrier unit extending outward by a preset distance, and the macro-attachment area specifically refers to the complete administrative village range to which the functional carrier unit belongs. In ecological and functional studies, integrity is a very important concept. Ontological scope provides a relatively closed system that facilitates the precise measurement and evaluation of internal functional attributes, thereby ensuring the accuracy and reliability of the research. The designation of influence radiation zones also takes into account the interaction between biological and social systems. For example, land use change and the flow of ecosystem services can affect the ecological environment of the surrounding area. Therefore, by delineating influence radiation zones, we can better understand the ecological and socio-economic impacts of functional carrying units on neighboring areas. At the macro level, the division of administrative regions can influence resource allocation and management decisions. By analyzing macro-level jurisdictions, we can understand the economic, social and environmental connections of functional units on a larger scale and provide support for the formulation of regional policies. The confidence levels of data used at different space observation levels may differ; therefore, when setting theoretical weights, adjustments need to be made based on the actual situation at each level. Data for the ontological scope are generally more reliable because they are directly based on field surveys or monitoring; while data affecting radiation belts and macroscopic attribution areas originate from indirect observations and require corresponding weight adjustments. The specific steps for determining the functional effectiveness value of a functional carrier unit at different spatial observation levels are as follows: For any functional carrier unit, determine the number of basic analysis cells belonging to various functional attributes within the functional carrier unit at different spatial observation levels. Use this as the initial functional effectiveness value for each functional attribute of the functional carrier unit at different spatial observation levels. Based on the initial functional effectiveness value and combined with the theoretical weights of each spatial observation level, determine the functional effectiveness value of each functional attribute of the functional carrier unit at different spatial observation levels. The specific formula used to calculate the functional effectiveness value of each functional attribute of the functional carrier unit at different spatial observation levels is as follows: In the formula, For the f-th functional unit, the functional effectiveness value of the k-th functional attribute is given within the spatial observation level of the ontology. Let f be the functional effectiveness value of the k-th functional attribute of the f-th functional unit within the spatial observation level of the influence radiation belt. Let f be the functional effectiveness value of the k-th functional attribute within the macroscopic spatial observation level of the functional unit. For the f-th functional unit, the number of basic analysis cells of the k-th functional attribute within the ontology scope space. For the f-th functional unit, the number of cells with the k-th functional attribute in the influence radiation zone is analyzed. For the f-th functional unit, the number of the k-th functional attribute basic analysis cell within the macroscopic affiliation region. , and Here, f represents the theoretical weight, and f is the index of the functional unit. It should be noted that the functional performance value represents the functional attribute performance of a certain functional unit within a specific spatial observation level; At each space observation level, the number of basic analysis cells is regarded as a preliminary indicator for evaluating the effectiveness of functional attributes. The more cells there are, the stronger the performance of the functional attribute at that level. Therefore, the number of basic analysis cells for each functional unit at different levels directly determines the initial functional effectiveness value. The confidence level of the data used at each space observation level is specifically characterized by the spatial area between different space observation levels, and the reciprocal of the spatial area ratio coefficient between different space observation levels is used as the theoretical weight. The confidence level of data may differ at different spatial observation levels. Therefore, it is necessary to adjust the influence of each level through theoretical weights. The weights are set by the spatial area ratio between each observation level. Since a larger spatial area may indicate a relatively lower data confidence level, while a smaller area may mean higher precision, the reciprocal method can more reasonably reflect the reliability of the data.

[0044] Step 6: For the same functional attribute of the same functional carrier unit, analyze the dispersion of its functional effectiveness value at different spatial observation levels, and determine the core function of the functional carrier unit based on the functional attribute with the largest functional effectiveness value.

[0045] For any functional unit, the specific steps for analyzing the dispersion of its various functional attributes are as follows: Calculate the dispersion coefficient by considering the proportion of the maximum functional efficiency value and the number of non-zero functional efficiency values ​​within the same spatial observation level. The dispersion coefficient characterizes the dispersion of each functional attribute of the functional unit. The specific formula used to calculate the dispersion coefficient is as follows: In the formula, Let f be the dispersion coefficient of the f-th functional unit. Let f be the number of functional attributes of the f-th functional unit with a non-zero functional effectiveness value at the r-th spatial observation level. , which is the standard deviation of the proportion of the maximum functional efficiency value of the f-th functional unit in each spatial observation level; Among them, determine The specific method for determining the parameters is as follows: Determine the functional effectiveness value of each functional attribute of any functional carrying unit in different spatial observation levels. For any spatial observation level, determine the maximum value among all functional effectiveness values ​​of its functional attributes. Calculate the proportion of the maximum value of this functional effectiveness value in the sum of all functional effectiveness values ​​in this spatial observation level to determine the proportion of the maximum functional effectiveness value in this spatial observation level. Calculate the proportion of the maximum functional effectiveness value in the three spatial observation levels based on this proportion. Based on this proportion, calculate the standard deviation of the proportion of the maximum functional effectiveness value in different spatial observation levels.

[0046] It should be noted that the dispersion coefficient of the f-th functional unit... Indicates the first The degree of dispersion of the functional attributes of a functional carrier unit across different spatial observation levels is used to assess the uniformity or degree of variation in the distribution of its functional attributes. The larger the dispersion coefficient, the more significant the difference in the functional effectiveness of the functional carrier unit across different levels; that is, there may be significant functional imbalances across different levels.

[0047] Coefficient of Dispersion The core function is to determine whether the functional attributes within a functional unit exhibit significant imbalance or dispersion. A high dispersion coefficient indicates that the functional attributes show large differences in performance across spatial observation levels, exhibiting complexity. Conversely, a low dispersion coefficient indicates that the functional attributes are relatively uniform across different levels, tending towards stability. Description of the The degree of variation in the proportion of the maximum functional effectiveness value of a functional unit across different spatial observation levels. A larger standard deviation indicates a more dispersed distribution of functional effectiveness values, reflecting differences in functional performance between different levels; Indicates the first Within a space observation level, the number of functional attributes with effective functional performance values ​​reflects the level of functional activity of a functional unit within that space observation level. The larger the value, the more effective functional attributes there are within the space observation level, that is, the greater the difference in functional performance.

[0048] The specific steps for determining the core function of each functional unit are as follows: determine whether the dispersion coefficient of each functional unit is greater than the preset dispersion threshold. If the value exceeds the preset discrete threshold, the functional unit will be split up, and an adaptive aggregation growth algorithm will be used to redetermine the functional unit based on the discreteness coefficient. If the value is less than a preset discrete threshold, the functional attribute with the highest functional effectiveness value within the spatial observation level of the entity scope will be taken as the core function of that functional unit. The preset discrete threshold can be set based on expert experience and actual partitioning requirements.

[0049] It should be noted that when the dispersion coefficient If the value exceeds the preset discrete threshold, it indicates that the functional attributes within the functional carrying unit are relatively complex, with significant imbalances or functional dispersion. In this case, simply selecting one functional attribute as the core function cannot accurately reflect the true functional characteristics of the carrying unit.

[0050] Therefore, based on the dispersion coefficient, an adaptive aggregation growth algorithm is adopted to redetermine the functional carrying units. This involves merging the basic analysis cells within the corresponding functional carrying units to form new functional carrying units. The dispersion coefficient of each newly formed functional carrying unit is less than the dispersion threshold, resulting in more unified functional attributes. Specific steps include: splitting the corresponding functional carrying unit into several independent basic analysis cells; marking all independent basic analysis cells as unassigned; randomly selecting one basic analysis cell from the unassigned cells as a growth base point; constructing a set including any growth base point; determining all directly adjacent unassigned basic analysis cells based on this growth base point; constructing a detection region based on this growth base point; and calculating the absolute deviation of the dispersion coefficient for any basic analysis cell within the detection region other than the growth base point. This is represented as the difference between the dispersion coefficient of the basic analysis cell in the detection area and the average dispersion coefficient of all basic analysis cells in the detection area, excluding the growth base point. Based on the standard deviation of the dispersion coefficient of the detection area, the above-mentioned method for calculating the dynamic growth threshold is used to calculate the dispersion growth threshold. If it is determined that the absolute deviation of the dispersion coefficient of the basic analysis cell in the detection area is not greater than the dispersion growth threshold of the detection area, then it is included in the set where the growth base point is located. The basic analysis cells contained in the set are merged, and the merged area is used as the new growth base point. The operation of determining the detection area and making inclusion judgment is repeated until the set no longer includes new basic analysis cells. The basic analysis cell areas in the set are merged to generate a functional carrying unit. New growth base points are re-determined among all unassigned basic analysis cells. The above process is repeated to obtain several newly formed functional carrying units.

[0051] The adaptive aggregation growth algorithm dynamically adjusts the division of functional carrying units by analyzing the spatial distribution and dispersion of functional attributes, and aggregates regions with similar functional performance attributes. This algorithm ensures that the newly divided functional carrying units are more consistent with actual functional performance and avoids excessive dispersion of functional attributes. The adaptive aggregation growth algorithm is consistent with the above method. Specifically, it calculates the absolute deviation by comparing the dispersion coefficient of the functional carrying unit with the average dispersion coefficient of the u-th basic analysis cell and the growth base point in the detection area. The corresponding dynamic growth threshold is constructed by adjusting the parameters, the standard deviation of the dispersion coefficient of the detection area, and the area of ​​the detection area. The subsequent merging steps are the same as above and will not be elaborated here.

[0052] When the dispersion coefficient If the value is less than the preset discrete threshold, it indicates that the performance of each functional attribute within the functional carrying unit is relatively balanced, and there is no significant imbalance or difference in the distribution of functional performance values. It is considered that the performance of the functional attributes within the functional carrying unit is relatively stable, and the functional attribute with the largest functional performance value within the spatial observation level of the ontology range is directly determined as the core function. By comparing the dispersion coefficients of various functional attributes, the functional attribute that exhibits the greatest stability and consistency across different spatial observation levels is identified. That is, the functional attribute with the highest functional effectiveness value will be determined as the core function of the functional carrying unit. The reason for prioritizing the largest functional attribute at the spatial observation level within the scope of the entity in this case is that the data at this level has a high confidence level and can directly reflect the internal functional performance of the functional carrying unit. The functional attribute with the largest functional effectiveness value usually represents the main functional characteristics of the carrying unit. Therefore, the functional attribute with the largest functional effectiveness value is determined to be the core function.

[0053] Step 7: Based on the core function adjudication results of the functional carrying unit, draw a core functional zoning map with the functional carrying unit as the mapping unit, and output rural landscape map data containing the core functional zoning map.

[0054] The specific method for drawing a core functional zoning spectrum with functional carrying units as mapping units is as follows: obtain the core functional decision results of each functional carrying unit, including the functional performance value, dispersion coefficient and core functions of each unit.

[0055] Collect spatial location information of functional units for subsequent map drawing. Organize functional units, core functions, and related attributes into tables or data frames, ensuring that each functional unit corresponds to its core function. Select suitable drawing tools or software. Commonly used drawing tools include: GIS software such as ArcGIS and QGIS, used for spatial analysis and visualization of geographic information systems; data visualization tools such as Tableau and Power BI, suitable for generating interactive charts; and programming languages ​​such as Python or R, suitable for custom plotting. Import the prepared functional unit data into QGIS, ensuring its spatial data format is correct, and overlay the functional unit data layer onto the base map. Based on the category of core functions, set different colors or symbols for different functional attributes, add legends to the map to explain the core functions represented by different colors or symbols, and add labels to each functional unit to display its core function name. After completing the drawing, export the core functional zoning map as an image format.

[0056] Please see Figure 4 The present invention also provides a scale-adaptive rural landscape map data production system for performing the above-described scale-adaptive rural landscape map data production method, characterized in that it includes: The data acquisition module is used to acquire multi-dimensional raw data of the rural areas to be divided. The basic parameter set processing module is used to divide the rural area to be divided into several basic analysis cells based on the high-definition remote sensing images in the multi-dimensional raw data, and to parse the feature parameters of each basic analysis cell to form a basic parameter set. The heterogeneity calculation module is used to calculate the local heterogeneity index of each basic analysis cell based on the aforementioned set of basic parameters. The aggregation generation module is used to select basic analysis cells that meet the homogeneity conditions as growth base points based on the local heterogeneity index, and iteratively aggregate adjacent basic analysis cells until aggregation can no longer continue, so as to generate several functional carrying units. The multi-level functional analysis module is used to set three spatial observation levels: the body range, the influence radiation zone, and the macroscopic belonging area. It combines the confidence level of the data used in each spatial observation level to set the theoretical weight of each spatial observation level and determine the functional effectiveness value of each functional attribute of the functional carrying unit at different spatial observation levels. The core function adjudication module is used to analyze the dispersion of the functional effectiveness value of the same functional attribute of the same functional carrier unit at different spatial observation levels, and adjudicate the core function of the functional carrier unit based on the functional attribute with the largest functional effectiveness value. The mapping output module is used to draw a core functional zoning map with the functional carrying unit as the mapping unit based on the core functional adjudication result of the functional carrying unit, and output rural landscape map data containing the core functional zoning map.

[0057] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0058] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0059] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0060] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for producing rural landscape map data based on scale adaptation, and used to produce rural landscape map data containing core functional zoning maps, characterized in that, The specific steps include: Obtain multi-dimensional raw data of the rural areas to be divided; Based on the high-resolution remote sensing images in the original data, the rural area to be divided is divided into several basic analysis cells, and the characteristic parameters of each basic analysis cell are analyzed to form a basic parameter set. Based on the aforementioned set of basic parameters, the local heterogeneity index of each basic analysis cell is calculated. Based on the local heterogeneity index, basic analysis cells that meet the homogeneity condition are randomly selected as growth base points, and adjacent basic analysis cells are iteratively aggregated until aggregation can no longer continue, so as to generate several functional carrying units. Three spatial observation levels are set: the scope of the main body, the radiation zone of influence, and the macroscopic belonging area. Based on the confidence level of the data used in each spatial observation level, the theoretical weight of each spatial observation level is set, and the functional effectiveness values ​​of each functional attribute of the functional carrying unit at different spatial observation levels are determined. For the same functional attribute of the same functional carrier unit, analyze the dispersion of its functional effectiveness value at different spatial observation levels, and determine the core function of the functional carrier unit based on the functional attribute with the largest functional effectiveness value. Based on the core function adjudication results of the functional carrying units, a core functional zoning map with the functional carrying units as mapping units is drawn, and rural landscape map data containing the core functional zoning map is output.

2. The method for producing rural landscape map data based on scale adaptation according to claim 1, characterized in that: The multi-dimensional raw data specifically includes high-resolution remote sensing images, regional elevation values, remote sensing vegetation indices, and meteorological data. Based on the high-resolution remote sensing images, the functional attributes of each basic analysis cell are determined. The functional attributes are cultivation, forest protection, animal husbandry, water resource supply, or construction use. The specific method for dividing the entire rural area to be divided into several basic analysis cells is as follows: based on the high-definition remote sensing image, determine the minimum bounding rectangle of the rural area to be divided, select the raster resolution, divide the minimum bounding rectangle into several raster, and take the rural area to be divided in each raster as a basic analysis cell. The characteristic parameters include the topographic heterogeneity coefficient and the land use heterogeneity coefficient.

3. The method for producing rural landscape map data based on scale adaptation according to claim 2, characterized in that: The specific steps for analyzing the topographic heterogeneity coefficient of any basic analysis cell are as follows: taking the basic analysis cell as the reference, calculate the distance between the center of the basic analysis cell and the centers of the remaining basic analysis cells, take the remaining basic analysis cells whose distance is not greater than the preset neighborhood distance threshold as its neighborhood cells, summarize the basic analysis cell and its neighborhood cells to form a neighborhood window with the basic analysis cell as the reference, and calculate the elevation standard deviation of all basic analysis cells in the neighborhood window as the topographic heterogeneity coefficient of the basic analysis cell. The specific formula used to calculate the topographic heterogeneity coefficient of each basic analysis cell is as follows: In the formula, Let be the topographic heterogeneity coefficient of the i-th basic analysis cell. Let the elevation value be the j-th basic analysis cell within the neighborhood window formed based on the i-th basic analysis cell. This represents the average elevation of all basic analysis cells within a neighborhood window formed using the i-th basic analysis cell as the reference, where i is the index of the basic analysis cell and j is the index of the basic analysis cell within the neighborhood window. This represents the total number of basic analysis cells in the neighborhood window.

4. The method for producing rural landscape map data based on scale adaptation according to claim 3, characterized in that: The specific steps for analyzing the land use heterogeneity coefficient of any basic analysis cell are as follows: Calculate the proportion of basic analysis cells belonging to various functional attributes within the neighborhood window based on that cell; calculate the land use heterogeneity coefficient of the basic analysis cell using the Shannon diversity analysis method; and the specific formula used to calculate the land use heterogeneity coefficient of each basic analysis cell is as follows: In the formula, Let be the land use heterogeneity coefficient of the i-th basic analysis cell. This represents the proportion of basic analysis cells belonging to the k-th functional attribute within a neighborhood window formed based on the i-th basic analysis cell, where k is the index of the functional attribute type. This represents the total number of functional attributes.

5. The method for producing rural landscape map data based on scale adaptation according to claim 4, characterized in that: The specific steps for calculating the local heterogeneity index of each basic analysis cell are as follows: The local heterogeneity index of each basic analysis cell is calculated comprehensively based on its characteristic parameters. The specific formula used to calculate the local heterogeneity index is as follows: In the formula, Let be the local heterogeneity index of the i-th basic analysis cell. and Let i be the normalized values ​​of the topographic heterogeneity coefficient and the land use heterogeneity coefficient of the i-th basic analysis cell. and Here are the weighting coefficients, where ; in The normalized value of the topographic heterogeneity coefficient of the i-th basic analysis cell is obtained by normalizing the topographic heterogeneity coefficient of each basic analysis cell using the min-max normalization algorithm. The method for obtaining the normalized value of the land use heterogeneity coefficient is similar.

6. The method for producing rural landscape map data based on scale adaptation according to claim 1, characterized in that: Several functional carrier units are generated through an adaptive aggregation growth algorithm. The specific steps include: marking all basic analysis cells in the entire rural area to be divided as unassigned; randomly selecting a basic analysis cell that meets the homogeneity condition from among the unassigned basic analysis cells as a growth base point, wherein the homogeneity condition is that the local heterogeneity index of the basic analysis cell is not greater than a preset growth variation threshold; for any growth base point, constructing a set including it; using the growth base point as a reference, determining all unassigned basic analysis cells directly adjacent to it; constructing a detection region based on the growth base point; for any basic analysis cell within the detection region other than the growth base point, calculating the absolute deviation of its local heterogeneity index, using the following formula: In the formula, To measure the absolute bias of the local heterogeneity index of the u-th basic analysis cell within the detection region. The local heterogeneity index of the u-th basic analysis cell in the detection region. is the average local heterogeneity index of all basic analysis cells in the detection area, excluding the growth basal point; u is the index of other basic analysis cells in the detection area, excluding the growth basal point. Based on the area of ​​the detection region and the standard deviation of the local heterogeneity index, the dynamic growth threshold of the detection region is calculated. The specific formula used to calculate the dynamic growth threshold is as follows: In the formula, The dynamic growth threshold of the detection area. To adjust the parameters, The standard deviation of the local heterogeneity index of the detection area. The area of ​​the detection zone; If the absolute deviation of the local heterogeneity index of the u-th basic analysis cell within the detection area is not greater than the dynamic growth threshold of the detection area, then the u-th basic analysis cell is included in the set of growth base points. The basic analysis cells contained in the set are merged, and the merged area is used as a new growth base point. The operation of determining the detection area and making inclusion judgment is repeated until the set no longer includes new basic analysis cells. The basic analysis cell regions within the set are merged to generate a functional carrying unit. A new growth base point is re-determined among all unassigned basic analysis cells. The above process is repeated to obtain several functional carrying units.

7. The method for producing rural landscape map data based on scale adaptation according to claim 1, characterized in that: The specific steps for setting the three spatial observation levels of the main body range, the influence radiation zone and the macro-attachment area are as follows: the main body range is the area range of the functional carrier unit itself, the influence radiation zone is the buffer zone formed by the functional carrier unit extending outward by a preset distance, and the macro-attachment area refers to the complete administrative village range to which the functional carrier unit belongs. The specific steps for determining the functional effectiveness value of a functional carrier unit at different spatial observation levels are as follows: For any functional carrier unit, determine the number of basic analysis cells belonging to various functional attributes within the functional carrier unit at different spatial observation levels. Use this as the initial functional effectiveness value for each functional attribute of the functional carrier unit at different spatial observation levels. Based on the initial functional effectiveness value and combined with the theoretical weights of each spatial observation level, determine the functional effectiveness value of each functional attribute of the functional carrier unit at different spatial observation levels. The specific formula used to calculate the functional effectiveness value of each functional attribute of the functional carrier unit at different spatial observation levels is as follows: In the formula, For the f-th functional unit, the functional effectiveness value of the k-th functional attribute is given within the spatial observation level of the ontology. Let f be the functional effectiveness value of the k-th functional attribute of the f-th functional unit within the spatial observation level of the influence radiation belt. Let f be the functional effectiveness value of the k-th functional attribute within the macroscopic spatial observation level of the functional unit. For the f-th functional unit, the number of basic analysis cells of the k-th functional attribute within the ontology scope space. For the f-th functional unit, the number of cells with the k-th functional attribute in the influence radiation zone is analyzed. For the f-th functional unit, the number of the k-th functional attribute basic analysis cell within the macroscopic affiliation region. , and Here, f represents the theoretical weight, and f is the index of the functional unit. The confidence level of the data used at each space observation level is specifically characterized by the spatial area between different space observation levels, and the reciprocal of the spatial area ratio coefficient between different space observation levels is used as the theoretical weight.

8. The method for producing rural landscape map data based on scale adaptation according to claim 1, characterized in that: For any functional unit, the specific steps for analyzing the dispersion of its various functional attributes are as follows: Calculate the dispersion coefficient by considering the proportion of the maximum functional efficiency value and the number of non-zero functional efficiency values ​​within the same spatial observation level. The dispersion coefficient characterizes the dispersion of each functional attribute of the functional unit. The specific formula used to calculate the dispersion coefficient is as follows: In the formula, Let f be the dispersion coefficient of the f-th functional unit. Let f be the number of functional attributes of the f-th functional unit with a non-zero functional effectiveness value at the r-th spatial observation level. , which is the standard deviation of the proportion of the maximum functional efficiency value of the f-th functional unit in each spatial observation level; The specific steps for determining the core function of each functional unit are as follows: determine whether the dispersion coefficient of each functional unit is greater than the preset dispersion threshold. If the value is greater than the preset discrete threshold, the functional carrying unit will be split up, and an adaptive aggregation growth algorithm will be used to redetermine the functional carrying unit based on the discreteness coefficient. If the value is less than the preset discrete threshold, the functional attribute with the highest functional performance value within the spatial observation level of the body range will be taken as the core function of the functional carrying unit.

9. A method for producing rural landscape map data based on scale adaptation according to claim 8, characterized in that: The specific steps for re-determining the functional carrying units by splitting them into smaller units and using an adaptive aggregation and growth algorithm based on the degree of dispersion include: The functional carrying unit is divided into several independent basic analysis cells, and all independent basic analysis cells are marked as unassigned. Randomly select a basic analysis cell from the unassigned basic analysis cells as a growth base point. Based on the growth base point, determine all unassigned basic analysis cells that are directly adjacent to it, and construct a detection region based on the growth base point. For any basic analysis cell in the detection area other than the growth base point, calculate the absolute deviation of its dispersion coefficient. The absolute deviation of the dispersion coefficient is specifically expressed as the difference between the dispersion coefficient of the basic analysis cell and the average dispersion coefficient of all basic analysis cells in the detection area other than the growth base point. Based on the standard deviation of the dispersion coefficient of the detection area, the dispersion growth threshold is calculated; If the absolute deviation of the dispersion coefficient of the basic analysis cell within the detection area is not greater than the dispersion growth threshold, then the basic analysis cell is included in the set where the growth base point is located, merged into a new growth base point, and the above process is repeated until no new cells are added, so as to re-form the functional carrying unit. New growth base points are identified in all unassigned basic analysis cells, and the above process is repeated until several newly formed functional carrier units are obtained.

10. A scale-adaptive rural landscape map data production system, used to execute the scale-adaptive rural landscape map data production method according to any one of claims 1-9, characterized in that, include: The data acquisition module is used to acquire multi-dimensional raw data of the rural areas to be divided. The basic parameter set processing module is used to divide the rural area to be divided into several basic analysis cells based on the high-definition remote sensing images in the multi-dimensional raw data, and to parse the feature parameters of each basic analysis cell to form a basic parameter set. The heterogeneity calculation module is used to calculate the local heterogeneity index of each basic analysis cell based on the aforementioned set of basic parameters. The aggregation generation module is used to select basic analysis cells that meet the homogeneity conditions as growth base points based on the local heterogeneity index, and iteratively aggregate adjacent basic analysis cells until aggregation can no longer continue, so as to generate several functional carrying units. The multi-level functional analysis module is used to set three spatial observation levels: the body range, the influence radiation zone, and the macroscopic belonging area. It combines the confidence level of the data used in each spatial observation level to set the theoretical weight of each spatial observation level and determine the functional effectiveness value of each functional attribute of the functional carrying unit at different spatial observation levels. The core function adjudication module is used to analyze the dispersion of the functional effectiveness value of the same functional attribute of the same functional carrier unit at different spatial observation levels, and adjudicate the core function of the functional carrier unit based on the functional attribute with the largest functional effectiveness value. The mapping output module is used to draw a core functional zoning map with the functional carrying unit as the mapping unit based on the core functional adjudication result of the functional carrying unit, and output rural landscape map data containing the core functional zoning map.

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