Stable and suitable coordination method, equipment, medium and product for watershed mountain, water, forest, lake, grass and sand

Abstract

The invention discloses a stable and suitable coordination method, equipment, medium and product for mountain, water, forest, lake, grass and sand in a watershed, and relates to the field of ecological hydrological protection and governance, and the method comprises the following steps: carrying out grid processing on a research area, dividing the watershed into a plurality of research units, and according to watershed basic data corresponding to each research unit, calculating the watershed basic data corresponding to each research unit; a multi-objective optimization model for the inland river basin is constructed by taking ecological optimization and hydrological function balance optimization as objectives, grain and livestock yield maximization as objectives, water resource distribution efficiency optimization as objectives, environmental restoration cost minimization as objectives, social change minimization as objectives and environmental risk minimization as objectives; and solving the model through a hybrid algorithm combining a genetic algorithm and a particle swarm optimization algorithm to obtain a stable and suitable coordination result of the watershed mountain, water, forest, lake, grass and sand. According to the method, overall optimization and dynamic coordination of multiple elements on the watershed scale can be achieved, and the long-term stability and suitability requirements of a complex watershed ecological system are met.

Description

Technical Field

[0001] The present application relates to the field of eco-hydrological protection and management technology, and in particular to a method, equipment, medium and product for stabilizing and appropriately coordinating the distribution of mountains, waters, forests, fields, lakes, grasslands and sands in a watershed. Background Art

[0002] Existing ecological protection and governance technologies typically target single ecosystems, such as wetlands, forests, and grasslands, lacking a comprehensive, holistic, and systematic approach to watershed management. This decentralized governance model struggles to coordinate the dynamic balance among multiple elements within a watershed, including mountains, water, forests, fields, lakes, grasslands, and sand. This results in limited improvements in ecosystem function and low resource utilization efficiency.

[0003] In the cold and arid northern regions, the dual pressures of water scarcity and a fragile ecological environment complicate the interplay between multiple factors within the basin. Water shortages limit vegetation recovery, while vegetation degradation exacerbates soil erosion and river siltation, further weakening regional ecological functions and soil and water conservation capacity. Existing technologies are unable to achieve a stable and optimal coordination of these multiple factors in terms of spatial distribution and functional effects.

[0004] In recent years, although the integrated management of mountains, rivers, forests, farmlands, lakes, grasslands and deserts has achieved certain results, these technical means are mostly concentrated on local management, failing to achieve overall optimization and dynamic coordination of multiple factors at the basin scale, and it is difficult to meet the long-term stability and suitability needs of complex basin ecosystems. Summary of the Invention

[0005] The purpose of this application is to provide a method, equipment, medium and product for the stable and suitable coordination of mountains, rivers, forests, fields, lakes, grasslands and sand in a watershed, which can achieve overall optimization and dynamic coordination of multiple factors at the watershed scale, and meet the long-term stability and suitability requirements of complex watershed ecosystems.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a method for stabilizing and appropriately coordinating the distribution of mountains, waters, forests, farmlands, lakes, grasslands, and sand in a watershed, the method comprising:

[0008] The study area is gridded to divide the watershed into multiple study units; the study area is an area containing the watershed and its components; the watershed components include mountains, water, forests, fields, lakes, grass, and sand;

[0009] For each of the research units, basic watershed data corresponding to the research unit is obtained, whereby basic watershed data corresponding to each of the research units is obtained; the basic watershed data is data related to the components of the watershed; the basic watershed data includes topographic data, hydrological and meteorological data, ecological factor data, water demand data, and industrial and population distribution data;

[0010] Based on the basic watershed data corresponding to each research unit, a multi-objective optimization model for inland river basins is constructed with the goals of optimal ecological and hydrological function trade-offs, maximizing grain and livestock production, optimizing water resource allocation efficiency, minimizing environmental restoration costs, minimizing social changes, and minimizing environmental risks. The multi-objective optimization model for inland river basins is a nonlinear multi-objective dynamic programming model based on the benign interactive relationship between ecology and hydrology.

[0011] By combining a hybrid algorithm of genetic algorithm and particle swarm optimization algorithm, the multi-objective optimization model for inland river basins is solved to obtain a stable and suitable coordination result of mountains, rivers, forests, farmlands, lakes, grasslands and sands in the basin; the stable and suitable coordination result of mountains, rivers, forests, farmlands, lakes, grasslands and sands in the basin includes the optimal land use allocation result, the optimal water resources allocation result and the optimal land area allocation result.

[0012] Optionally, the gridding process is performed on the study area to divide the watershed into multiple study units, specifically including:

[0013] Based on remote sensing images and GIS technology, the watershed is divided into regular or irregular grids according to spatial characteristics to obtain n research units; the i-th research unit corresponds to the i-th region; i is the research unit number, i = 1, 2,…, n; n is the total number of research units.

[0014] Optionally, the topographic data includes watershed elevation data, river course direction data, slope distribution data, and land use type data;

[0015] The hydrological and meteorological data include precipitation data, runoff data, evaporation data, temperature data and groundwater level data;

[0016] The ecological factor data include wetland area data, biodiversity data, vegetation coverage data and land salinization degree data;

[0017] The water demand data includes the current data of agricultural water use, animal husbandry water use, industrial water use, and residential water use; the current data of agricultural water use includes the types, area, and yield of irrigated crops; the current data of animal husbandry water use includes the scale of breeding and water consumption; the current data of industrial water use includes production water consumption, wastewater discharge, and water resource reuse rate; the current data of residential water use includes per capita daily water consumption, water supply coverage, and seasonal water use changes;

[0018] The industry and population distribution data include population size, migration trends and industry layout data within the research unit.

[0019] Optionally, the decision variables of the multi-objective optimization model for inland river basins include land use allocation variables, water resource allocation variables, and land area allocation variables;

[0020] The land use allocation variable is expressed as: X ij |i=1,2,…,n;j=1,2,…,7;The water resource allocation variable is expressed as: W ij |i=1,2,…,n;j=1,2,…,7;the land area allocation variable is expressed as: A ij |i=1,2,…,n;j=1,2,…,7;where X ij Indicates whether the i-th area is allocated to the j-th use, X ij =1 means allocation, X ij =0 means no allocation, j=1,2,3,4,5,6,7 corresponds to the seven uses of mountain, water, forest, field, lake, grass, and sand; W ij A represents the amount of water resources allocated to the jth purpose in the i-th region; ij represents the land area allocated to the jth use in the i-th region.

[0021] Optionally, the objective function of the multi-objective optimization model for inland river basins is expressed as: OptF = [MaxOb, MaxProd, MaxWQ, MinCOST, MinCH, MinEN]; wherein OptF represents the objective function of the multi-objective optimization model for inland river basins, MaxOb represents the goal of optimal balance between ecological optimization and hydrological functions, MaxProd represents the goal of maximizing grain and livestock production, MaxWQ represents the goal of optimizing water resource allocation efficiency, MinCOST represents the goal of minimizing environmental restoration costs, MinCH represents the goal of minimizing social changes, and MinEN represents the goal of minimizing environmental risks.

[0022] Optionally, the constraints of the multi-objective optimization model for inland river basins include use uniqueness constraints, suitability constraints, water resource constraints, area constraints, irrigation water volume constraints, canal water delivery efficiency constraints, water balance constraints, hydrological function target constraints, ecological function target constraints, and surface water and groundwater availability constraints.

[0023] Optionally, the water resource constraints include research unit water resource allocation constraints, health water resource demand lower limit constraints, and basin total water resource balance constraints;

[0024] The area constraints include regional total area constraints, land allocation suitability constraints, suitable climate zone area constraints, minimum protection area constraints and land use classification balance constraints.

[0025] In the second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and runnable on the processor, wherein the processor executes the computer program to implement any of the above-mentioned methods for stable and suitable coordination of mountains, waters, forests, farmlands, lakes, grasslands, and sands in a watershed.

[0026] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-mentioned methods for stable and suitable coordination of mountains, waters, forests, farmlands, lakes, grasslands and sands in a watershed.

[0027] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned methods for stable and suitable coordination of mountains, waters, forests, farmlands, lakes, grasslands and sands in a watershed.

[0028] According to the specific embodiments provided in this application, this application has the following technical effects:

[0029] The present application provides a method, equipment, medium and product for the stable and appropriate coordination of mountains, waters, forests, fields, lakes, grasses and sands in a watershed. By gridding the entire study area including the watershed and its internal mountains, waters, forests, fields, lakes, grasses, sands and other elements, multiple research units are obtained. For each research unit, its corresponding topography, hydrological and meteorological data, ecological factors, water demand, and industrial and population distribution data are obtained. These natural and socio-economic basic data related to mountains, waters, forests, fields, lakes, grasses, sands and other elements are used to establish a nonlinear-multi-objective dynamic programming model based on the benign interactive relationship of eco-hydrology with the goal of optimizing eco-hydrological functions, improving economic and social benefits and maximizing resource and environmental utilization. The multi-objective optimization method is used to coordinate the ecological elements such as mountains, waters, forests, fields, lakes, grasses and sands. Functional allocation and dynamic coordination are achieved to realize data collection, analysis and optimal configuration of multiple factors within each research unit. Since the calculation results of each research unit not only take into account the hydrological characteristics of the basin, but also comprehensively consider the spatial distribution and functional coordination of other ecological factors, the final stable and suitable coordination results of the basin's mountains, rivers, forests, fields, lakes, grasslands and sands are obtained through the optimal configuration of all grid units. This is based on a comprehensive analysis of the entire study area, which can realize the overall optimization and dynamic coordination of multiple factors at the basin scale. From the perspective of spatial distribution and functional coordination, it breaks through the existing technical bottlenecks, realizes the improvement of basin ecosystem functions and maximization of resource benefits, provides technical support for regional ecological security and sustainable development, and can meet the long-term stability and suitability needs of complex basin ecosystems. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0031] Figure 1 A schematic flow chart of a method for stabilizing and appropriately coordinating the distribution of mountains, waters, forests, farmlands, lakes, grasslands, and sand in a watershed provided in one embodiment of the present application;

[0032] Figure 2 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0034] The purpose of this application is to provide a method, equipment, medium and product for the stable and suitable coordination of mountains, rivers, forests, fields, lakes, grasslands and sand in a watershed, which can achieve overall optimization and dynamic coordination of multiple factors at the watershed scale, and meet the long-term stability and suitability requirements of complex watershed ecosystems.

[0035] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0036] like Figure 1 As shown, the present application provides a method for stabilizing and appropriately coordinating the distribution of mountains, waters, forests, farmlands, lakes, grasslands, and sand in a watershed, including:

[0037] Step 101: Grid the study area and divide the watershed into multiple study units; the study area is an area that includes the watershed and its components; the watershed components include mountains, water, forests, fields, lakes, grass, and sand.

[0038] The step 101 specifically includes:

[0039] Based on remote sensing images and GIS technology, the watershed is divided into regular or irregular grids according to spatial characteristics to obtain n research units; the i-th research unit corresponds to the i-th region; i is the research unit number, i = 1, 2,…, n; n is the total number of research units.

[0040] Step 102: For each research unit, obtain the basic data of the watershed corresponding to the research unit, and obtain the basic data of the watershed corresponding to each research unit; the basic data of the watershed is data related to the components of the watershed; the basic data of the watershed includes topographic data, hydrological and meteorological data, ecological factor data, water demand data, and industry and population distribution data.

[0041] In step 102, the topographic data includes watershed elevation data, river direction data, slope distribution data, and land use type data.

[0042] Hydrometeorological data include precipitation data, runoff data, evaporation data, temperature data and groundwater level data.

[0043] Ecological factor data include wetland area data, biodiversity data, vegetation coverage data and land salinization data.

[0044] Water demand data include current data on agricultural water use, animal husbandry water use, industrial water use and residential water use; current data on agricultural water use include the types, area and yield of irrigated crops; current data on animal husbandry water use include breeding scale and water consumption; current data on industrial water use include production water consumption, wastewater discharge and water resource reuse rate; current data on residential water use include per capita daily water consumption, water supply coverage and seasonal water use changes.

[0045] Industrial and population distribution data include population size, migration trends, and industrial layout data within the research unit.

[0046] Step 103: Based on the basic data of the watershed corresponding to each research unit, a multi-objective optimization model for inland river basins is constructed with the goals of optimal ecological and hydrological function trade-offs, maximizing grain and livestock production, optimizing water resource allocation efficiency, minimizing environmental restoration costs, minimizing social changes, and minimizing environmental risks. The multi-objective optimization model for inland river basins is a nonlinear multi-objective dynamic programming model based on the benign interactive relationship between ecology and hydrology.

[0047] In step 103, the decision variables of the multi-objective optimization model for inland river basins include land use allocation variables, water resource allocation variables, and land area allocation variables.

[0048] The land use allocation variable is expressed as: X ij |i=1,2,…,n;j=1,2,…,7;water resource allocation variable is expressed as: W ij |i=1,2,…,n;j=1,2,…,7;The land area allocation variable is expressed as: A ij |i=1,2,…,n;j=1,2,…,7;where X ij Indicates whether the i-th area is allocated to the j-th use, X ij =1 means allocation, X ij =0 means no allocation, j=1,2,3,4,5,6,7 corresponds to the seven uses of mountain, water, forest, field, lake, grass, and sand; W ij A represents the amount of water resources allocated to the jth purpose in the i-th region; ij represents the land area allocated to the jth use in the i-th region.

[0049] The objective function of the multi-objective optimization model for inland river basins is expressed as: OptF = [MaxOb, MaxProd, MaxWQ, MinCOST, MinCH, MinEN]; where OptF represents the objective function of the multi-objective optimization model for inland river basins, MaxOb represents the goal of optimizing the balance between ecological optimization and hydrological functions, MaxProd represents the goal of maximizing grain and livestock production, MaxWQ represents the goal of optimizing water resource allocation efficiency, MinCOST represents the goal of minimizing environmental restoration costs, MinCH represents the goal of minimizing social changes, and MinEN represents the goal of minimizing environmental risks.

[0050] The constraints of the multi-objective optimization model for inland river basins include use uniqueness constraints, suitability constraints, water resource constraints, area constraints, irrigation water volume constraints, canal water delivery efficiency constraints, water balance constraints, hydrological function target constraints, ecological function target constraints, and surface water and groundwater availability constraints.

[0051] Among them, water resource constraints include water resource allocation constraints of research units, lower limit constraints on healthy water resource demand, and total water resource balance constraints in the basin.

[0052] Area constraints include regional total area constraints, land allocation suitability constraints, suitable climate zone area constraints, minimum protection area constraints and land use classification balance constraints.

[0053] Step 104: A multi-objective optimization model for inland river basins is solved by combining a hybrid algorithm of a genetic algorithm and a particle swarm optimization algorithm to obtain a stable and suitable coordination result of mountains, rivers, forests, farmlands, lakes, grasslands, and sand in the basin; the stable and suitable coordination result of mountains, rivers, forests, farmlands, lakes, grasslands, and sand in the basin includes an optimal land use allocation result, an optimal water resource allocation result, and an optimal land area allocation result.

[0054] The technical solution of this application is described below with a specific embodiment:

[0055] The method for stable and appropriate coordination of mountains, waters, forests, farmlands, lakes, grasses, and sands in a watershed provided by this application is a stable and appropriate coordination technology that takes water resources as the core line and coordinates multiple elements (i.e., mountains, waters, forests, farmlands, lakes, grasses, sands, etc.) in the watershed. This technology breaks through the bottleneck of existing technologies from the perspective of spatial distribution and functional synergy, achieves functional improvement of the watershed ecosystem and maximization of resource benefits, and provides technical support for regional ecological security and sustainable development. This application constructs a nonlinear-multi-objective dynamic programming model based on the benign interactive relationship between eco-hydrology, combines constraints such as water resources, land area, socio-economic needs, hydrological functions, and ecological functions, with the goal of optimizing eco-hydrological functions, improving economic and social benefits, and maximizing resource and environmental utilization. Through a multi-objective optimization method, it coordinates the functional allocation and dynamic coordination of ecological elements such as mountains, waters, forests, farmlands, lakes, grasses, and sands, fully reflecting the high correlation between natural and human factors. This application aims to achieve a stable and appropriate coordination of mountain, water, forest, field, lake, grass and sand elements, integrate and construct ecological protection, construction and governance technologies with the characteristics of a living community, and provide a systematic solution for the sustainable development of the basin.

[0056] The method for stabilizing and appropriately coordinating the watershed's mountains, waters, forests, farmlands, lakes, grasslands, and sands provided in this application includes the following steps (1) to (4):

[0057] (1) Basin grid processing:

[0058] The study area is gridded, and the basin is divided into n study units. Based on remote sensing imagery and GIS technology, the basin is divided into regular grids (e.g., 1 km × 1 km) or irregular grids (e.g., based on topographic units) according to spatial characteristics. Each grid unit (i.e., cell) is numbered i.

[0059] Among them, the study area refers to the area containing the watershed and its constituent elements (mountains, water, forests, fields, lakes, grass, and sand). The study area is gridded, and the watershed is divided into n research units (i.e., grid units). It is not divided and calculated only for the watershed part, but the entire study area (including the watershed and its internal mountains, water, forests, fields, lakes, grass, sand and other elements) is gridded. Through this gridding process, the present application can collect data, analyze and optimize the configuration of multiple elements in each grid unit. The calculation results of each grid unit not only take into account the hydrological characteristics of the watershed, but also comprehensively consider the spatial distribution and functional synergy of other ecological elements. Therefore, through the optimal configuration of all grid units, the final stable and suitable coordination results of the watershed mountains, waters, forests, fields, lakes, grasses and sands are based on a comprehensive analysis of the entire study area, rather than just for the watershed part.

[0060] (2) Collection of basic data of the watershed:

[0061] For each research unit (i), we collected basic natural and socioeconomic data related to elements such as mountains, watersheds, forests, farmlands, lakes, grasslands, and sand to construct a dataset. This data included: watershed elevation, river course, slope distribution, land use type, precipitation, runoff, evaporation, temperature, groundwater level, wetland area, biodiversity, vegetation cover, land salinization, current water use in agriculture, animal husbandry, industry, and residential areas, and industry and population distribution.

[0062] (1) Acquisition of natural and ecological data of the watershed:

[0063] ① Topography: Using high-resolution remote sensing images and GIS technology, we collected data on watershed elevation, river direction, slope distribution, and land use type, which are recorded as a set {D geo}.

[0064] ② Hydrometeorology: Collect meteorological and hydrological data such as precipitation, runoff, evaporation, temperature, groundwater level, etc. measured or observed over many years, and record them as a set {D hydro}.

[0065] ③ Ecological factors: Investigate wetland area, biodiversity, vegetation coverage, land salinization, etc., combine ground observation and remote sensing data to form an ecological factor data set, which is recorded as set {D eco}.

[0066] (2) Socioeconomic status survey:

[0067] ① Water demand: Obtain the current water use status of agriculture (irrigated crop types, area, and yield), animal husbandry (breeding scale and water consumption), industry (production water consumption, wastewater discharge, and water resource reuse rate), and residents’ daily life (per capita daily water consumption, water supply coverage, and seasonal water use changes), recorded as a set {D soc}.

[0068] ②Industry and population distribution: Investigate the population size, migration trends and industrial layout within the research unit for use in subsequent social change cost estimation.

[0069] (3) Constructing a nonlinear multi-objective dynamic programming model (i.e., a nonlinear multi-objective dynamic programming model based on the benign interaction between ecology and hydrology):

[0070] Based on the above investigation and analysis, a multi-objective optimization model for inland river basins is established, that is, based on the natural and socio-economic basic data related to elements such as mountains, water, forests, fields, lakes, grass, and sand obtained in step (2), a multi-objective optimization model for inland river basins is established.

[0071] (1) Define decision variables:

[0072] ① Land use allocation variable: X ij |i=1,2,…,n;j=1,2,…,7,indicates whether the i-th area (i.e., the i-th research unit) is allocated to the j-th purpose (i.e., the j-th purpose), X ij =1 means allocation, X ij =0 means no allocation, j=1,2,…,7 corresponds to the seven uses (i.e. land types): mountain, water, forest, field, lake, grass, and sand.

[0073] ② Water resource allocation variable: W ij , which represents the amount of water resources allocated to use j in region i.

[0074] ③Land area allocation variable: A ij , represents the land area allocated to use j in region i.

[0075] (2) Objective function:

[0076] OptF=[MaxOb,MaxProd,MaxWQ,MinCOST,MinCH,MinEN] (1)

[0077] ① The goal is to achieve the best balance between ecological and hydrological functions (i.e., the best balance between ecological and hydrological functions):

[0078]

[0079] Among them, α k represents the weight coefficient of k hydrological function indicators; α l Represents the weight coefficient of l ecological function indicators; Hy k represents k hydrological function indicators (groundwater level, runoff, precipitation, etc.); Ec l represents an ecological function indicator (vegetation coverage, biodiversity, primary productivity, etc.); X ij is a binary variable indicating whether plot i (i.e., area i) is allocated to use j. ij =1 means allocation, X ij =0 means no allocation.

[0080] ② Aiming to maximize grain and livestock production:

[0081]

[0082] Among them, gr ij represents the food production allocated to the jth land type (i.e., the jth land use) in the i-th region; sc ij represents the amount of livestock raised in the i-th region allocated to the j-th land type.

[0083] ③ Aiming at optimizing water resource allocation efficiency (i.e., achieving optimal water resource allocation efficiency):

[0084]

[0085] Among them, wq ij represents the water resource utilization efficiency allocated to the jth land type in the i-th region.

[0086] ④ Taking the minimum environmental restoration cost (i.e. minimization of environmental restoration cost) as the goal:

[0087]

[0088] q min ≤q ijw ≤q max (7)

[0089] Among them, co ijw represents the unit cost of the wth item of environmental restoration allocated to the jth land type in the i-th region; environmental restoration includes soil restoration, water restoration, vegetation restoration, wetland restoration, sand management, ecological corridor construction, etc. The specific type and scale of restoration measures will be adjusted and optimized according to the actual situation of each research unit (grid unit). Environmental restoration may be diverse, and the specific type and scale will vary according to different regions and land types; q ijw Cost represents the scale or quantity of the wth environmental restoration allocated to the jth land type in the i-th region; sum represents the total cost; q min represents the feasibility scale of ecological restoration; q max represents the maximum feasible size.

[0090] ⑤ Aiming for minimal social change (i.e., minimization of social change):

[0091]

[0092] Among them, γ ij represents the unit social change cost, taking into account migration, employment changes, etc.; S ij It represents the social impact coefficient, which reflects the actual impact of land use on regional society.

[0093] ⑥ Aiming to minimize environmental risks:

[0094]

[0095] in, Indicates the intensity of environmental risk, combined with indicators such as soil erosion and water pollution; R ij Represents environmental risk weights, which are dynamically adjusted to reflect regional and use differences.

[0096] (3) Constraints:

[0097] ①Use uniqueness constraint:

[0098]

[0099] Each land unit i can only be allocated to one use (mountain, water, forest, field, lake, grass, sand).

[0100] ② Suitability constraints:

[0101]

[0102] Among them, S ij is a binary variable, indicating the suitability coefficient, S ij =1 means that the i-th area is suitable for allocation purpose j, S ij =0 indicates that the i-th area is not suitable for allocation purpose j.

[0103] ③ Water resource constraints:

[0104] Research Unit Water Allocation: Q(ij,t)=δ ij ×Q(t) (12)

[0105] The minimum requirement for healthy water resources: Q(ij,t)≥Q HS (ij) (13)

[0106] Constraints on the total water resources balance in the basin:

[0107] Where Q(ij,t) represents the amount of water resources allocated to use j in region i at time t; time t is a period of time, and the specific range can be flexibly set according to research or management needs. Common ranges include days, months, quarters, and years. For example, if the research focus is seasonal water resource allocation, time t can be set to one quarter. If the research focus is long-term ecological restoration, time t can be set to one year or multiple years. ij is the allocation weight, which indicates the degree to which the region is suitable for allocating water resources; Q(t) represents the total available water resources at time t; Q HS (ij) represents the healthy socio-economic water demand, which is related to the uses and regional characteristics corresponding to i, j, and ensures basic ecological and economic needs.

[0108] ④Area constraint:

[0109] Total area constraint:

[0110] Land allocation suitability constraints:

[0111] Constraints on the area of suitable climate zones:

[0112] Minimum protection area constraints:

[0113] Land use classification balance constraints:

[0114] Among them, A ij A represents the utilization area of land of type j (i.e., land type j) in region i (i.e., region i); total represents the total land use area; S ij is the suitability coefficient (0 or 1), indicating whether region i (i.e., region i) is allocated to use j (i.e., use j); S ij is a binary variable, S ij =1 means suitable for allocation, S ij =0 means not suitable for allocation; represents the maximum allocable area of area i corresponding to use j; It represents the area of the suitable climate zone for land use j in region i; A represents the minimum protected area for area i corresponding to use j; itc and A irc represents the area of natural and artificial grassland in region i; A itl and A ijl represents the area of natural and artificial forest land in region i; A is represents the mountainous area in region i.

[0115] ⑤ Irrigation water constraints:

[0116] ∑ z Ir ijz ·A ijz ≥MinIr eco (ij)+MinIr prod (ij) (20)

[0117] Among them, Ir ijz A represents the irrigation water volume per unit area when the zth block in the i-th region is used for purpose j; ijz represents the land area of the zth block in the i-th region used for purpose j; MinIr eco (ij) and MinIr prod (ij) represents the minimum ecological and production water requirement for the jth use (i.e., the jth type of use) in the i-th area; the division of blocks can be based on the following methods:

[0118] 1. Physical Geography Unit:

[0119] The blocks are divided according to natural geographical features such as topography, water system, and soil type.

[0120] For example, the watershed can be divided into different blocks according to sub-watersheds, slope direction, altitude, etc.

[0121] 2. Land use type:

[0122] The blocks are divided according to the current land use status (such as farmland, forest land, grassland, etc.).

[0123] For example, areas with the same land use type are divided into blocks.

[0124] 3. Administrative or management unit:

[0125] Blocks are divided according to administrative divisions or management units (such as villages, towns, etc.).

[0126] For example, land within the same administrative area is divided into a block.

[0127] 4. Grid division:

[0128] Based on the results of the gridding process, each grid cell is further subdivided into smaller blocks.

[0129] For example, within a 1km×1km grid unit, blocks are divided into smaller scales (such as 100m×100m).

[0130] The specific division method should be determined based on the characteristics and actual needs of the study area.

[0131] ⑥ Constraints on canal water delivery efficiency:

[0132] ∑ z η ijz ·Ir ijz ·A ijz ≥MinIr eco (ij)+MinIr prod (ij) (21)

[0133] Among them, η ijz It represents the canal system water delivery efficiency when the zth block in the i-th region is used for purpose j.

[0134] ⑦ Surface water and groundwater availability constraints (i.e. surface water and groundwater availability constraints):

[0135]

[0136] Among them, SX ij and GXij represents the surface water and groundwater supply allocated to use j in region i (mm / hm2) 2 );EP i represents the precipitation in region i (mm); β ij is the precipitation distribution coefficient, which indicates the proportion of precipitation actually received by use j; ET ij represents the evapotranspiration demand (mm) for use j in region i; α represents the supply-demand adjustment coefficient; the specific value of α needs to be determined by comprehensively considering regional hydrological characteristics, ecological needs, socioeconomic needs, and model calibration and optimization processes. In actual applications, the value of α can be flexibly adjusted based on specific data and model requirements:

[0137] 1. Preliminary estimate:

[0138] Based on the regional water resource supply and demand, the value of α is preliminarily estimated. For example, α = 1 can be set as the initial value, indicating a balance between supply and demand.

[0139] 2. Model optimization:

[0140] During the model operation, the value of α is adjusted through trial and error or optimization algorithms (such as genetic algorithm, particle swarm optimization, etc.) to optimize the objective function.

[0141] 3. Sensitivity analysis:

[0142] Conduct sensitivity analysis to test the impact of changes in α on model results and determine its reasonable range.

[0143] 4. Finalize:

[0144] The specific value of α is determined based on the results of model calibration, optimization, and sensitivity analysis.

[0145] ⑧Water balance constraints:

[0146]

[0147] Among them, I ij represents the water input allocated to use j in region i, including precipitation, surface water inflow, groundwater recharge, etc.; ij represents the water output allocated to use j in region i, including evapotranspiration demand, surface water outflow, groundwater leakage, etc.; ΔW i Represents the dynamic changes of water storage in region i.

[0148] ⑨Hydrological function target constraints:

[0149] For the formulated configuration results of mountains, rivers, forests, farmlands, lakes, grasslands and sands, basic hydrological service functions such as river runoff, sediment, flow velocity and lake ecological water level must be met at the regional scale. Hydrological service function target constraints (i.e., hydrological function target constraints) are set:

[0150]

[0151] Among them, H k' , where k' = 1, 2, 3, 4 represents the regulation ability of runoff, sediment content, river channel velocity, and lake water level under the current vegetation configuration; when k' = 1, H k' represents the regulation ability of runoff under the current vegetation configuration, when k' = 2, H k' represents the river sediment content under the current vegetation configuration, when k' = 3, H k' represents the river channel velocity under the current vegetation configuration, when k' = 4, H k' represents the lake water level under the current vegetation configuration; K k' represents the balance threshold or ecological capacity of the k'-th type of hydrological service function in the region, that is, the target achievement value or target expected value, with a priority level l, l = 1, 2, 3, 4; K k' is the target value or expected value of the k'-th type of hydrological service function; the priority level l represents the importance and priority of different functions, l = 1 is the highest priority, l = 4 is the lowest priority; in the optimal configuration, the model will sequentially meet the target values of K1', K2', K3', and K4' according to the priority level l to ensure the priority realization of high-priority functions; K1' represents the regulation ability of runoff: the annual runoff variability; K2' represents the appropriate sediment content of the river: the sediment content Cs(t) of the river in the t period < the sediment-carrying capacity M(v, t) of the river in the t period; K3' represents the river channel velocity: V d < V < V s , the river channel velocity V should be between the channel siltation velocity V d and the channel scouring velocity V s ; K4' represents the lake water level: H ≥ H s , the lake water level H should be greater than the ecological water level H s .

[0152] ⑩ Ecological function target constraint:

[0153] The optimal configuration results of mountains, waters, forests, farmlands, lakes, grasses, and sands need to meet the basic ecological functions of different types of ecosystems, and set ecological function target constraints:

[0154]

[0155] Among them, V k , where k = 1, 2, 3, 4 represents the lower limit of biodiversity of a healthy ecosystem, the minimum ecological flow of the river, the lowest ecological water level of the lake, and the minimum ecological water demand of the wetland under the current optimal configuration; K kK represents the balance threshold or ecological capacity of the kth ecological service function, that is, the target achievement value or target expectation value, with priority level l, l = 1, 2, 3, 4; k is the target value or expected value of the kth type of ecological service function; the priority level l indicates the importance and priority of different functions, l = 1 is the highest priority, and l = 4 is the lowest priority; in the optimization configuration, the model will meet the target values of K1, K2, K3, and K4 in turn according to the priority level l to ensure that high-priority functions are realized first; K1 is the regional biodiversity index, with a first-level target value of 20, a second-level target value of 30, and a third-level target value of 60; K2 is the minimum basic flow of the river: the first-level target value is 10% of the multi-year average runoff, the second-level target value is 20% of the multi-year average runoff, and the third-level target value is 30% of the multi-year average runoff; K3 is the minimum basic flow of the lake Ecological water level satisfaction degree: the first-level target value is that the 7-day sliding average water level is lower than the minimum ecological water level, the second-level target value is that the 3-day sliding average water level is lower than the minimum ecological water level, but the 7-day sliding average water level is not lower than the minimum ecological water level, the third-level target value is that the daily average water level is lower than the minimum ecological water level, but the 3-day sliding average water level is lower than the minimum ecological water level, and the fourth-level target value is that the daily average water level throughout the year is higher than the minimum ecological water level; K4 is the minimum ecological water demand of the wetland: the first-level target value is that the wetland vegetation and water surface account for 60% to 70% of the total area, the second-level target value is that the wetland vegetation and water surface account for 70% to 80% of the total area, and the third-level target value is that the wetland vegetation and water surface account for more than 80% of the total area.

[0156] (IV) Solution and optimization:

[0157] In order to achieve the optimal configuration of mountains, rivers, forests, farmlands, lakes, grasslands and sand in the watershed, the solution and optimization steps of this application are specifically divided into the following stages:

[0158] (1) Initialization and preprocessing:

[0159] ① Model parameter setting: determine the weight coefficient of the objective function; set the initial conditions, including the ecological capacity of the watershed, the total land area, the total amount of water resources, etc.; define the upper and lower limits of the decision variables X ij ∈{0,1},W ij >0,A ij >0.

[0160] Among them, the objective function weight coefficient refers to the formula α in k and α l ; Initial conditions refer to the basic parameters and constraints that need to be set before the model runs, including: Formula (2) Hy k 、Ec l , formula (3)gr ij , sc ij, formula (4)wq ij , formula (5)co ijw ,q ijw , formula (6)Cost sum , formula (8)γ ij 、S ij , formula (9)θ ij 、R ij , formula (12)δ ij , Q(t), formula (13)Q HS (ij), formula (15) Formula (16) S ij 、 Formula (17) Formula (18) Formula (20)Ir ijz 、MinIr eco (ij), MinIr prod (ij), formula (21)η ijz , formula (22)SX ij 、GX ij , EP i ET ij , formula (23)I ij , O ij , Formula (24) K k' , Formula (25) K k .

[0161] ② Data gridding: Divide the watershed into small grid units (such as 1km 2 ), with each unit acting as an independent decision-making unit. Topographic, ecological, and hydrological characteristics are input into each grid cell of the watershed division as the basis for decision-making in the optimization model. Data is obtained through the following methods:

[0162] Topographic data: Remote sensing images (such as satellites and drones) and GIS technology are used to generate digital elevation models (DEMs).

[0163] Ecological data: remote sensing inversion (such as NDVI vegetation index) combined with ground surveys (biodiversity, wetland area).

[0164] Hydrological data: meteorological / hydrological station observations, satellite precipitation products (such as GPM) and hydrological model simulations (such as SWAT).

[0165] (2) Algorithm design and parameter configuration:

[0166] To solve the nonlinear multi-objective dynamic programming model, this application uses a combination of the following optimization algorithms:

[0167] ①Genetic Algorithm (GA) configuration:

[0168] Encoding method: X ij 、W ij 、A ij are grouped into chromosomes. For example, each chromosome contains: a binary part X ij (7 bits, indicating 7 uses); real number part W ij and A ij (continuous value).

[0169] Parameter settings: Set the population size to 200; set the crossover rate to 0.8 (two-point crossover); set the mutation rate to 0.05 (bit flip mutation).

[0170] ②Particle Swarm Optimization (PSO) configuration:

[0171] Particle representation: Each particle represents a set of solutions (X ij 、W ij 、A ij ), real number encoding.

[0172] Parameter settings: Set the number of particles to 100; set the inertia weight to 0.9 (linearly decreasing); set the learning factor c1 = c2 = 2.0.

[0173] ③ Hybrid algorithm strategy:

[0174] Phase 1 (GA global search): Run GA for 100 generations to generate diverse candidate solutions.

[0175] Phase 2 (PSO local optimization): The first 50 solutions are selected from the Pareto frontier of GA as the initial particle swarm of PSO, and 50 generations are iterated for fine-tuning.

[0176] (3) Iterative solution and optimization:

[0177] ① Parameter initialization: Randomly generate an initial solution set (such as 100 sets of X ij 、W ij 、A ij ), ensuring that the binary and area constraints are met.

[0178] ② Iterative solution: Evaluate the objective function value of each solution; generate a new solution by updating the speed and position in the optimization; check whether the solution meets all constraints. If not, correct it through the penalty function.

[0179] ③ Convergence criterion: When the change of the objective function is less than a threshold (such as 0.1%), or when the maximum number of iterations is reached (such as 500 times), stop the optimization.

[0180] (4) Result output and verification:

[0181] ① Extract the optimal solution: extract the optimized optimal solution set and output the land use allocation (X ij ), water resources allocation (W ij ) and area allocation (A ij ).

[0182] Among them, the relationship between land use allocation, water resource allocation and area allocation (i.e. land area allocation) and the stable and appropriate coordination of mountains, rivers, forests, farmlands, lakes, grasslands and sands in the basin is: land use, water resources and area allocation are specific technical means, and the refined allocation of space and resources is achieved through scientific decision-making. The stable and appropriate coordination of mountains, rivers, forests, farmlands, lakes, grasslands and sands in the basin is the ultimate goal, which is reflected in the stability of the ecosystem, the efficiency of resource utilization, and the sustainability of the social economy. The dynamic coordination of the three is the key to achieving the goal: land use determines the functional positioning (such as forest land conserving water sources), water resource allocation supports the realization of functions (such as forest land requires a certain amount of precipitation or irrigation), and area allocation controls the scale of functions (such as the forest area must meet the ecological threshold).

[0183] ②Verify constraint compliance: Check whether all constraints are met.

[0184] ③Uncertainty analysis: Using Monte Carlo simulation, randomly perturb the input data (such as ±10% precipitation change) to evaluate the robustness of the optimization results.

[0185] Monte Carlo simulation quantifies the potential impact of random perturbations on input parameters on the optimized allocation results, ensuring that stable coordination of mountains, rivers, forests, farmlands, lakes, grasslands and sands can still be achieved under climate change.

[0186] This application uses innovative technologies for stabilizing and optimizing the distribution of mountains, rivers, forests, farmlands, lakes, grasslands, and sand in the watershed, taking into account the dynamic balance and coordinated optimization of multiple factors within the watershed, to achieve the following results:

[0187] (1) Improving the overall ecological function of the watershed: This application focuses on optimizing the eco-hydrological function and, through a multi-objective dynamic programming model, achieves a stable and appropriate coordination of elements such as mountains, water, forests, fields, lakes, grass, and sand. This effectively improves the stability, resilience, and sustainability of the overall ecosystem in the watershed.

[0188] (2) Optimizing resource utilization efficiency: By coordinating the rational allocation of water and land resources, this application significantly improves resource utilization efficiency. In water resource management, it achieves improved water use efficiency and optimizes the spatial allocation of water resources, meeting the multiple needs of ecological protection and socioeconomic development.

[0189] (3) Constructing a regional ecological security pattern: This application constructs a security pattern for the watershed ecosystem through the coordinated optimization of spatial distribution and functions. This application achieves the benign regulation of the hydrological cycle, the gradient optimization of vegetation restoration, and the stabilization of the ecological water levels of wetlands, rivers, and lakes, significantly enhancing the region's ability to resist extreme climate events.

[0190] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 2 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data on the stability and suitability of the watershed's mountains, waters, forests, farmlands, lakes, grasslands and sands. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for the stability and suitability of the watershed's mountains, waters, forests, farmlands, lakes, grasslands and sands is implemented.

[0191] Those skilled in the art will understand that Figure 2 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present application and does not constitute a limitation on the computer device to which the solution of the present application is applied. A specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps of the above-mentioned method embodiments when executing the computer program.

[0192] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0193] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0194] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0195] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0196] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0197] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0198] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for stabilizing and appropriately coordinating the distribution of mountains, waters, forests, farmlands, lakes, grasslands, and sand in a watershed, characterized in that: The method for stabilizing and balancing the watershed's mountains, waters, forests, farmlands, lakes, grasslands, and sand includes: The study area is gridded to divide the watershed into multiple study units; the study area is an area containing the watershed and its components; the watershed components include mountains, water, forests, fields, lakes, grass, and sand; For each of the research units, basic watershed data corresponding to the research unit is obtained, whereby basic watershed data corresponding to each of the research units is obtained; the basic watershed data is data related to the components of the watershed; the basic watershed data includes topographic data, hydrological and meteorological data, ecological factor data, water demand data, and industrial and population distribution data; Based on the basic watershed data corresponding to each research unit, a multi-objective optimization model for inland river basins is constructed with the goals of optimal ecological and hydrological function trade-offs, maximizing grain and livestock production, optimizing water resource allocation efficiency, minimizing environmental restoration costs, minimizing social changes, and minimizing environmental risks. The multi-objective optimization model for inland river basins is a nonlinear multi-objective dynamic programming model based on the benign interactive relationship between ecology and hydrology. By combining a hybrid algorithm of genetic algorithm and particle swarm optimization algorithm, the multi-objective optimization model for inland river basins is solved to obtain a stable and suitable coordination result of mountains, rivers, forests, farmlands, lakes, grasslands and sands in the basin; the stable and suitable coordination result of mountains, rivers, forests, farmlands, lakes, grasslands and sands in the basin includes the optimal land use allocation result, the optimal water resources allocation result and the optimal land area allocation result.

2. The method for stabilizing and appropriately coordinating the watershed mountains, waters, forests, farmlands, lakes, grasslands and sand according to claim 1 is characterized in that: The study area is gridded and the basin is divided into multiple research units, including: Based on remote sensing images and GIS technology, the watershed is divided into regular or irregular grids according to spatial characteristics to obtain n research units; the i-th research unit corresponds to the i-th region; i is the research unit number, i = 1, 2,…, n; n is the total number of research units.

3. The method for stabilizing and appropriately coordinating the watershed mountains, waters, forests, farmlands, lakes, grasslands and sand according to claim 1 is characterized in that: The topographic data include watershed elevation data, river course data, slope distribution data and land use type data; The hydrological and meteorological data include precipitation data, runoff data, evaporation data, temperature data and groundwater level data; The ecological factor data include wetland area data, biodiversity data, vegetation coverage data and land salinization degree data; The water demand data includes the current data of agricultural water use, animal husbandry water use, industrial water use, and residential water use; the current data of agricultural water use includes the types, area, and yield of irrigated crops; the current data of animal husbandry water use includes the scale of breeding and water consumption; the current data of industrial water use includes production water consumption, wastewater discharge, and water resource reuse rate; the current data of residential water use includes per capita daily water consumption, water supply coverage, and seasonal water use changes; The industry and population distribution data include population size, migration trends and industry layout data within the research unit.

4. The method for stabilizing and appropriately coordinating the watershed mountains, waters, forests, farmlands, lakes, grasslands and sand according to claim 2, characterized in that: The decision variables of the multi-objective optimization model for inland river basins include land use allocation variables, water resource allocation variables and land area allocation variables; The land use allocation variable is expressed as: X ij |i=1,2,…,n;j=1,2,…,7;The water resource allocation variable is expressed as: W ij |i=1,2,…,n;j=1,2,…,7;the land area allocation variable is expressed as: A ij |i=1,2,…,n;j=1,2,…,7;where X ij Indicates whether the i-th area is allocated to the j-th use, X ij =1 means allocation, X ij =0 means no allocation, j=1,2,3,4,5,6,7 corresponds to the seven uses of mountain, water, forest, field, lake, grass, and sand; W ij A represents the amount of water resources allocated to the jth purpose in the i-th region; ij represents the land area allocated to the jth use in the i-th region.

5. The method for stabilizing and appropriately coordinating the watershed mountains, waters, forests, farmlands, lakes, grasslands and sand according to claim 1 is characterized in that: The objective function of the multi-objective optimization model for inland river basins is expressed as: OptF = [MaxOb, MaxProd, MaxWQ, MinCOST, MinCH, MinEN]; wherein OptF represents the objective function of the multi-objective optimization model for inland river basins, MaxOb represents the goal of optimal balance between ecological optimization and hydrological functions, MaxProd represents the goal of maximizing grain and livestock production, MaxWQ represents the goal of optimizing water resource allocation efficiency, MinCOST represents the goal of minimizing environmental restoration costs, MinCH represents the goal of minimizing social changes, and MinEN represents the goal of minimizing environmental risks.

6. The method for stabilizing and appropriately coordinating the watershed mountains, waters, forests, farmlands, lakes, grasslands and sand according to claim 1, characterized in that: The constraints of the multi-objective optimization model for inland river basins include use uniqueness constraints, suitability constraints, water resource constraints, area constraints, irrigation water volume constraints, canal water delivery efficiency constraints, water balance constraints, hydrological function target constraints, ecological function target constraints, and surface water and groundwater availability constraints.

7. The method for stabilizing and appropriately coordinating the watershed mountains, waters, forests, farmlands, lakes, grasslands and sand according to claim 6, characterized in that: The water resource constraints include the water resource allocation constraints of the research unit, the lower limit constraints of healthy water resource demand and the total water resource balance constraints of the basin; The area constraints include regional total area constraints, land allocation suitability constraints, suitable climate zone area constraints, minimum protection area constraints and land use classification balance constraints.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and runnable on the processor, characterized in that the processor executes the computer program to implement the method for stable and suitable coordination of mountains, waters, forests, fields, lakes, grasslands and sands in a watershed according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for stable and suitable coordination of mountains, waters, forests, farmlands, lakes, grasslands and sands in a watershed as described in any one of claims 1 to 7 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for stable and suitable coordination of mountains, waters, forests, farmlands, lakes, grasslands and sands in a watershed as described in any one of claims 1 to 7 is implemented.

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