Land element optimal configuration decision-making system and method based on artificial intelligence

Abstract

The invention relates to the field of land management, in particular to a land element optimal configuration decision system and method based on artificial intelligence, and the system comprises a land remote sensing module, a region segmentation module, a development prediction module, a virtual merging module and a configuration decision module. The region segmentation module is used for generating development regions and calculating shape coefficients, the development prediction module is used for calculating output efficiency of land grids, the virtual merging module is used for merging adjacent development regions, and the configuration decision module is used for planning development types of conflicting grids. The low-efficiency utilization caused by land dispersion is avoided, the land utilization efficiency is improved, the land development and management cost is reduced, the scientificity and accuracy of land allocation are ensured, and efficient utilization of land resources and coordinated development of the ecological environment are achieved.

Description

Technical Field

[0001] The present invention relates to the field of land management, and in particular to an artificial intelligence-based land element optimization configuration decision-making system and method. Background Art

[0002] Land factors refer to the fundamental production elements of land resources, including natural attributes such as area, location, fertility, and geological conditions, as well as socioeconomic attributes such as usage, benefits, and development level. Optimizing the allocation of land factors requires scientific and rational planning and management to achieve the optimal combination of land resources in terms of space, time, and usage, thereby achieving efficient utilization of land resources.

[0003] During land development, due to factors such as the ecological environment, land type, and development history, small, intertwined plots of land, including cultivated land, forest land, and construction land, can appear in developed areas. This phenomenon is known as land fragmentation. Fragmented land can hinder industrial agglomeration, leading to lower total land output. Furthermore, differences in land elements across regions make it difficult to effectively integrate fragmented land, thus affecting land use efficiency.

[0004] In addition, the land planning process involves changes in land types, and a complex forecasting process is required before making land allocation decisions. Most forecasting models take a long time, and the forecast results are not accurate enough. They cannot make the most favorable allocation of land development types, and the level of intelligent decision-making is not high. Summary of the Invention

[0005] The purpose of the present invention is to provide a land element optimization configuration decision-making system and method based on artificial intelligence to solve the problems raised in the above background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a land element optimization configuration decision system based on artificial intelligence, comprising: a land remote sensing module, a regional segmentation module, a development prediction module, a virtual merging module and a configuration decision module;

[0007] The land remote sensing module is used to input regional statistical data into the GIS geographic information system, establish a coordinate system in the satellite remote sensing image of the land to be developed, rasterize the land according to the coordinates, remove the grids of undevelopable land, and input the land element information of each land grid. The land elements include: land development type, land area, land topography, vegetation distribution, population composition, land input and land output;

[0008] The region segmentation module is used to merge grids of the same land development type to form a development area, and the land development type of all adjacent grids in the development area is different from the land development type of the development area. The development area is segmented from the remote sensing image and the regional edges are connected using image intelligent segmentation technology, and the shape coefficient of each development area is calculated based on the edge perimeter and the area of the development area;

[0009] The development prediction module is used to obtain the regional agglomeration effect based on the shape coefficient of the development area and the agglomeration effect coefficient of the industry. The output efficiency of each land grid is calculated based on the input-output ratio, agglomeration effect, land adaptability, development cost and environmental loss of each development type. The output efficiency of all land grids is accumulated to obtain the global output efficiency, and a mapping relationship between the global output efficiency and the land grid output efficiency is established;

[0010] The virtual merging module is used to mark development areas with domain identity below a threshold, and the development areas closest to the connected areas and with the same development type are used as merged areas. The artificial intelligence model is used to establish the shortest path between the connected areas and the merged areas, and the grids passed by the shortest path are merged into the connected areas to connect the connected areas with the merged areas. Grids are selected from around the shortest path and merged into the connected areas to increase the output efficiency of the entire area. This is done until the total land efficiency index decreases after any adjacent grid is added to the merged area, and all newly merged land grids are marked.

[0011] The configuration decision module is used to treat land grids with more than one mark as conflict grids, use development type as a variable, and use the output efficiency of each development type as a weight for the conflict grid to construct an output impact function. The planning condition is to maximize the global output efficiency, and land restrictions and environmental restrictions are used as constraints for planning. The planning status of each conflict grid is determined, and the decision result of the development type of each land grid is output.

[0012] Furthermore, the land remote sensing module includes: a GIS unit and an element statistics unit;

[0013] The GIS unit is used to obtain satellite remote sensing images of the area to be developed, establish regional coordinates, and rasterize the land according to the coordinates;

[0014] The element statistics unit is used to determine the land element information of each grid from satellite images, development logs and land statistical data.

[0015] Furthermore, the region segmentation module includes: a rasterization unit, a boundary recognition unit and a shape evaluation unit;

[0016] The gridding unit is used to merge grids according to land development types to form development areas. When there are more than one development type in a grid, the development type with the largest area is used as the development type of the grid.

[0017] The boundary recognition unit is used to segment the boundaries of the development area from the satellite remote sensing image using image recognition and segmentation models;

[0018] The shape evaluation unit is used to calculate a shape coefficient of the development area according to the perimeter and area of the boundary of the development area.

[0019] Furthermore, the development prediction module includes: a data decision unit and an efficiency evaluation unit;

[0020] The data decision unit is used to calculate the agglomeration effect of each development area and make decisions for changes in development types;

[0021] The efficiency evaluation unit is used to calculate the output efficiency of each land grid and all areas to be developed.

[0022] Furthermore, the virtual merging module includes: an intelligent addressing unit and a grid marking unit;

[0023] The intelligent addressing unit is used to access the artificial intelligence service interface and plan the shortest path for regional connectivity between two development areas;

[0024] The grid marking unit is used to determine the scope of the merged area to maximize the global output efficiency and mark the newly merged grids.

[0025] Furthermore, the configuration decision module includes: a conflict grid unit, an index planning unit and a grid configuration unit;

[0026] The conflict grid unit is used to determine the output efficiency of each development type of the conflict grid configuration and generate an output impact function;

[0027] The index planning unit is used to plan the development type of the conflict grid to maximize the global output efficiency;

[0028] The grid configuration unit is used to update the remote sensing map, output the new development type of each grid, and provide the total development cost.

[0029] A land element optimization configuration decision-making method based on artificial intelligence includes the following steps:

[0030] Step S1. Generate a satellite remote sensing image of the area to be developed, establish a coordinate system in the image, rasterize the land according to the coordinates, remove the grid of undevelopable land, and determine the land elements of each grid from satellite images, development logs, and land statistical data;

[0031] Step S2. Merge the grids with the same land development type to form a development area. The development area is a connected area with no grids of the same development type surrounding it. Use an image segmentation model to segment the boundaries of each development area from the remote sensing image and calculate the shape coefficient of the development area based on the perimeter and area of the boundary.

[0032] Step S3. Multiply the shape coefficient of the development area and the agglomeration effect coefficient of the industry to obtain the regional agglomeration effect. The development parameters of each development type and the regional agglomeration effect are weighted and accumulated to obtain the output efficiency of each land grid and the global output efficiency.

[0033] Step S4. Select two development areas with the same development type and domain identity above a threshold. Use the artificial intelligence model to plan a minimum-cost connecting path between the two development areas. Merge the grids passed by the path into the two development areas to form a connected area. When the overall output efficiency is improved, merge the adjacent grids in the connected area and mark all the merged land grids.

[0034] Step S5. Grids with more than one mark are considered conflict grids. Planning is performed with the maximum global output efficiency as the planning condition and land restrictions and environmental restrictions as the constraints. The development type change decision for each conflict grid is output.

[0035] Furthermore, step S1 includes:

[0036] Step S11. Using a GIS (Geographic Information System), obtain satellite remote sensing images of the area to be developed, establish a coordinate system for the remote sensing images, and divide the remote sensing images into square grids with a side length a, where a is a preset grid width. The number of grids divided is greater than one, and grids representing undevelopable land are removed.

[0037] Step S12. Obtain the land area, land topography, and vegetation distribution type of the region from the remote sensing image. Obtain the land development type, population composition, land input, and land output from the development log and land statistical data. When more than one development type exists in a grid, the development type with the largest area is used as the development type of the grid.

[0038] Label the land elements of each grid, including land development type, land area, land topography, vegetation distribution, population composition, land input and land output.

[0039] Furthermore, step S2 includes:

[0040] Step S21. Merge the grids according to land development type. A merging method is performed on each grid to obtain a development area. The merging method is as follows: if a neighboring grid of the same development type exists, the current grid and the neighboring grid are merged to form a regional core. If a neighboring grid of the same development type exists around the regional core, the neighboring grid is merged into the regional core. If no neighboring grid of the same development type exists around the regional core, the regional core is converted into a development area.

[0041] Step S22. Use the image segmentation model to segment the boundaries of each development area from the remote sensing image, obtain the perimeter and area of the boundary, and calculate the shape coefficient of the development area:

[0042]

[0043] Among them, δ k Represents the shape factor of the development area, Y k Represents the perimeter of the development area, A k represents the area of the development area, r represents the grid scaling factor, and c represents the number of grids included in the development area.

[0044] Furthermore, step S3 includes:

[0045] Step S31. Calculate the land agglomeration effect of each development area, satisfying P = u·δ k , where P represents agglomeration effect, u is the agglomeration effect coefficient of the development type, which includes cultivated land, forest land, construction land, transportation land, industrial and mining land, and water use land. The agglomeration effect coefficient is determined by the ratio of output increment to area increment of the corresponding development type;

[0046] Step S32. Obtain development parameters for each development type, including input-output ratio, agglomeration effect, land adaptability, development cost, and environmental loss, and determine the output efficiency of the land grid:

[0047]

[0048] Where μ is the output efficiency of the land grid, N is the number of development parameters, and F i is the value of the ith development parameter, R i is the weight of the i-th development parameter;

[0049] The output efficiency of all grids is accumulated to obtain the global output efficiency.

[0050] Furthermore, step S4 includes:

[0051] Step S41. Mark two development areas with the same development type and domain identity below a threshold. Domain identity represents the minimum boundary distance between the two areas. Input the change cost of each grid development type into the artificial intelligence model, and plan a connection path between the two areas so that the cumulative change cost of all grids passed by the connection path is minimized.

[0052] Step S42. Merge all grids that the connected path passes through into the two development areas to obtain a connected area. Simulate each grid around the connected area. If the global output efficiency increases after the grid is merged, the grid is merged into the connected area. When there are no grids that can be merged around the connected area, update the connected area and mark all the grids that have been merged in steps S41 and S42.

[0053] Furthermore, step S5 includes:

[0054] Step S51. Grids with at least one marker are considered conflicting grids. The conflicting development type is determined based on the markers of the conflicting grids. The output efficiency increment for each conflicting grid as a development type is calculated to obtain the output efficiency factor μ(x), where x represents the type of conflicting development type and μ(x) represents the increment in global output efficiency when the conflicting grid adopts the xth development type.

[0055] Step S52: Taking the maximum output efficiency of the entire region as the planning condition and land restrictions and environmental restrictions as the constraints, plan each conflicting grid:

[0056]

[0057] Among them, S represents the global output efficiency, M1 and M2 represent the number of development areas and the number of conflict grids respectively, and Z j Represents the output efficiency of the part without conflict grids in the jth development area, μ k (x) represents the output efficiency factor of the kth conflict grid, c x and S x Represent the number and output efficiency of all land grids using the xth development type, c1 and c2 are the upper and lower limits of the number of grids, S0 is the lower limit of output efficiency, H(S) is a mandatory constraint condition determined by the development policy, and H0 is a mandatory constraint parameter;

[0058] Step S53: Determine the development type of each conflicting grid to maximize the overall output efficiency and satisfy the constraints, and output the decision results for all land grids.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] 1. The present invention can use GIS remote sensing technology and statistical data to rasterize land according to development type, merge raster areas with the same development type, and calculate the shape coefficient and neighborhood identity of the area. By integrating fragmented land, it can reduce the waste of land resources, avoid inefficient use caused by land dispersion, and improve land use efficiency.

[0061] 2. The present invention can calculate the efficiency index of each grid based on its land elements, consider land areas with domain identity above a threshold as merged areas, select grids around the merged areas to join, and increase the overall efficiency index of the region as a whole. It can also intelligently select various types of development areas to maximize the benefits of the land allocation process, exchange the same amount of land input for greater resource output, and reduce land development and management costs.

[0062] 3. The present invention constructs a global efficiency index function by taking the conflict area as a variable and the efficiency index increment of each conflict area in the conflict grid as a weight. The planning is carried out with the maximum global efficiency index function as the planning condition, and land restrictions and environmental restrictions as constraints. Iterative decision-making is carried out on the planning results to ensure the scientificity and accuracy of land allocation, achieve efficient utilization of land resources and coordinated development of the ecological environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0064] Figure 1 This is a schematic diagram of the structure of a land element optimization configuration decision-making system based on artificial intelligence of the present invention;

[0065] Figure 2 It is a schematic diagram of the steps of a land element optimization configuration decision-making method based on artificial intelligence of the present invention. DETAILED DESCRIPTION

[0066] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0067] See also Figure 1 , the present invention provides a technical solution: a land element optimization configuration decision system based on artificial intelligence, including: a land remote sensing module, a regional segmentation module, a development prediction module, a virtual merging module and a configuration decision module;

[0068] The land remote sensing module is used to input regional statistical data into the GIS geographic information system, establish a coordinate system in the satellite remote sensing image of the land to be developed, rasterize the land according to the coordinates, remove the grids of undevelopable land, and input the land element information of each land grid. The land elements include: land development type, land area, land topography, vegetation distribution, population composition, land input and land output;

[0069] The land remote sensing module includes: a GIS unit and an element statistics unit;

[0070] The GIS unit is used to obtain satellite remote sensing images of the area to be developed, establish regional coordinates, and rasterize the land according to the coordinates;

[0071] The element statistics unit is used to determine the land element information of each grid from satellite images, development logs and land statistical data.

[0072] The region segmentation module is used to merge grids of the same land development type to form a development area, and the land development type of all adjacent grids in the development area is different from the land development type of the development area. The development area is segmented from the remote sensing image and the regional edges are connected using image intelligent segmentation technology, and the shape coefficient of each development area is calculated based on the edge perimeter and the area of the development area;

[0073] The region segmentation module includes: a rasterization unit, a boundary recognition unit and a shape evaluation unit;

[0074] The gridding unit is used to merge grids according to land development types to form development areas. When there are more than one development type in a grid, the development type with the largest area is used as the development type of the grid.

[0075] The boundary recognition unit is used to segment the boundaries of the development area from the satellite remote sensing image using image recognition and segmentation models;

[0076] The shape evaluation unit is used to calculate a shape coefficient of the development area according to the perimeter and area of the boundary of the development area.

[0077] The development prediction module is used to obtain the regional agglomeration effect based on the shape coefficient of the development area and the agglomeration effect coefficient of the industry. The output efficiency of each land grid is calculated based on the input-output ratio, agglomeration effect, land adaptability, development cost and environmental loss of each development type. The output efficiency of all land grids is accumulated to obtain the global output efficiency, and a mapping relationship between the global output efficiency and the land grid output efficiency is established;

[0078] The development prediction module includes: a data decision unit and an efficiency evaluation unit;

[0079] The data decision unit is used to calculate the agglomeration effect of each development area and make decisions for changes in development types;

[0080] The efficiency evaluation unit is used to calculate the output efficiency of each land grid and all areas to be developed.

[0081] The virtual merging module is used to mark development areas with domain identity below a threshold, and the development areas closest to the connected areas and with the same development type are used as merged areas. The artificial intelligence model is used to establish the shortest path between the connected areas and the merged areas, and the grids passed by the shortest path are merged into the connected areas to connect the connected areas with the merged areas. Grids are selected from around the shortest path and merged into the connected areas to increase the output efficiency of the entire area. This is done until the total land efficiency index decreases after any adjacent grid is added to the merged area, and all newly merged land grids are marked.

[0082] The virtual merging module includes: an intelligent addressing unit and a grid marking unit;

[0083] The intelligent addressing unit is used to access the artificial intelligence service interface and plan the shortest path for regional connectivity between two development areas;

[0084] The grid marking unit is used to determine the scope of the merged area to maximize the global output efficiency and mark the newly merged grids.

[0085] The configuration decision module is used to treat land grids with more than one mark as conflict grids, use development type as a variable, and use the output efficiency of each development type as a weight for the conflict grid to construct an output impact function. The planning condition is to maximize the global output efficiency, and land restrictions and environmental restrictions are used as constraints for planning. The planning status of each conflict grid is determined, and the decision result of the development type of each land grid is output.

[0086] The configuration decision module includes: a conflict grid unit, an index planning unit and a grid configuration unit;

[0087] The conflict grid unit is used to determine the output efficiency of each development type of the conflict grid configuration and generate an output impact function;

[0088] The index planning unit is used to plan the development type of the conflict grid to maximize the global output efficiency;

[0089] The grid configuration unit is used to update the remote sensing map, output the new development type of each grid, and provide the total development cost.

[0090] like Figure 2 As shown, a land element optimization configuration decision-making method based on artificial intelligence includes the following steps:

[0091] Step S1. Generate a satellite remote sensing image of the area to be developed, establish a coordinate system in the image, rasterize the land according to the coordinates, remove the grid of undevelopable land, and determine the land elements of each grid from satellite images, development logs, and land statistical data;

[0092] Step S1 includes:

[0093] Step S11. Using a GIS (Geographic Information System), obtain satellite remote sensing images of the area to be developed, establish a coordinate system for the remote sensing images, and divide the remote sensing images into square grids with a side length a, where a is a preset grid width. The number of grids divided is greater than one, and grids representing undevelopable land are removed.

[0094] Step S12. Obtain the land area, land topography, and vegetation distribution type of the region from the remote sensing image. Obtain the land development type, population composition, land input, and land output from the development log and land statistical data. When more than one development type exists in a grid, the development type with the largest area is used as the development type of the grid.

[0095] Label the land elements of each grid, including land development type, land area, land topography, vegetation distribution, population composition, land input and land output.

[0096] Step S2. Merge the grids with the same land development type to form a development area. The development area is a connected area with no grids of the same development type surrounding it. Use an image segmentation model to segment the boundaries of each development area from the remote sensing image and calculate the shape coefficient of the development area based on the perimeter and area of the boundary.

[0097] Step S2 includes:

[0098] Step S21. Merge the grids according to land development type. A merging method is performed on each grid to obtain a development area. The merging method is as follows: if a neighboring grid of the same development type exists, the current grid and the neighboring grid are merged to form a regional core. If a neighboring grid of the same development type exists around the regional core, the neighboring grid is merged into the regional core. If no neighboring grid of the same development type exists around the regional core, the regional core is converted into a development area.

[0099] Step S22. Use the image segmentation model to segment the boundaries of each development area from the remote sensing image, obtain the perimeter and area of the boundary, and calculate the shape coefficient of the development area:

[0100]

[0101] Among them, δ k Represents the shape factor of the development area, Y kRepresents the perimeter of the development area, A k represents the area of the development area, r represents the grid scaling factor, and c represents the number of grids included in the development area.

[0102] Step S3. Multiply the shape coefficient of the development area and the agglomeration effect coefficient of the industry to obtain the regional agglomeration effect. The development parameters of each development type and the regional agglomeration effect are weighted and accumulated to obtain the output efficiency of each land grid and the global output efficiency.

[0103] Step S3 includes:

[0104] Step S31. Calculate the land agglomeration effect of each development area, satisfying P = u·δ k , where P represents agglomeration effect, u is the agglomeration effect coefficient of the development type, which includes cultivated land, forest land, construction land, transportation land, industrial and mining land, and water use land. The agglomeration effect coefficient is determined by the ratio of output increment to area increment of the corresponding development type;

[0105] Step S32. Obtain development parameters for each development type, including input-output ratio, agglomeration effect, land adaptability, development cost, and environmental loss, and determine the output efficiency of the land grid:

[0106]

[0107] Where μ is the output efficiency of the land grid, N is the number of development parameters, and F i is the value of the ith development parameter, R i is the weight of the i-th development parameter;

[0108] The output efficiency of all grids is accumulated to obtain the global output efficiency.

[0109] Step S4. Select two development areas with the same development type and domain identity above a threshold. Use the artificial intelligence model to plan a minimum-cost connecting path between the two development areas. Merge the grids passed by the path into the two development areas to form a connected area. When the overall output efficiency is improved, merge the adjacent grids in the connected area and mark all the merged land grids.

[0110] Step S4 includes:

[0111] Step S41. Mark two development areas with the same development type and domain identity below a threshold. Domain identity represents the minimum boundary distance between the two areas. Input the change cost of each grid development type into the artificial intelligence model, and plan a connection path between the two areas so that the cumulative change cost of all grids passed by the connection path is minimized.

[0112] Step S42. Merge all grids that the connected path passes through into the two development areas to obtain a connected area. Simulate each grid around the connected area. If the global output efficiency increases after the grid is merged, the grid is merged into the connected area. When there are no grids that can be merged around the connected area, update the connected area and mark all the grids that have been merged in steps S41 and S42.

[0113] Step S5. Grids with more than one mark are considered conflict grids. Planning is performed with the maximum global output efficiency as the planning condition and land restrictions and environmental restrictions as the constraints. The development type change decision for each conflict grid is output.

[0114] Step S5 includes:

[0115] Step S51. Grids with at least one marker are considered conflicting grids. The conflicting development type is determined based on the markers of the conflicting grids. The output efficiency increment for each conflicting grid as a development type is calculated to obtain the output efficiency factor μ(x), where x represents the type of conflicting development type and μ(x) represents the increment in global output efficiency when the conflicting grid adopts the xth development type.

[0116] Step S52: Taking the maximum output efficiency of the entire region as the planning condition and land restrictions and environmental restrictions as the constraints, plan each conflicting grid:

[0117]

[0118] Among them, S represents the global output efficiency, M1 and M2 represent the number of development areas and the number of conflict grids respectively, and Z j Represents the output efficiency of the part without conflict grids in the jth development area, μ k (x) represents the output efficiency factor of the kth conflict grid, c x and S x Represent the number and output efficiency of all land grids using the xth development type, c1 and c2 are the upper and lower limits of the number of grids, S0 is the lower limit of output efficiency, H(S) is a mandatory constraint condition determined by the development policy, and H0 is a mandatory constraint parameter;

[0119] Step S53: Determine the development type of each conflicting grid to maximize the overall output efficiency and satisfy the constraints, and output the decision results for all land grids.

[0120] Example: There are 10 grids in the area to be developed, of which 2 grids are cultivated land, 3 grids are forest land, 3 grids are industrial and mining land, 1 grid is construction land, and 1 grid is water use land. It is stipulated that the cultivated land grid is not less than 2, the water use land grid is not less than 1, and the output efficiency of cultivated land, forest land, industrial and mining land, construction land, and water use land are 0.2, 0.1, 0.3, 0.5, and 0.1, respectively. The overall output efficiency is 2.2. There is one conflicting grid in the forest land. If the grid is cultivated land, the overall output efficiency becomes 2.3, and if it is construction land, the overall output efficiency becomes 2.5. In this case, the development type of the conflicting grid is changed from forest land to construction land.

[0121] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0122] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A land element optimization configuration decision-making method based on artificial intelligence, characterized in that: The method comprises the following steps: Step S1. generating a satellite remote sensing image of the area to be developed, establishing a coordinate system in the image, rasterizing the land according to the coordinates, removing the grids of undevelopable land, and determining the land elements of each grid from the satellite image, development logs and land statistical data; Step S2. Merge the grids with the same land development type to form a development area. The development area is a connected area with no grids of the same development type surrounding it. Use an image segmentation model to segment the boundaries of each development area from the remote sensing image and calculate the shape coefficient of the development area based on the perimeter and area of the boundary. Step S3. Multiply the shape coefficient of the development area and the agglomeration effect coefficient of the industry to obtain the regional agglomeration effect. The development parameters of each development type and the regional agglomeration effect are weighted and accumulated to obtain the output efficiency of each land grid and the global output efficiency. Step S4. Select two development areas with the same development type and domain identity above a threshold. Use the artificial intelligence model to plan a minimum-cost connecting path between the two development areas. Merge the grids passed by the path into the two development areas to form a connected area. When the overall output efficiency is improved, merge the adjacent grids in the connected area and mark all the merged land grids. Step S5. Grids with more than one mark are considered conflict grids. Planning is performed with the maximum global output efficiency as the planning condition and land restrictions and environmental restrictions as the constraints. The development type change decision for each conflict grid is output.

2. The method for optimizing land element allocation based on artificial intelligence according to claim 1, characterized in that: Step S1 includes: Step S11. Using a GIS (Geographic Information System), obtain satellite remote sensing images of the area to be developed, establish a coordinate system for the remote sensing images, and divide the remote sensing images into square grids with a side length a, where a is a preset grid width. The number of grids divided is greater than one, and grids representing undevelopable land are removed. Step S12. Obtain the land area, land topography, and vegetation distribution type of the region from the remote sensing image. Obtain the land development type, population composition, land input, and land output from the development log and land statistical data. When more than one development type exists in a grid, the development type with the largest area is used as the development type of the grid. Label the land elements of each grid, including land development type, land area, land topography, vegetation distribution, population composition, land input and land output.

3. The method for optimizing land element allocation based on artificial intelligence according to claim 2, characterized in that: Step S2 includes: Step S21. Merge the grids according to land development type. A merging method is performed on each grid to obtain a development area. The merging method is as follows: if a neighboring grid of the same development type exists, the current grid and the neighboring grid are merged to form a regional core. If a neighboring grid of the same development type exists around the regional core, the neighboring grid is merged into the regional core. If no neighboring grid of the same development type exists around the regional core, the regional core is converted into a development area. Step S22. Use the image segmentation model to segment the boundaries of each development area from the remote sensing image, obtain the perimeter and area of the boundary, and calculate the shape coefficient of the development area: Among them, δ k Represents the shape factor of the development area, Y k Represents the perimeter of the development area, A k represents the area of the development area, r represents the grid scaling factor, and c represents the number of grids included in the development area.

4. The method for optimizing land element allocation based on artificial intelligence according to claim 3, characterized in that: Step S3 include: Step S31. Calculate the land agglomeration effect of each development area, satisfying P = u·δ k , where P represents agglomeration effect, u is the agglomeration effect coefficient of the development type, which includes cultivated land, forest land, construction land, transportation land, industrial and mining land, and water use land. The agglomeration effect coefficient is determined by the ratio of output increment to area increment of the corresponding development type; Step S32. Obtain development parameters for each development type, including input-output ratio, agglomeration effect, land adaptability, development cost, and environmental loss, and determine the output efficiency of the land grid: Where μ is the output efficiency of the land grid, N is the number of development parameters, and F i is the value of the ith development parameter, R i is the weight of the i-th development parameter; The output efficiency of all grids is accumulated to obtain the global output efficiency; Step S4 includes: Step S41. Mark two development areas with the same development type and domain identity below a threshold. Domain identity represents the minimum boundary distance between the two areas. Input the change cost of each grid development type into the artificial intelligence model, and plan a connection path between the two areas so that the cumulative change cost of all grids passed by the connection path is minimized. Step S42. Merge all grids that the connected path passes through into the two development areas to obtain a connected area. Simulate each grid around the connected area. If the global output efficiency increases after the grid is merged, the grid is merged into the connected area. When there are no grids that can be merged around the connected area, update the connected area and mark all the grids that have been merged in steps S41 and S42.

5. The method for optimizing land element configuration based on artificial intelligence according to claim 4, characterized in that: Step S5 includes: Step S51. Grids with at least one marker are considered conflicting grids. The conflicting development type is determined based on the markers of the conflicting grids. The output efficiency increment for each conflicting grid as a development type is calculated to obtain the output efficiency factor μ(x), where x represents the type of conflicting development type and μ(x) represents the increment in global output efficiency when the conflicting grid adopts the xth development type. Step S52: Taking the maximum output efficiency of the entire region as the planning condition and land restrictions and environmental restrictions as the constraints, plan each conflicting grid: Among them, S represents the global output efficiency, M1 and M2 represent the number of development areas and the number of conflict grids respectively, and Z j Represents the output efficiency of the part without conflict grids in the jth development area, μ k (x) represents the output efficiency factor of the kth conflict grid, c x and S x Represent the number and output efficiency of all land grids using the xth development type, c1 and c2 are the upper and lower limits of the number of grids, S0 is the lower limit of output efficiency, H(S) is a mandatory constraint condition determined by the development policy, and H0 is a mandatory constraint parameter; Step S53: Determine the development type of each conflicting grid to maximize the overall output efficiency and satisfy the constraints, and output the decision results for all land grids.

6. A land element optimization configuration decision system based on artificial intelligence, characterized by: The system includes the following modules: land remote sensing module, regional segmentation module, development prediction module, virtual merging module and configuration decision module; The land remote sensing module is used to input regional statistical data into the GIS geographic information system, establish a coordinate system in the satellite remote sensing image of the land to be developed, rasterize the land according to the coordinates, remove the grids of undevelopable land, and input the land element information of each land grid. The land elements include: land development type, land area, land topography, vegetation distribution, population composition, land input and land output; The region segmentation module is used to merge grids of the same land development type to form a development area, and the land development type of all adjacent grids in the development area is different from the land development type of the development area. The development area is segmented from the remote sensing image and the regional edges are connected using image intelligent segmentation technology, and the shape coefficient of each development area is calculated based on the edge perimeter and the area of the development area; The development prediction module is used to obtain the regional agglomeration effect based on the shape coefficient of the development area and the agglomeration effect coefficient of the industry. The output efficiency of each land grid is calculated based on the input-output ratio, agglomeration effect, land adaptability, development cost and environmental loss of each development type. The output efficiency of all land grids is accumulated to obtain the global output efficiency, and a mapping relationship between the global output efficiency and the land grid output efficiency is established; The virtual merging module is used to mark development areas with domain identity below a threshold, and the development areas closest to the connected areas and with the same development type are used as merged areas. The artificial intelligence model is used to establish the shortest path between the connected areas and the merged areas, and the grids passed by the shortest path are merged into the connected areas to connect the connected areas with the merged areas. Grids are selected from around the shortest path and merged into the connected areas to increase the output efficiency of the entire area. This is done until the total land efficiency index decreases after any adjacent grid is added to the merged area, and all newly merged land grids are marked. The configuration decision module is used to treat land grids with more than one mark as conflict grids, use development type as a variable, and use the output efficiency of each development type as a weight for the conflict grid to construct an output impact function. The planning condition is to maximize the global output efficiency, and land restrictions and environmental restrictions are used as constraints for planning. The planning status of each conflict grid is determined, and the decision result of the development type of each land grid is output.

7. The artificial intelligence-based land element optimization configuration decision-making system according to claim 6 is characterized by: The land remote sensing module includes: a GIS unit and an element statistics unit; The GIS unit is used to obtain satellite remote sensing images of the area to be developed, establish regional coordinates, and rasterize the land according to the coordinates; The element statistics unit is used to determine the land element information of each grid from satellite images, development logs and land statistical data.

8. The artificial intelligence-based land element optimization configuration decision-making system according to claim 7 is characterized by: The region segmentation module includes: a rasterization unit, a boundary recognition unit and a shape evaluation unit; The gridding unit is used to merge grids according to land development types to form development areas. When there are more than one development type in a grid, the development type with the largest area is used as the development type of the grid. The boundary recognition unit is used to segment the boundaries of the development area from the satellite remote sensing image using image recognition and segmentation models; The shape evaluation unit is used to calculate a shape coefficient of the development area according to the perimeter and area of the boundary of the development area.

9. The artificial intelligence-based land element optimization configuration decision-making system according to claim 8, characterized in that: The development prediction module includes: a data decision unit and an efficiency evaluation unit; The data decision unit is used to calculate the agglomeration effect of each development area and make decisions for changes in development types; The efficiency evaluation unit is used to calculate the output efficiency of each land grid and all areas to be developed; The virtual merging module includes: an intelligent addressing unit and a grid marking unit; The intelligent addressing unit is used to access the artificial intelligence service interface and plan the shortest path for regional connectivity between two development areas; The grid marking unit is used to determine the scope of the merged area to maximize the global output efficiency and mark the newly merged grids.

10. The artificial intelligence-based land element optimization configuration decision-making system according to claim 9, characterized in that: The configuration decision module includes: a conflict grid unit, an index planning unit and a grid configuration unit; The conflict grid unit is used to determine the output efficiency of each development type of the conflict grid configuration and generate an output impact function; The index planning unit is used to plan the development type of the conflict grid to maximize the global output efficiency; The grid configuration unit is used to update the remote sensing map, output the new development type of each grid, and provide the total development cost.

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