A method for collaborative optimization of watershed forest ecological products

Abstract

This invention discloses a method for collaborative optimization of watershed forest ecological products, specifically relating to the field of data processing and optimization technology. It involves acquiring watershed digital elevation data, river network data, and forest patch distribution data; calculating confluence direction and cumulative contribution area to construct a hydrological connectivity matrix; superimposing the cumulative attenuation of ecological product supply and demand grids based on the hydrological connectivity matrix to generate an ecological product spatial flow matrix; calculating the benefit transfer coefficient based on the ecological product spatial flow matrix combined with the population exposure of the benefiting area and the weight of the water-using industry to generate a benefit transfer matrix; constructing a collaborative optimization model with consistent constraints on the supply and demand sides using the ecological product spatial flow matrix and the benefit transfer matrix, and solving it to obtain a set of spatial allocation schemes; and performing upstream and downstream sharing calculations and cross-regional quota allocation on the spatial allocation scheme set to obtain spatial allocation and distribution schemes.

Description

Technical Field

[0001] This invention relates to the field of data processing and optimization technology, and more specifically, to a method for collaborative optimization of watershed forest ecological products. Background Technology

[0002] Watershed forest ecological products exhibit distinct spatial distribution characteristics, with upstream forest ecosystems often providing downstream areas with ecological services such as water conservation and soil and water conservation. Existing methods for allocating and managing watershed ecological products typically use administrative divisions as boundaries, lacking a deep understanding of the overall spatial flow patterns of watershed ecological products and failing to establish a coordinated relationship between upstream and downstream regions for ecological product supply and demand. This results in unclear upstream and downstream relationships regarding ecological product allocation and responsibility sharing, hindering the optimal allocation and effective management of ecological product resources.

[0003] Existing methods for allocating watershed forest ecological products typically lack a unified constraint expression of the spatial transmission relationship and cross-regional benefit correspondence of ecological products under the condition of watershed hydrological connectivity. This makes it difficult to match the contribution of the supply side and the benefit of the demand side on the same scale, and thus makes it difficult to form directly executable allocation results for cross-regional collaborative allocation and sharing accounting. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a method for collaborative optimization of watershed forest ecological products to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A method for synergistic optimization of watershed forest ecological products includes the following steps:

[0007] S1: Obtain watershed digital elevation data, river network data, and forest patch distribution data, and then uniformly project and rasterize them to obtain a watershed spatial element dataset;

[0008] S2: Calculate the confluence direction and cumulative contribution area based on the watershed spatial element dataset, and construct a hydrological connectivity matrix;

[0009] S3: Based on the hydrological connectivity matrix, the ecological product supply grid and the ecological product demand grid are superimposed with path accumulation and attenuation to obtain the ecological product spatial flow matrix.

[0010] S4: Calculate the benefit transfer coefficient based on the spatial flow matrix of ecological products, combined with the population exposure of the beneficiary area and the weight of the water-using industry, to obtain the benefit transfer matrix;

[0011] S5: Construct a collaborative optimization model with consistent constraints on the supply and demand sides using the spatial flow matrix of ecological products and the benefit transfer matrix, and solve it to obtain a set of spatial configuration schemes;

[0012] S6: Perform upstream and downstream cost-sharing calculations and cross-regional quota allocations on the spatial configuration scheme set to obtain spatial configuration and allocation schemes.

[0013] In a preferred embodiment, S1 specifically refers to:

[0014] The watershed boundary range is determined by the administrative boundary of the watershed to obtain watershed digital elevation data, watershed river network data is generated based on the watershed digital elevation data, and forest patch distribution data is determined by combining forest resource survey data;

[0015] The watershed digital elevation data, watershed river network data, and forest patch distribution data are projected with unified coordinates and rasterized according to a set raster scale to generate a watershed spatial element dataset.

[0016] In a preferred embodiment, S2 specifically refers to:

[0017] Based on the watershed spatial element dataset, depression filling and slope smoothing are performed on the watershed digital elevation data.

[0018] The downstream confluence direction pointed to by each grid is calculated based on the processed watershed digital elevation data, and a confluence direction grid is generated.

[0019] Based on the confluence direction grid, the upstream inflow relationship is accumulated and summarized to form a cumulative contribution area grid;

[0020] Consistency checks are performed between the cumulative contribution area raster and the watershed river network data, and rasteres that deviate from the confluence direction are corrected to generate a hydrological connectivity matrix.

[0021] In a preferred embodiment, S3 specifically refers to:

[0022] Based on the hydrological connectivity matrix, forest patch distribution data are converted into ecological product supply raster, and ecological product demand raster is constructed based on land use type and population spatial distribution data of the beneficiary area.

[0023] The ecological product supply grid is accumulated level by level along the downstream path determined by the hydrological connectivity matrix, and the attenuation is calculated in combination with the path length to form a path accumulation attenuation grid.

[0024] By spatially overlaying and matching the path cumulative attenuation grid with the ecological product demand grid, a spatial flow matrix of ecological products is generated.

[0025] In a preferred embodiment, S4 specifically refers to:

[0026] Based on the spatial flow matrix of ecological products, identify the set of upstream supply source grids corresponding to each benefiting area, and calculate the path contribution value of each supply source grid in the spatial flow matrix of ecological products.

[0027] Spatial matching of path contribution values ​​with spatial distribution data of the population in the beneficiary area is used to form a raster of population exposure distribution in the beneficiary area.

[0028] By combining the water use industry weights corresponding to the land use types in the beneficiary areas, the population exposure distribution grid of the beneficiary areas is weighted and calculated to construct the benefit transfer coefficient. Then, a benefit transfer matrix is ​​generated according to the correspondence between the upstream supply source grid and the beneficiary areas.

[0029] In a preferred embodiment, S5 specifically refers to:

[0030] Using the supply path relationship represented by the ecological product spatial flow matrix and the benefit distribution relationship represented by the benefit transfer matrix as constraint inputs, a collaborative optimization model is constructed that includes the total balance constraint on the supply side and the benefit matching constraint on the demand side.

[0031] In the collaborative optimization model, a mapping relationship is established between the decision variables corresponding to the ecological product supply grid and the benefit transfer coefficients in the benefit transfer matrix, forming a consistent constraint expression between the supply and demand sides.

[0032] The collaborative optimization model is solved iteratively to generate a set of spatial configuration schemes that satisfy the consistency constraints.

[0033] In a preferred embodiment, S6 specifically refers to:

[0034] Based on the spatial configuration scheme set, determine the grid set of ecological product supply sources corresponding to each beneficiary area;

[0035] By combining the path contribution value determined by the ecological product spatial flow matrix and the benefit transfer coefficient determined by the benefit transfer matrix, the ecological product supply responsibility ratio between each benefit region and the corresponding supply source grid is calculated.

[0036] Based on the proportion of responsibility for the supply of ecological products, upstream and downstream sharing is calculated, and cross-regional quota allocation is carried out based on the calculation results to form a spatial configuration and allocation scheme.

[0037] The technical effects and advantages of the method for collaborative optimization of watershed forest ecological products of the present invention are as follows:

[0038] By uniformly projecting and rasterizing watershed digital elevation data, river network data, and forest patch distribution data, a watershed spatial element dataset is generated, enabling the watershed spatial analysis objects to be computable and alignable under the same spatial benchmark. A hydrological connectivity matrix is ​​constructed by calculating confluence directions and cumulative contribution areas, allowing for traceable representation of upstream-to-downstream connectivity paths. An ecological product spatial flow matrix is ​​generated by superimposing ecological product supply and demand raster grids with path cumulative attenuation, providing a quantitative carrier for the spatial transmission relationship from the supply to the demand side of ecological products. A benefit transfer coefficient is calculated by combining the population exposure of the beneficiary area with the weight of the water-using industry, generating a benefit transfer matrix, ensuring a consistent mapping between benefit distribution and supply path relationships. A collaborative optimization model with consistent constraints on the supply and demand sides is constructed and solved to obtain a set of spatial configuration schemes, ensuring that the spatial configuration results simultaneously satisfy supply ceiling constraints and benefit matching constraints. By performing upstream-downstream sharing accounting and cross-regional quota allocation on the spatial configuration scheme set, the cross-regional collaborative configuration results form spatial configuration and allocation schemes that can be directly used for sharing accounting and quota execution. Attached Figure Description

[0039] Figure 1 This is a schematic diagram of a watershed forest ecological product collaborative optimization method according to the present invention. Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0041] Example 1

[0042] Figure 1 This invention presents a method for synergistic optimization of watershed forest ecological products, comprising the following steps:

[0043] S1: Obtain watershed digital elevation data, river network data, and forest patch distribution data, and then uniformly project and rasterize them to obtain a watershed spatial element dataset;

[0044] S2: Calculate the confluence direction and cumulative contribution area based on the watershed spatial element dataset, and construct a hydrological connectivity matrix;

[0045] S3: Based on the hydrological connectivity matrix, the ecological product supply grid and the ecological product demand grid are superimposed with path accumulation and attenuation to obtain the ecological product spatial flow matrix.

[0046] S4: Calculate the benefit transfer coefficient based on the spatial flow matrix of ecological products, combined with the population exposure of the beneficiary area and the weight of the water-using industry, to obtain the benefit transfer matrix;

[0047] S5: Construct a collaborative optimization model with consistent constraints on the supply and demand sides using the spatial flow matrix of ecological products and the benefit transfer matrix, and solve it to obtain a set of spatial configuration schemes;

[0048] S6: Perform upstream and downstream cost-sharing calculations and cross-regional quota allocations on the spatial configuration scheme set to obtain spatial configuration and allocation schemes.

[0049] S1: Acquire watershed digital elevation data, river network data, and forest patch distribution data, and uniformly project and rasterize them to obtain a watershed spatial element dataset, including:

[0050] The watershed boundary range is determined by the administrative boundary of the watershed to obtain watershed digital elevation data, watershed river network data is generated based on the watershed digital elevation data, and forest patch distribution data is determined by combining forest resource survey data;

[0051] Specifically, the administrative boundary of the watershed is determined as the watershed's boundary range. This boundary is based on locally released administrative boundary vector data, including but not limited to county, city, or provincial level administrative boundary vector data. After determining the administrative boundary, it is used as a mask boundary to extract watershed digital elevation data matching the administrative boundary range from publicly released digital elevation data. Digital elevation data refers to datasets containing surface elevation information; for example, publicly available remote sensing mapping digital elevation data with a spatial resolution of 30 meters or higher can be selected as the source of watershed digital elevation data.

[0052] The specific steps for generating watershed river network data based on watershed digital elevation data are as follows: First, the confluence direction is calculated from the watershed digital elevation data. The D8 algorithm is used to determine the confluence direction of each raster cell; that is, among the eight neighboring directions surrounding the raster cell, the lowest-lying neighbor is selected as the downstream confluence direction. After determining the confluence direction, the cumulative confluence is calculated, which is the cumulative area contributed by all upstream cells to each raster cell, to obtain the cumulative contribution area distribution of each raster cell. River network rasters are generated by setting a river network determination threshold. The river network determination threshold is determined when the cumulative contribution area reaches a certain size, indicating that the corresponding raster cell belongs to the river network. The river network determination threshold is selected based on the watershed topographic features or existing river network vector data; for example, the river network determination threshold is set to range from 10 square kilometers to 50 square kilometers. After generating the river network raster, it is vectorized to generate watershed river network data. The watershed river network data is vector data, expressing the location and direction of rivers within the watershed in the form of broken lines.

[0053] Determining forest patch distribution data by combining forest resource survey data involves: acquiring forest resource survey data, which is forest type and spatial distribution data obtained through field surveys, remote sensing image interpretation, or a combination thereof, including the geographical location, polygon boundary range, and attribute information such as tree species, age, and canopy closure for each forest patch; extracting the spatial distribution information of the forest patch polygons from the forest resource survey data, retaining only the polygon boundary range and necessary forest type information to generate forest patch distribution data; and representing the forest patch distribution data in the form of polygon vector data.

[0054] Unifying the projection coordinates of watershed digital elevation data, watershed river network data, and forest patch distribution data involves: determining a unified projection coordinate system, such as the Gauss-Kruger projection coordinate system, and selecting a central meridian parameter suitable for the watershed area to ensure that all spatial data are unified under the same projection coordinate system. Then, the watershed digital elevation data, watershed river network data, and forest patch distribution data are converted from their original coordinate systems to the unified projection coordinate system.

[0055] The watershed digital elevation data, watershed river network data, and forest patch distribution data are projected with unified coordinates and rasterized according to a set raster scale to generate a watershed spatial element dataset.

[0056] Specifically, a suitable raster scale for watershed-scale analysis is determined, i.e., the spatial size of the raster cell. For example, the raster scale can be set to a range of 30m × 30m or 90m × 90m. When rasterizing watershed digital elevation data after unifying projected coordinates, new raster data is generated by re-interpolating the original digital elevation data at the raster scale. When rasterizing watershed river network data after unifying projected coordinates, the river network vector data is rasterized at the raster scale. If river network vector data passes through a raster cell, it is marked as a river network raster cell. When rasterizing forest patch distribution data after unifying projected coordinates, the forest patch polygon data is rasterized at the raster scale. If a raster cell spatially overlaps with the forest patch polygon data, the raster cell is marked as a forest patch raster. After rasterization at a unified scale, a watershed spatial element dataset is generated; the watershed spatial element dataset includes watershed digital elevation raster data, watershed river network raster data, and forest patch raster data.

[0057] S2: Based on the watershed spatial element dataset, calculate the confluence direction and cumulative contribution area, and construct a hydrological connectivity matrix, including:

[0058] Based on the watershed spatial element dataset, depression filling and slope smoothing are performed on the watershed digital elevation data.

[0059] Specifically, the process iterates through all raster cells in the watershed digital elevation raster data, comparing the elevation value of each raster cell with the elevation values ​​of its neighboring raster cells. If a raster cell's elevation value is lower than the elevation values ​​of all its neighboring raster cells, it is determined that the corresponding raster cell contains a local depression. For the identified local depression raster cells, the elevation value is adjusted using an interpolation algorithm, including distance-weighted inverse proportional interpolation or average value interpolation, so that the adjusted local depression raster cell's elevation value is equal to or higher than the lowest elevation value of its neighboring raster cells, thereby achieving the filling of the depression. After the depression filling process is completed, the watershed digital elevation raster data is subjected to slope smoothing processing. The elevation value of each raster cell is processed using a spatial neighborhood mean filtering algorithm or a Gaussian smoothing filtering algorithm, so that the elevation value of each raster cell after processing is equal to the weighted average of the elevation values ​​of all raster cells in the raster cell's neighborhood. For example, a new elevation value is obtained by weighted averaging the elevation values ​​in the 3×3 neighborhood of each raster cell, so as to achieve the purpose of slope smoothing and form the processed watershed digital elevation data.

[0060] The downstream confluence direction pointed to by each grid is calculated based on the processed watershed digital elevation data, and a confluence direction grid is generated.

[0061] Specifically, using the processed digital elevation data of the watershed as input, the downstream confluence direction of each grid cell is determined using the D8 algorithm. This algorithm compares the elevation value of each grid cell with the elevation values ​​of its eight neighboring grid cells and selects the grid cell with the lowest elevation value in its neighborhood as the downstream confluence direction of that grid cell. This determines the confluence direction vector corresponding to each grid cell. The confluence direction vector is represented in the form of a direction angle or a direction code. For example, numbers 1 to 8 are typically used to represent the directions of the eight neighboring grid cells, thus determining the confluence direction of all grid cells and generating confluence direction grid data. The confluence direction grid data is in grid format, and each grid cell value in the grid data represents the downstream confluence direction corresponding to that grid cell.

[0062] Based on the confluence direction grid, the upstream inflow relationship is accumulated and summarized to form a cumulative contribution area grid;

[0063] Specifically, based on the confluence direction raster data, all upstream confluence raster units corresponding to each raster unit are identified from the watershed digital elevation raster data. The areas of the upstream confluence raster units are accumulated level by level downstream to obtain the total upstream confluence area corresponding to each raster unit, which is the cumulative contribution area raster data. The calculation method is as follows: starting from the raster unit at the highest position within the watershed, the raster units are traversed sequentially downstream. The initial contribution area of ​​each raster unit is its own area. Then, the contribution area values ​​of all directly confluenced upstream raster units are added to the raster unit. This accumulation process is repeated level by level until all raster units are traversed, thus obtaining the cumulative contribution area raster data corresponding to each raster unit. The cumulative contribution area raster data is represented in raster form, and the value of each raster unit represents the total upstream confluence area flowing into the raster unit.

[0064] Based on the cumulative contribution area raster and the watershed river network data, consistency verification is performed and raster that deviates from the confluence direction is corrected to generate a hydrological connectivity matrix.

[0065] Specifically, the cumulative contribution area raster data is spatially compared with the watershed river network raster data to verify whether there are inconsistencies or deviations in the raster positions. The watershed river network data is river network raster data converted from vector data, representing the actual river channels in spatial location. The cumulative contribution area raster data is raster data calculated from the confluence direction raster data. Consistency verification is performed by setting a verification threshold method. For example, an area difference threshold can be set, and the positions of raster cells in the cumulative contribution area raster with confluence areas greater than the verification threshold are compared with the river network raster positions. If the position of a raster cell deviates from the actual river network position, it is determined that the raster cell has an error of deviating from the confluence direction. For the identified raster cells that deviate from the confluence direction, the confluence direction of the raster cell is corrected using the watershed river network raster data as a spatial positioning reference. The correction method is to adjust the downstream confluence direction vector of the raster cell to point back to the actual river network raster position, and update the confluence direction raster data. After updating the confluence direction raster data, the updated cumulative contribution area raster data is recalculated.

[0066] After filling depressions, smoothing slopes, calculating confluence direction, calculating cumulative contribution area, and verifying and correcting consistency with the watershed river network data, a hydrological connectivity matrix is ​​obtained to express the relationship between flow direction and water volume contribution within the watershed. Each row or column of the hydrological connectivity matrix represents the spatial location of a grid cell, and each element represents the upstream and downstream connectivity between grid cells, as well as the cumulative contribution area of ​​an upstream grid cell to a downstream grid cell.

[0067] S3: Based on the hydrological connectivity matrix, the ecological product supply grid and the ecological product demand grid are superimposed using path accumulation attenuation to obtain the ecological product spatial flow matrix, including:

[0068] Based on the hydrological connectivity matrix, forest patch distribution data are converted into ecological product supply raster, and ecological product demand raster is constructed based on land use type and population spatial distribution data of the beneficiary area.

[0069] Specifically, forest patch raster data is obtained from the watershed spatial element dataset. Each raster cell in the forest patch raster data has a forest type attribute, including but not limited to coniferous forest, broadleaf forest, mixed forest, and economic forest. Different forest types contribute differently to the supply capacity of ecological products. Therefore, it is necessary to determine the ecological product supply capacity coefficient per unit area for each forest type. The method for determining the ecological product supply capacity coefficient per unit area is as follows: based on forest attribute data such as forest type, tree age, tree species composition, and canopy density, combined with historical monitoring data and ecological benefit assessment data, the coefficient is calculated and weighted using regression analysis or expert scoring methods to obtain the correspondence between forest type and ecological product supply capacity coefficient per unit area. For example, the ecological product supply capacity coefficient per unit area for coniferous forests... The coefficient can be set to 0.75 to 0.95 for broadleaf forests, 0.60 to 0.85 for mixed forests, 0.80 to 1.0 for economic forests, and 0.50 to 0.70 for economic forests. After obtaining the ecological product supply capacity coefficient per unit area, the forest type attribute corresponding to each raster cell in the forest patch raster data is matched with the ecological product supply capacity coefficient per unit area. Combined with the raster cell area, the ecological product supply of each raster cell is obtained by multiplying the area by the ecological product supply capacity coefficient per unit area. This realizes the conversion of forest patch distribution data to ecological product supply raster data, generating ecological product supply raster data. The value of each raster cell in the ecological product supply raster data represents the ecological product supply corresponding to the raster cell.

[0070] Obtain land use type data and population spatial distribution data for the beneficiary area. The land use type data specifies the land use nature of each grid cell, such as urban construction land, agricultural land, industrial land, and ecological protection zone land. The population spatial distribution data is expressed in raster form, showing the population size within each grid cell. Different land use types and population densities have different demands for ecological products; therefore, determine the ecological product demand coefficient per unit area corresponding to each land use type and the ecological product demand coefficient per unit population corresponding to each population density. The determination method is as follows: based on the functional attributes of the land use type, through historical ecological product consumption statistical analysis or expert evaluation, determine the ecological product demand coefficient per unit area corresponding to different land use types. For example, urban construction land can be set to 0.9 to 1.0, agricultural land to 0.5 to 0.7, and industrial land to 0.7. The area ratio can be set to 0.6 to 0.8, and the area ratio for ecological protection zones can be set to 0.2 to 0.4. Simultaneously, based on historical per capita ecological product consumption data for the region, the ecological product demand coefficient per unit population is determined, for example, it can be set to 0.001 to 0.003 units / person. The ecological product demand coefficient per unit area and the ecological product demand coefficient per unit population are matched raster-by-raster with the land use type raster data and the population spatial distribution raster data, respectively. The area of ​​each raster is multiplied by the corresponding ecological product demand coefficient per unit area, and then added to the result calculated by multiplying the population within the raster by the ecological product demand coefficient per unit population, to obtain the ecological product demand for each raster, generating ecological product demand raster data. The value of each raster in the ecological product demand raster data is the ecological product demand corresponding to that raster.

[0071] The ecological product supply grid is accumulated level by level along the downstream path determined by the hydrological connectivity matrix, and the attenuation is calculated in combination with the path length to form a path accumulation attenuation grid.

[0072] Specifically, using raster data of ecological product supply as initial input data, the values ​​in the ecological product supply raster are transmitted downstream level by level according to the downstream confluence path of the hydrological connectivity matrix. During the transmission of ecological products along the path, there is a phenomenon of attenuation with increasing path length; therefore, a path length attenuation function for ecological product transmission needs to be established. The method for determining the path length attenuation function is as follows: the expression form of the path length attenuation function is determined through fitting analysis of historical data of hydrological path length and actual ecological product monitoring quantities. For example, it can be set as an exponential attenuation function: Q_downstream = Q_upstream × exp(-k × L), where Q_downstream represents the remaining amount of ecological product at the downstream location, and Q_upstream represents the ecological product transmitted from the upstream to that location. The quantity is defined as follows: k is the ecological product attenuation coefficient, and L is the ecological product transmission path length. The ecological product attenuation coefficient is determined by fitting historical monitoring data, for example, set between 0.001 and 0.005. The path length attenuation function is applied to the step-by-step transmission process of the ecological product supply grid. That is, when each grid unit transmits to the next grid unit, the ecological product supply transmitted by the upstream grid unit is attenuated according to the path length. This process is repeated step-by-step and accumulated to form the ecological product quantity received by each grid unit after path cumulative attenuation. Finally, path cumulative attenuation grid data is generated. The value of each grid unit in the path cumulative attenuation grid data is the cumulative amount of ecological product transmitted and attenuated along the hydrological path.

[0073] The path cumulative attenuation grid is spatially overlaid and matched with the ecological product demand grid to generate an ecological product spatial flow matrix.

[0074] Specifically, path cumulative attenuation raster data and ecological product demand raster data are spatially overlaid and matched using the same spatial coordinates and raster scale. That is, the path cumulative attenuation value of ecological products in the corresponding raster unit is compared one-to-one with the ecological product demand value. During the overlay matching process, all upstream path cumulative attenuation raster units corresponding to each ecological product demand raster unit are identified. The upstream and downstream relationships between raster units are determined based on the hydrological connectivity matrix, and an ecological product spatial flow matrix is ​​constructed. The rows and columns of the ecological product spatial flow matrix represent ecological product supply raster units and ecological product demand raster units, respectively. Each element represents the amount of ecological product after path cumulative attenuation from the corresponding ecological product supply raster unit to the ecological product demand raster unit through the hydrological connectivity matrix. After spatial overlay matching and matrix construction, an ecological product spatial flow matrix is ​​formed, which expresses the spatial flow direction and transmission amount distribution of ecological products within the watershed from the forest patch supply area to the benefit demand area through hydrological paths.

[0075] S4: Based on the spatial flow matrix of ecological products, combined with the population exposure of the beneficiary area and the weight of the water-using industry, the benefit transfer coefficient is calculated to obtain the benefit transfer matrix, including:

[0076] Based on the spatial flow matrix of ecological products, identify the set of upstream supply source grids corresponding to each benefiting area, and calculate the path contribution value of each supply source grid in the spatial flow matrix of ecological products.

[0077] Specifically, based on the spatial flow matrix of ecological products, the method for identifying all upstream ecological product supply grid units corresponding to each benefit area grid unit in the spatial flow matrix of ecological products, i.e., the upstream supply source grid set, is as follows: For each benefit area grid unit, based on the spatial flow matrix of ecological products, by judging the non-zero nature of the elements in the matrix, all upstream ecological product supply grid units that have been transferred to the benefit area grid unit after the cumulative attenuation of the path are screened, and the screened grid units are determined as the upstream supply source grid set corresponding to the benefit area grid unit;

[0078] The statistical path contribution value based on the spatial flow matrix of ecological products is specifically as follows: For each grid cell in the upstream supply source grid set identified by the grid cell of the beneficiary area, the corresponding element value is read from the spatial flow matrix of ecological products. This value represents the cumulative attenuation of the ecological product path transmitted from the upstream supply source grid cell to the beneficiary area grid cell through the hydrological path. This contribution value is defined as the path contribution value and recorded in the statistical data of each upstream supply source grid cell, thereby forming the statistical data of the upstream supply source grid set and the corresponding path contribution value for each beneficiary area grid cell.

[0079] Spatial matching of path contribution values ​​with spatial distribution data of the population in the beneficiary area is used to form a raster of population exposure distribution in the beneficiary area.

[0080] Specifically, spatial distribution data of the population in the beneficiary area is obtained. This data is in raster format, representing the population distribution within each raster cell. The method for spatially matching the path contribution value with the spatial distribution data of the population in the beneficiary area is as follows: For each raster cell in the beneficiary area, based on the amount of ecological product contribution received by the raster cell in the path contribution value statistics, the path contribution value received by the raster cell is used as a parameter representing the population exposure. The path contribution value of the raster cell is divided by the population size within the raster cell to form the ecological product exposure level per unit population, which is the beneficiary area. The population exposure of the region is calculated as follows: If the population in a raster cell is zero or extremely low, it is adjusted by setting a lower threshold for the population. For example, if the lower threshold is set to 1 person, the calculation is performed based on 1 person when the population in a raster cell is less than 1 person, in order to avoid anomalies caused by dividing the path contribution value by an extremely low population. After spatial matching calculation, the population exposure of the beneficiary area is calculated for each raster cell and summarized to form the population exposure distribution raster data of the beneficiary area. The value of each raster cell in the population exposure distribution raster data of the beneficiary area is the degree of ecological product exposure per unit population.

[0081] The population exposure distribution grid of the beneficiary area is weighted by combining the water industry weights corresponding to the land use type of the beneficiary area to construct the benefit transfer coefficient, and a benefit transfer matrix is ​​generated according to the correspondence between the upstream supply source grid and the beneficiary area.

[0082] Specifically, land use type data for the beneficiary area is obtained, and the land use type corresponding to each raster cell is represented in raster form, such as urban construction land, agricultural land, industrial land, and ecological protection zone land. Different land use types have different water use industry weights, therefore it is necessary to determine the water use industry weight for each land use type. Based on historical water use statistics and land use function attributes, the water use industry weight for each land use type is determined through historical statistical data analysis methods. For example, the water use industry weight for urban construction land is set to 0.9 to 1.0, and the water use industry weight for agricultural land is set to 0.6 to 0. 8. The water use industry weight for industrial land is set at 0.7 to 0.9, and the water use industry weight for ecological protection area land is set at 0.3 to 0.5. The water use industry weight is selected based on the actual situation of the watershed. The raster data of population exposure distribution in the beneficiary area is spatially matched with the water use industry weight corresponding to the land use type. That is, each raster cell is multiplied by the corresponding cell value of the population exposure distribution raster according to the water use industry weight corresponding to the land use type data to obtain the weighted benefit transfer coefficient. The benefit transfer coefficient raster data is formed after weighted calculation, and the value of each raster cell is the weighted benefit transfer coefficient.

[0083] The specific steps for generating a benefit transfer matrix based on the correspondence between upstream supply source grids and benefit transfer coefficient grid data are as follows: A benefit transfer matrix is ​​constructed based on the upstream supply source grid set and benefit transfer coefficient grid data. The rows and columns of the benefit transfer matrix represent ecological product supply grid units and benefit region grid units, respectively. The value of each element represents the actual benefit value of the ecological product supply grid unit provided to the corresponding benefit region grid unit under the weighted benefit transfer coefficient. The matrix construction method is as follows: For each benefit region grid unit, based on the statistically obtained upstream supply source grid set and path contribution value, and combined with the grid unit value corresponding to the benefit transfer coefficient grid data, the product of the path contribution value and the benefit transfer coefficient is used as the value of the matrix element. The matrix element value expresses the actual effective benefit degree of the ecological product supplied by the ecological product supply grid unit to the benefit region grid unit. All ecological product supply grid units and benefit region grid units are calculated to finally form the benefit transfer matrix. Each element of the benefit transfer matrix represents the correspondence between the upstream ecological product supply region and the downstream benefit region regarding the actual benefit of ecological products.

[0084] S5: Construct and solve a collaborative optimization model with consistent constraints on the supply and demand sides using the spatial flow matrix and benefit transfer matrix of ecological products, obtaining a set of spatial allocation schemes, including:

[0085] Using the supply path relationship represented by the ecological product spatial flow matrix and the benefit distribution relationship represented by the benefit transfer matrix as constraint inputs, a collaborative optimization model is constructed that includes the total balance constraint on the supply side and the benefit matching constraint on the demand side.

[0086] Specifically, the goal of constructing the collaborative optimization model is to achieve a spatial balance between the supply and demand sides of ecological products within the watershed, so that the amount of ecological products obtained by each ecological product demand area meets the actual demand, and at the same time, the contribution and benefit of the ecological product supply area to each demand area are matched. The collaborative optimization model includes two constraints: the total supply balance constraint and the demand benefit matching constraint.

[0087] The supply-side aggregate balance constraint is specifically as follows: The available supply of ecological products in each grid cell of the ecological product supply grid data is used as the upper limit, and the actual allocation of ecological products corresponding to the grid cell is set as the decision variable; the available supply of ecological products in any grid cell is used as the upper limit constraint condition for the decision variable, expressed as: ;in, The actual allocation of ecological products for the i-th ecological product supply grid cell (i.e., the amount of ecological products actually allocated to each grid cell), in units of ecological product quantity; The maximum available supply of ecological products for the i-th ecological product supply grid cell is directly given by the ecological product supply grid data determined in step S3. For example, if the maximum available supply of ecological products for the grid cell is 100 units, then the corresponding decision variable satisfies the constraints. .

[0088] The demand-side benefit matching constraint is as follows: For each benefiting region grid cell, the actual benefit is determined by the proportion of contribution of the supply grid cell to the ecological products of the benefiting grid cell, represented by each element in the benefit transfer matrix. That is, the actual benefit of each benefiting region grid cell should not be less than a given proportion of the demand for ecological products of the grid cell. The expression is: ;in, The actual allocation of ecological products for the i-th ecological product supply grid cell (i.e., the amount of ecological products actually allocated to each grid cell). The benefit transfer coefficient of the i-th supply grid cell to the j-th benefit grid cell in the benefit transfer matrix; The ecological product demand of the j-th beneficiary area grid cell is given by the ecological product demand grid data; To match the ratio threshold, it is determined based on historical experience in allocating ecological products in the watershed, with a range of 0.8 to 1.0. For example, a value of 0.9 means that the actual benefit from ecological products meets at least 90% of the ecological product demand in the benefited area. The number of grid cells supplying all ecological products.

[0089] In the collaborative optimization model, a mapping relationship is established between the decision variables corresponding to the ecological product supply grid and the benefit transfer coefficients in the benefit transfer matrix, forming a consistent constraint expression between the supply and demand sides.

[0090] Specifically, based on the corresponding positional elements of the spatial flow matrix and benefit transfer matrix of ecological products, the effective benefit of ecological products is expressed by multiplying the decision variables of each supply grid unit by the corresponding benefit transfer coefficient: the effective benefit of the i-th supply grid unit to the j-th benefit grid unit is... By traversing all supply grid cells and benefit area grid cells, the ecological product benefit mapping relationship between each grid cell is determined, thereby forming a consistent constraint expression between the supply and demand sides: Where n is the number of grid cells in the entire ecological product benefit area.

[0091] The collaborative optimization model is solved iteratively to generate a set of spatial configuration schemes that satisfy the consistency constraints.

[0092] Specifically, using the maximum available supply of ecological products provided by the ecological product supply raster data as the initial decision variable, the objective function for solving the collaborative optimization model is determined to be the function that minimizes the difference between the actual ecological product benefit and demand in the beneficiary area, expressed as:

[0093] .

[0094] Based on the above objective function and constraints, the iterative solution algorithm is selected as follows: If the scale of the ecological product supply raster data is small and the constraints are all linear, then the simplex method or interior point method is selected for linear programming calculation; if the data scale is large or there are nonlinear expressions, then a heuristic algorithm, such as particle swarm optimization or genetic algorithm, is selected for nonlinear iterative solution. Taking particle swarm optimization as an example, the particle swarm size is determined according to the scale of the raster data, and the particle size ranges from 30 to 100 particles. The initial position of each particle represents a set of decision variable solution vectors; the update parameters of particle velocity and position, such as inertia factor, learning factor and neighborhood optimal factor, are determined through sensitivity analysis or experience. For example, the inertia factor is 0.4 to 0.9 and the learning factor is 1.5 to 2.5.

[0095] The termination convergence condition of the iterative algorithm is determined as follows: the absolute value of the change in the objective function value between two consecutive iterations is less than a given iterative convergence accuracy threshold. The method for determining the convergence accuracy threshold is to determine it based on historical model solving experience or watershed management requirements, with a value range of 0.001 to 0.01, for example, 0.005.

[0096] After iterative solution, the actual ecological product allocation decision variable value for each ecological product supply grid cell is determined, and the actual ecological product amount obtained by the corresponding benefit area grid cell is determined based on the benefit transfer matrix, thus forming a spatial allocation scheme that satisfies the consistency constraint. By repeatedly adjusting the initial solution vector or optimizing the algorithm parameters, the solution process is repeated to form multiple spatial allocation schemes, and finally a set of spatial allocation schemes that satisfy the consistency constraint of watershed ecological product supply and demand is generated.

[0097] S6: Perform upstream and downstream cost-sharing calculations and cross-regional quota allocation on the spatial configuration scheme set to obtain spatial configuration and allocation schemes, including:

[0098] Based on the spatial configuration scheme set, determine the grid set of ecological product supply sources corresponding to each beneficiary area;

[0099] Specifically, based on the decision variable values ​​of the actual ecological product allocation quantity for each spatial configuration scheme in the spatial configuration scheme set for ecological product supply grid units, and combined with the upstream and downstream spatial transmission relationships determined by the ecological product spatial flow matrix, by traversing all beneficiary area grid units and all supply grid units, it is determined whether each beneficiary area grid unit has an ecological product path contribution from each ecological product supply grid unit. The determination method is to check the ecological product path transmission quantity element value from the corresponding supply grid unit to the beneficiary area grid unit in the ecological product spatial flow matrix for each beneficiary area grid unit one by one. If the ecological product path transmission quantity is greater than zero and the decision variable value of the corresponding ecological product supply grid unit is greater than zero, it is determined that the corresponding ecological product supply grid unit is one of the ecological product supply sources of the beneficiary area grid unit. By traversing each spatial configuration scheme in the spatial configuration scheme set, all valid ecological product supply source grid units are identified for each beneficiary area grid unit, forming the ecological product supply source grid set corresponding to the beneficiary area grid unit.

[0100] By combining the path contribution value determined by the ecological product spatial flow matrix and the benefit transfer coefficient determined by the benefit transfer matrix, the ecological product supply responsibility ratio between each benefit region and the corresponding supply source grid is calculated.

[0101] Specifically, based on the path contribution value determined by the ecological product spatial flow matrix and the benefit transfer coefficient determined by the benefit transfer matrix, the responsibility ratio of each supply source grid unit to the ecological product demand of the corresponding benefit area grid unit is calculated. The calculation method is as follows: For each benefit area grid unit, based on the ecological product spatial flow matrix, the ecological product path contribution value of all corresponding ecological product supply source grid units is determined; the ecological product path contribution value is determined based on the product of the actual ecological product quantity allocated to each supply grid unit and the path cumulative attenuation coefficient; the benefit transfer coefficient of each ecological product supply grid unit to the benefit area grid unit is extracted from the benefit transfer matrix, and the benefit transfer coefficient represents the actual effective contribution ratio of the ecological product after it is transferred from the corresponding supply source grid unit to the benefit area grid unit; the responsibility ratio of each ecological product supply source grid unit to the corresponding benefit area grid unit is calculated by multiplying the ecological product path contribution value and the benefit transfer coefficient. Actual effective contribution: The actual effective contribution of all ecological product supply source grid units in the beneficiary area grid unit is summarized to obtain the total amount of ecological products actually obtained by the beneficiary area grid unit; the actual effective contribution of each ecological product supply source grid unit to the beneficiary area grid unit is divided by the total amount of ecological products actually obtained by the beneficiary area grid unit to calculate the ecological product supply responsibility ratio of each ecological product supply source grid unit to the corresponding beneficiary area grid unit; for example, if the total amount of ecological products actually obtained by the beneficiary area grid unit is 100 units, and the actual effective contribution of a certain supply source grid unit is 25 units, then the corresponding ecological product supply responsibility ratio is 25 / 100, that is, 0.25.

[0102] Based on the proportion of ecological product supply responsibility, upstream and downstream sharing is calculated, and cross-regional quota allocation is carried out based on the calculation results to form a spatial configuration and allocation scheme.

[0103] Specifically, the upstream and downstream sharing of ecological product supply responsibility is calculated based on the ecological product supply responsibility ratio. This involves: for multiple beneficiary grid units and ecological product supply source grid units in the upstream and downstream relationship of the watershed, the ecological product supply responsibility between upstream and downstream is calculated according to the actual effective contribution relationship determined by the ecological product supply responsibility ratio. The calculation process is as follows: determine the total ecological product demand of each beneficiary grid unit within the watershed, i.e., the sum of the ecological product demand of the beneficiary areas provided by the ecological product demand grid data; based on the ecological product supply responsibility ratio, calculate the total actual effective supply responsibility of each ecological product supply source grid unit, and sum the actual effective contributions of each supply source grid unit to all beneficiary grid units to obtain the total ecological product supply responsibility of each supply source grid unit; verify the consistency between the sum of the supply responsibility of all ecological product supply source grid units and the total ecological product demand of all beneficiary areas, ensuring consistency within the allowable error range; if a deviation occurs, the ecological product supply responsibility ratio needs to be recalculated or the decision variable configuration needs to be adjusted until the consistency requirement is met.

[0104] Cross-regional quota allocation is based on the accounting results, specifically as follows: Based on the actual effective supply responsibility of each ecological product supply source grid unit obtained from the ecological product supply responsibility accounting, cross-regional ecological product quota allocation is carried out according to the administrative region where the grid unit is located. The allocation method is as follows: The administrative region unit where each ecological product supply source grid unit is located is determined, and the correspondence is established by overlaying the administrative region vector data with the spatial location of the ecological product supply source grid unit; the actual effective supply responsibility of ecological products of multiple ecological product supply source grid units belonging to the same administrative region is accumulated to obtain the total ecological product supply quota of the administrative region; the total ecological product supply quota of the administrative region is then allocated... The ecological product supply quota is compared with the ecological product demand of all beneficiary areas within the administrative region. If the ecological product supply quota of an administrative region is higher than the demand of its own beneficiary areas, the administrative region is an ecological product supply surplus area; if it is lower than the demand of the beneficiary areas, it is an ecological product supply deficit area. Through the cross-regional ecological product quota coordination mechanism, an ecological product supply quota allocation plan is formed according to the principle of allocating ecological product supply quotas from ecological product surplus areas to ecological product deficit areas. The cross-regional ecological product quota coordination mechanism includes consultation and cooperation mechanisms between administrative regions, such as signing ecological compensation agreements between administrative regions within a river basin to implement quota allocation.

[0105] Based on the above-determined ecological product supply responsibility ratio calculation results and the cross-regional ecological product supply quota allocation scheme, a spatial allocation and distribution scheme is formed. The spatial allocation and distribution scheme includes: the spatial distribution of the actual ecological product allocation of grid units on the ecological product supply side, the spatial distribution of the actual ecological product amount obtained by grid units in the beneficiary area, and the quantity and direction of cross-regional allocation of ecological product supply quotas between administrative regions. The spatial allocation and distribution scheme can accurately express the ecological product supply and demand relationship between the ecological product supply side and the demand side within the watershed.

[0106] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0107] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in 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. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0108] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0109] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0110] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0111] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0112] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0113] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations 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. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0114] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for synergistic optimization of watershed forest ecological products, characterized in that, Includes the following steps: S1: Obtain watershed digital elevation data, river network data, and forest patch distribution data, and then uniformly project and rasterize them to obtain a watershed spatial element dataset; S2: Calculate the confluence direction and cumulative contribution area based on the watershed spatial element dataset, and construct a hydrological connectivity matrix; S3: Based on the hydrological connectivity matrix, the ecological product supply grid and the ecological product demand grid are superimposed with path accumulation and attenuation to obtain the ecological product spatial flow matrix. S4: Calculate the benefit transfer coefficient based on the spatial flow matrix of ecological products, combined with the population exposure of the beneficiary area and the weight of the water-using industry, to obtain the benefit transfer matrix; S5: Construct a collaborative optimization model with consistent constraints on the supply and demand sides using the spatial flow matrix of ecological products and the benefit transfer matrix, and solve it to obtain a set of spatial configuration schemes; S6: Perform upstream and downstream cost-sharing calculations and cross-regional quota allocations on the spatial configuration scheme set to obtain spatial configuration and allocation schemes.

2. The method for collaborative optimization of watershed forest ecological products according to claim 1, characterized in that, S1, specifically: The watershed boundary range is determined by the administrative boundary of the watershed to obtain watershed digital elevation data, watershed river network data is generated based on the watershed digital elevation data, and forest patch distribution data is determined by combining forest resource survey data; The watershed digital elevation data, watershed river network data, and forest patch distribution data are projected with unified coordinates and rasterized according to a set raster scale to generate a watershed spatial element dataset.

3. The method for collaborative optimization of watershed forest ecological products according to claim 2, characterized in that, S2, specifically: Based on the watershed spatial element dataset, depression filling and slope smoothing are performed on the watershed digital elevation data. The downstream confluence direction pointed to by each grid is calculated based on the processed watershed digital elevation data, and a confluence direction grid is generated. Based on the confluence direction grid, the upstream inflow relationship is accumulated and summarized to form a cumulative contribution area grid; Consistency checks are performed between the cumulative contribution area raster and the watershed river network data, and rasteres that deviate from the confluence direction are corrected to generate a hydrological connectivity matrix.

4. The method for collaborative optimization of watershed forest ecological products according to claim 3, characterized in that, S3, specifically: Based on the hydrological connectivity matrix, forest patch distribution data are converted into ecological product supply raster, and ecological product demand raster is constructed based on land use type and population spatial distribution data of the beneficiary area. The ecological product supply grid is accumulated level by level along the downstream path determined by the hydrological connectivity matrix, and the attenuation is calculated in combination with the path length to form a path accumulation attenuation grid. By spatially overlaying and matching the path cumulative attenuation grid with the ecological product demand grid, a spatial flow matrix of ecological products is generated.

5. The method for collaborative optimization of watershed forest ecological products according to claim 4, characterized in that, S4, specifically: Based on the spatial flow matrix of ecological products, identify the set of upstream supply source grids corresponding to each benefiting area, and calculate the path contribution value of each supply source grid in the spatial flow matrix of ecological products. Spatial matching of path contribution values ​​with spatial distribution data of the population in the beneficiary area is used to form a raster of population exposure distribution in the beneficiary area. By combining the water use industry weights corresponding to the land use types in the beneficiary areas, the population exposure distribution grid of the beneficiary areas is weighted and calculated to construct the benefit transfer coefficient. Then, a benefit transfer matrix is ​​generated according to the correspondence between the upstream supply source grid and the beneficiary areas.

6. The method for collaborative optimization of watershed forest ecological products according to claim 5, characterized in that, S5, specifically: Using the supply path relationship represented by the ecological product spatial flow matrix and the benefit distribution relationship represented by the benefit transfer matrix as constraint inputs, a collaborative optimization model is constructed that includes the total balance constraint on the supply side and the benefit matching constraint on the demand side. In the collaborative optimization model, a mapping relationship is established between the decision variables corresponding to the ecological product supply grid and the benefit transfer coefficients in the benefit transfer matrix, forming a consistent constraint expression between the supply and demand sides. The collaborative optimization model is solved iteratively to generate a set of spatial configuration schemes that satisfy the consistency constraints.

7. The method for collaborative optimization of watershed forest ecological products according to claim 6, characterized in that, S6, specifically: Based on the spatial configuration scheme set, determine the grid set of ecological product supply sources corresponding to each beneficiary area; By combining the path contribution value determined by the ecological product spatial flow matrix and the benefit transfer coefficient determined by the benefit transfer matrix, the ecological product supply responsibility ratio between each benefit region and the corresponding supply source grid is calculated. Based on the proportion of responsibility for the supply of ecological products, upstream and downstream sharing is calculated, and cross-regional quota allocation is carried out based on the calculation results to form a spatial configuration and allocation scheme.

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