Watershed transverse ecological protection compensation grading and classification standard matching method and system and medium

Abstract

The invention relates to the technical field of watershed ecological digital models, in particular to a watershed transverse ecological protection compensation grading and classification standard matching method and system and a medium. Hydrological features, ecological features, topographic features and social economic features of a watershed are integrated, river classification is determined by adopting a self-organizing feature mapping neural network, the upper limit of the construction scale of a compensation mechanism is matched for rivers of different levels, and the problems to be supported and solved by the compensation mechanism are accurately determined for rivers of different categories. On the basis of problem orientation, compensation indexes, compensation modes and compensation standard accounting modes are subjected to differential matching, so that a set of graded and classified river basin ecological compensation mode matching method is constructed, and differential compensation mechanisms can be accurately matched according to different river characteristics and protection and treatment requirements.

Description

Technical Field

[0001] The present invention relates to the technical field of watershed ecological digital models, and in particular to a method, system and medium for matching grading and classification standards of watershed horizontal ecological protection compensation. Background Art

[0002] The establishment of a tiered and categorized compensation mechanism has entered the realm of both practical and academic research. Research on watershed grading and classification methods has proposed a method for interpreting the water function zoning of ecosystems at different levels within a watershed, based on characteristics such as watershed integrity, fluidity, and regional heterogeneity. This method uses multiple characteristic indicators to interpret the water function zoning of ecosystems at different levels within a watershed, providing a theoretical basis for grading and classification. Furthermore, a standard for categorizing watersheds based on the Strahler scale has been proposed, combining DEM data to extract river channel grades (1-7) and correlate them with parameters such as watershed area and topographic relief.

[0003] At present, due to the diversity of river basin types and huge differences between levels, there is a lack of unified plans for the scale, model, standard, and method of horizontal ecological protection compensation in river basins. This makes the design and analysis of compensation mechanisms for river basins time-consuming and labor-intensive, and there is a lack of comparability between horizontal compensation mechanisms in different river basins. In particular, in the same water system, compensation mechanisms in different river basins are difficult to coordinate and integrate with each other. Summary of the Invention

[0004] The purpose of the present invention is to provide a method, system and medium for matching the grading and classification standards of watershed horizontal ecological protection compensation to solve the above-mentioned problems in the prior art.

[0005] The present invention is achieved through the following technical solutions: In a first aspect, the present invention provides a method for matching grading and classification standards for horizontal ecological protection compensation in a watershed, including: Determine the level of the river basin through the water system hierarchy method combined with the basin area index, obtain the compensation scale of the current river basin based on the current river basin classification, and use the compensation scale to determine the upper limit of the overall scale of the ecological compensation fund for the current river basin; The Self-Organizing Feature Map (SOFM) neural network model is used to classify the current watershed based on the basin's hydrological, ecological, topographic, and socioeconomic data. Establish a mapping relationship data table between different watershed classifications and specific key measures of the compensation mechanism, and index the corresponding compensation mechanism measures in the mapping relationship data table according to the current watershed classification; The obtained compensation scale and compensation mechanism measures are saved as the final results for output.

[0006] Preferably, the method of determining the level of a watershed by combining the water system hierarchy method with the watershed area index includes: The current watershed is naturally graded using the water system hierarchy method to obtain an initial grade; Obtain the first area of the current watershed and the second area of the same-level watershed in historical data. If the first area is larger than the second area, raise the current initial level to the next level with a larger compensation scale. If the first area is smaller than the second area, the current initial level is reduced to the next level with a smaller compensation scale; If the first area is equal to the second area, the current initial level remains unchanged.

[0007] Preferably, the hydrological characteristic data include river flow data, river length data and basin area data of the basin; the ecological characteristic data include normalized difference vegetation index (NDVI data), annual precipitation data, water conservation service data; the terrain characteristic data include elevation data, slope data, and terrain undulation data; and the socio-economic characteristic data include population data and economic data of the administrative regions through which the basin passes.

[0008] Preferably, obtaining water conservation service data includes:

[0009] Where, is the annual water yield on the time grid for land use type, is the actual annual average evapotranspiration, is the average annual precipitation of the grid.

[0010] Preferably, the slope data includes:

[0011] in, is the slope, and The DEM grid cells are and Directional elevation gradient; The terrain relief data includes:

[0012] in, is the terrain relief, is the number of grids in the DEM data; It is the elevation difference of each grid, which represents the elevation difference between a grid and its adjacent grids.

[0013] Preferably, the determining the classification of rivers using the SOFM neural network includes: Set judgment thresholds for different classifications, obtain hydrological characteristic data, ecological characteristic data, topographic characteristic data, and socioeconomic characteristic data, and perform normalization preprocessing; Construct a SOFM neural network model, determine the input layer and output layer, and calculate the Euclidean distance between input neurons; Select the minimum distance and the winning neuron as the best matching unit, and output the current matching result.

[0014] Preferably, obtaining the compensation scale of the current watershed according to the classification of the current watershed includes: Setting up first-level river basins, second-level river basins to n-level river basins, with the compensation scales of the first-level river basins, second-level river basins to n-level river basins decreasing in sequence; Determine the compensation scale for the first-level watershed:

[0015]

[0016]

[0017]

[0018] Determine the compensation scale for secondary watersheds:

[0019] This process is repeated until the drainage area of rivers of level n and below is generally small (usually <50 km²), and the compensation scale for the level n drainage basin is determined as follows:

[0020] Where, is the compensation scale of the first-level basin, The basic coefficient is determined based on the ecological protection cost per unit area of the basin and the regional payment capacity, with the unit being 10,000 yuan / km². is the first-level watershed area, 、 and Calculate the weights respectively. is the hydrological regulation coefficient, is the ecological sensitivity coefficient, is the economic coefficient (reflecting the degree of development imbalance), is the median of the average flow of the same level basin in several years in historical data, is the actual flow of the current first-level basin, is the NDVI data of the current first-level watershed, is the compensation scale of the secondary watershed, is the area of the secondary watershed, is the functional condition factor, is the compensation scale of the n-level watershed, is the area of the n-level watershed, is the type connection coefficient, is the type unit price, is the total GDP value of the upper reaches of the current basin, It is the total GDP value of the current downstream of the basin.

[0021] Preferably, the functional condition factors include: Determine whether the current watershed is a water source or ecological protection area. If so, If not, then determine whether the current basin is an important agricultural settlement area or a population settlement area. If so, then 1.2, if not, then .

[0022] The watershed types include ecological tributary type, plain water-rich type, mountain ecological type and regional balanced type. The type connection coefficients corresponding to the watershed types are 1.2, 1.0, 1.1 and 0.9 respectively, and the type unit prices corresponding to the types are 1.2 million yuan / km², 60 or 100 / km², 90 / km² and 80 / km² respectively.

[0023] In a second aspect, the present invention also provides a watershed horizontal ecological protection compensation classification standard matching system, including: The watershed attribute determination module is configured to determine the level of the watershed based on the water system level and area index of the watershed, and to use the SOFM neural network to determine the classification of the river based on the hydrological characteristic data, ecological characteristic data, topographic characteristic data and socio-economic characteristic data of the watershed to obtain different classifications of the current watershed; The compensation scale and mechanism measure calculation module is configured to obtain the compensation scale of the current basin based on the current basin classification, and determine the upper limit of the overall scale of the ecological compensation fund for the current basin based on the compensation scale; establish a mapping relationship data table between different basin classifications and specific key measures of the compensation mechanism, and index the corresponding compensation mechanism in the mapping relationship data table based on the current basin classification; The output module is configured to save the obtained compensation scale and compensation mechanism measures as final results for output.

[0024] In a second aspect, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned method for matching the grading and classification standards of watershed horizontal ecological protection compensation.

[0025] The technical solution of the present invention has at least the following advantages and beneficial effects: The present invention follows the idea of natural grade as the main factor and watershed scale as the auxiliary factor, determines the level of the watershed through the water system hierarchy method combined with the watershed area index, comprehensively considers the hydrological characteristics, ecological characteristics, topographic characteristics and socio-economic characteristics of the watershed, and adopts the self-organizing feature mapping neural network to determine the classification of rivers, matches the upper limit of the construction scale of the compensation mechanism for rivers of different grades, and accurately determines the problems that the compensation mechanism should support and solve for rivers of different categories. On the basis of problem orientation, the compensation indicators, compensation methods and compensation standard calculation methods are differentiatedly matched, thereby constructing a set of graded and classified watershed ecological compensation model matching methods, which can accurately match differentiated compensation mechanisms according to the characteristics of different rivers and protection and management needs, forming a three-dimensional watershed ecological compensation system focusing on the whole chain process of "grading-classification-compensation matching", providing key technical support for the construction of my country's watershed ecological compensation system, enabling technical personnel to further improve their work efficiency when using it, and can be quickly applied to the design practice of horizontal ecological protection compensation mechanisms in watersheds across the country. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0027] Figure 1 It is the overall idea (technical route) diagram of the present invention; Figure 2 Schematic diagram of the SOFM neural network model structure of the present invention; Figure 3 The function regulating factor value determination process and standard of the present invention; Figure 4 Flowchart for determining compensation scale for small watersheds (area < 50 km²) of the present invention. DETAILED DESCRIPTION

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0029] The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. The naming or numbering of steps in this application does not necessarily imply that the steps in the method flow must be executed in the temporal or logical sequence indicated by the naming or numbering. Named or numbered process steps may be executed in a different order based on the technical objectives to be achieved, as long as the same or similar technical effects are achieved.

[0030] Independently described modules or submodules may or may not be physically separated: they may be implemented in software or hardware, and some modules or submodules may be implemented in software, with the processor invoking the software to implement the functions of these modules or submodules, while other modules or submodules may be implemented in hardware, such as hardware circuits. In addition, some or all of the modules may be selected according to actual needs to achieve the purpose of the present application.

[0031] Please refer to Figure 1 - Figure 4 The present invention provides a method for matching grading and classification standards for horizontal ecological protection compensation in a watershed, including: S101: Determine the level of the watershed using the watershed hierarchy method combined with the watershed area index, obtain the compensation scale for the current watershed based on the current watershed classification, and determine the upper limit of the overall scale of the ecological compensation fund for the current watershed based on the compensation scale; S102: combining the hydrological characteristic data, ecological characteristic data, topographic characteristic data and socio-economic characteristic data of the watershed, using a self-organizing feature mapping neural network model to classify the current watershed, thereby obtaining different classifications of the current watershed; In this embodiment, the types obtained by classification include main river basin type, ecological tributary type, plain water-rich type, mountain ecological type and regional balance type.

[0032] S103: Establishing a mapping relationship data table between different watershed classifications and compensation measures, and indexing the corresponding key measures of the compensation mechanism in the mapping relationship data table according to the current watershed classification; S104: The obtained compensation scale and compensation mechanism measures are saved as the final result for output.

[0033] The present invention follows the idea of natural grade as the main factor and watershed scale as the auxiliary factor. It determines the level of the watershed through the water system hierarchy method combined with the watershed area index, comprehensively considers the hydrological characteristics, ecological characteristics, topographic characteristics and socio-economic characteristics of the watershed, and adopts the self-organizing feature mapping neural network to determine the classification of rivers. It matches the upper limit of the construction scale of the compensation mechanism for rivers of different grades, accurately determines the problems that the compensation mechanism should support and solve for rivers of different categories, and differentiates the matching of compensation indicators, compensation methods and compensation standard calculation methods on the basis of problem orientation, thereby constructing a set of graded and classified watershed ecological compensation model matching methods. It can accurately match differentiated compensation mechanisms according to the characteristics of different rivers and the needs of protection and management, forming a three-dimensional watershed ecological compensation system focusing on the whole chain process of "grading-classification-compensation matching", providing key technical support for the construction of my country's watershed ecological compensation system, enabling technical personnel to further improve their work efficiency when using it, and can be quickly applied to the design practice of horizontal ecological protection compensation mechanisms in watersheds across the country.

[0034] In an exemplary embodiment of the present invention, the method of determining the level of a watershed by combining the natural water system method with the watershed area index includes: S201: Naturally classify the current watershed using the water system hierarchy method to obtain an initial grade; Among them, the river hierarchy method (also known as the Strahler classification method) is a method for quantitatively classifying rivers based on topological relationships, which is used to scientifically describe the river network structure. Combining GIS technology to carry out natural classification of the current river basin includes: Source rivers: River sections without tributaries are classified as Level 1. Confluence rules: When rivers of the same level meet, the level is increased by 1 (e.g., Level 1 + Level 1 → Level 2). When rivers of different levels meet, the highest level is used (e.g., Level 2 + Level 1 → Level 2).

[0035] Then, data preparation is performed, including DEM preprocessing and river network generation: flow direction calculation and runoff accumulation, threshold setting: adjusted according to the basin size (such as 2000 grid cells for a 10km² basin); river network extraction; Strahler classification, including vectorization of the river network, Stream to Feature to convert the raster river network into vector lines; classification calculation, including selecting the Strahler method, output: automatically generate the ORD_STRAHL field storage level, and finally visual analysis.

[0036] S202: Obtain the first area of the current watershed and the second area of the watershed of the same level in historical data. If the first area is larger than the second area, raise the current initial level to the upper level with a larger compensation scale; if the first area is smaller than the second area, lower the current initial level to the lower level with a smaller compensation scale; if the first area is equivalent to the second area, keep the current initial level unchanged.

[0037] That is, on the basis of natural classification, the river basin area of each river is compared with rivers of the same level. According to the expert experience method, the river with an area much larger than the same level will be appropriately upgraded, and the river with an area much smaller than the same level will be appropriately downgraded.

[0038] In an exemplary embodiment of the present invention, the hydrological characteristic data include river flow data, river length data and basin area data of the basin, the ecological characteristic data include NDVI data, annual precipitation data, water conservation service data, the terrain characteristic data include elevation data, slope data, and terrain undulation data, and the socio-economic characteristic data include population data and economic data of the administrative regions through which the basin passes, as shown in Table 1 below: Cross-provincial basin classification index system.

[0039] Table 1 Classification index system of interprovincial river basins

[0040] Hydrological characteristics serve as the basis for classification, with three secondary indicators: flow, river length, and drainage area. Flow reflects the amount of water resources in a river and is an important basis for assessing its carrying capacity and development and utilization potential. River length directly reflects the spatial extent of a river and is crucial for understanding its overall shape and flow. Drainage area comprehensively captures the spatial extent of a river's influence and serves as the basis for assessing its impact on the surrounding environment.

[0041] The river length was obtained based on river statistics, and the flow and basin area were obtained statistically using the Soil and Water Assessment Tool (SWAT) model based on DEM data for basin division.

[0042] In terms of ecological characteristics, NDVI, precipitation, and water conservation services were selected as secondary indicators. NDVI is a key indicator for measuring the health and stability of river ecosystems, reflecting the ecological conservation status of the river basin. Precipitation, as the primary source of river replenishment, has a direct impact on river water quantity and quality. Water conservation services emphasize the important ecological functions of rivers in maintaining water and soil, purifying water quality, and maintaining ecological balance.

[0043] Among them, NDVI data is calculated using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data.

[0044] The precipitation uses the daily precipitation station data provided by the National Meteorological Information Center. The spatial data of daily precipitation are obtained through the Kriging interpolation method and then calculated into annual precipitation data.

[0045] Water conservation services use water supply services. Using the InVEST model, we use DEM data, land use data, soil data, meteorological data, and other data. Based on the Budyko hydrothermal coupled balance principle, we combine the spatial differences in soil permeability and evapotranspiration of different land use types and other factors that affect runoff to build a suitable model. We use grids as the unit to quantitatively estimate water supply services. The calculation formula is as follows:

[0046] Where, is the annual water yield on the time grid for land use type, is the actual annual average evapotranspiration, is the average annual precipitation of the grid.

[0047] Topographic characteristics include three secondary indicators: elevation, slope, and relief. Elevation reflects the altitude of the river's location and is a key factor influencing its formation and development. Slope directly affects the flow rate and flow pattern of the river, significantly influencing its erosion and sedimentation processes. Relief, a comprehensive measure of topographic complexity, is crucial for assessing the river's ability to shape the landform.

[0048] The elevation uses the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) data developed by NASA, with a data resolution of 30m.

[0049] The slope is calculated using DEM data and GIS software. The calculation formula is as follows:

[0050] in, is the slope, and The DEM grid cells are and The directional gradient of elevation.

[0051] Surface relief, also known as topographic relief, relative relief, or relative height, is the difference between the highest and lowest points in a given analysis area. It reflects the surface relief characteristics within a macroscopic region and is an important indicator for quantitatively describing landform morphology and classifying landform types. One common method for calculating surface relief is neighborhood analysis, and its calculation formula is as follows:

[0052] Where, is the terrain relief, is the number of grids in the DEM data; It is the elevation difference of each grid, which represents the elevation difference between a grid and its adjacent grids.

[0053] The key to calculating terrain relief lies in the highest and lowest points of the analysis window. As the analysis area expands, the height difference changes, causing the terrain relief value to change accordingly, ultimately affecting the terrain relief results for the entire area. Therefore, determining the optimal analysis window is a core step in the terrain relief extraction algorithm and is crucial to the effectiveness and efficiency of regional terrain relief extraction.

[0054] Regarding socioeconomic characteristics, population and GDP were selected as secondary indicators. Population density reflects the intensity of human activity within a river basin and is a key indicator for assessing the impact of rivers on human development. GDP represents the level of economic development within a river basin and is closely related to the extent of river development and utilization and the contribution of the river to the economy. Population and GDP data are primarily obtained from the statistical yearbooks of the corresponding administrative regions.

[0055] In an exemplary embodiment of the present invention, the method of determining river classification using a SOFM neural network includes: The SOFM neural network was used to classify transboundary rivers. The model simulates the neural network in the human brain, automatically revealing the inherent patterns and nature of input elements through continuous observation, analysis, and comparison, allowing it to accurately classify and identify elements with similar characteristics.

[0056] Set judgment thresholds for different classifications, obtain hydrological characteristic data, ecological characteristic data, topographic characteristic data, and socioeconomic characteristic data, and perform normalization preprocessing;

[0057] in, For the year, As an indicator, is the normalized value, is the original value, and Represent all indicators separately All indicator values are in the post-processing range of [0,1].

[0058] Construct a SOFM neural network model, determine the input layer and output layer, calculate the Euclidean distance between input neurons, select the minimum distance, and select the winning neuron as the best matching unit, and output the current matching result.

[0059] Using this classification method, we can categorize different river basins within the same river system. We then provide a portrait description and interpretation of each category based on its prominent characteristics across four key indicators: hydrology, ecology, topography, and socioeconomics. Generally speaking, potential river basin types include main stem basin, ecological tributary, regionally balanced, plain water-rich, and mountain ecological. The key characteristics of each type are shown in Table 2, along with reference thresholds for key indicators.

[0060] Table 2 Main characteristics and reference thresholds of key indicators of different types

[0061] In an exemplary embodiment of the present invention, obtaining the compensation scale of the current watershed according to the classification of the current watershed includes: Based on the above basin classification and categorization methods, it is possible to classify several rivers in the same water system so that each river belongs to a certain level and type. Based on the classification and categorization, this method further proposes the compensation scale, compensation model, compensation indicators, standards and methods for the transboundary basin compensation mechanism corresponding to different levels and types. The specific matching method is as follows: The scale of the watershed ecological compensation mechanism is determined by basin classification. From Level 1 onwards, the levels gradually decrease. The higher the basin level, the larger the compensation scale of the watershed compensation mechanism should be. This method uses the compensation scale corresponding to the watershed level as the upper limit for the overall scale of the watershed ecological compensation fund.

[0062] Setting up first-level river basins, second-level river basins to n-level river basins, with the compensation scales of the first-level river basins, second-level river basins to n-level river basins decreasing in sequence; Determine the compensation scale for the first-level watershed:

[0063]

[0064]

[0065]

[0066] Determine the compensation scale for secondary watersheds:

[0067] It should be noted that when the area of river basins at level n and below is generally small (usually <50km²), in order to cope with the failure of scale effect, combined with the characteristic that the dominant functional differences of small and micro-watersheds are far greater than the differences in area, the calculation method is changed to lump-sum calculation through type labels. The functional types of small watersheds are refined and connected to the five major watershed types. The specific calculation formula for the reference value of the compensation scale of rivers at level n and below will not change, and is specifically: Determine the compensation scale for the n-level watershed:

[0068] Where, is the compensation scale of the first-level watershed (unit: 10,000 yuan), The basic coefficient is determined based on the ecological protection cost and regional payment capacity (for example, the Chengdu-Chongqing area is 1-2 million yuan / km²). is the first-level watershed area, 、 and Calculate the weights respectively. is the hydrological regulation coefficient, is the ecological sensitivity coefficient, is the economic coefficient, is the median of the average flow of the same level basin in several years in historical data, is the actual flow of the current first-level basin, is the NDVI data of the current first-level watershed, is the compensation scale of the secondary watershed, is the area of the secondary watershed, is the functional condition factor, is the compensation scale of the n-level watershed, is the area of the n-level watershed, is the type connection coefficient, is the type unit price, is the total GDP value of the upper reaches of the current basin, It is the total GDP value of the current downstream of the basin.

[0069] The values of T are shown in Table 3 below: Table 3 T value standard

[0070] Specifically, the functional condition factors include: Determine whether the current watershed is a water source or ecological protection area. If so, If not, then determine whether the current basin is an important agricultural settlement area or a population settlement area. If so, then 1.2, if not, then ; Among them, the criteria for determining a water source or ecological protection zone is the existence of legally designated drinking water source protection zones or national / provincial nature reserves and other important ecological function areas in the basin; the criteria for determining an agricultural settlement area is that the proportion of cultivated land in the basin is >40%; and the criteria for determining a population settlement area is that the basin has a permanent population >5,000 and a population density >200 people / km².

[0071] The types include ecological tributary type, plain water-rich type, mountain ecological type and regional balance type, and the corresponding connection coefficients are 1.2, 1.0, 1.1 and 0.9 respectively.

[0072] The reference values are shown in Table 4: Table 4 λ value standard

[0073] Based on the watershed classification, the key characteristics of each type of watershed, and the main issues and needs of protection and governance, the main positioning of the watershed ecological compensation mechanism is determined, and then the compensation model, compensation indicators, standards, and methods are determined. A data table mapping the different watershed classifications and compensation measures is established, as shown in Table 5 below.

[0074] Table 5 Mapping relationship data table of differentiated ecological compensation mechanisms for different watershed types

[0075] Coordinated processing of compensation scale and compensation standard: For the calculation results of compensation scale and compensation standard corresponding to a specific level and type of watershed, the one with the lower value is selected as the compensation fund for ecological compensation of the watershed to ensure the coordination and systematization of compensation scales for rivers of different levels in the same water system.

[0076] This technology solves the industry pain points of "single standards and extensive matching" in watershed ecological compensation through a hierarchical and classified dual-track coupling mechanism, providing a set of scientific and feasible compensation decision-making tools for complex geographical areas such as the upper reaches of the Yangtze River.

[0077] Improved efficiency: Grading and classification efficiency is improved, and compensation plan formulation is automatically output from expert discussion to system output. Accuracy optimization: Category identification is more accurate, and compensation scales can be self-consistent and coordinated within the same water system. Enhanced policy implementation: It can directly guide the construction of ecological compensation mechanisms in different river basins under the same water system in the southwest region, ensuring that the mechanisms are systematic and comparable.

[0078] In a second aspect, the present invention also provides a watershed horizontal ecological protection compensation classification standard matching system, including: The watershed attribute determination module is configured to determine the level of the watershed based on the watershed area index, and to determine the classification of the river using a self-organizing feature mapping neural network based on the hydrological characteristic data, ecological characteristic data, topographic characteristic data, and socio-economic characteristic data of the watershed to obtain different classifications of the current watershed; The compensation calculation module is configured to determine the compensation scale of the current watershed based on the classification of the current watershed, and determine the upper limit of the overall scale of the ecological compensation fund for the current watershed based on the compensation scale; establish a mapping relationship data table between different watershed classifications and compensation measures, and index the corresponding compensation measures in the mapping relationship data table based on the classification of the current watershed; The output module is configured to save the obtained compensation scale and compensation measures as final results for output.

[0079] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0080] If the integrated unit is implemented as a software functional unit and sold or used as a standalone product, it can be stored on a computer-readable storage medium. This computer software product, stored on a storage medium, includes instructions for causing a computer device (which may be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of the various embodiments of the present invention. Such storage media include various media capable of storing program code, such as USB flash drives, removable hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0081] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. The method for matching the grading and classification standards of watershed horizontal ecological protection compensation is characterized by: include: Using the water system hierarchy method combined with the basin area index to classify the basin and determine the level of the basin, the compensation scale of the current basin is obtained based on the current basin classification, and the upper limit of the overall scale of the ecological compensation fund for the current basin is determined based on the compensation scale; Combining the hydrological characteristic data, ecological characteristic data, topographic characteristic data and socio-economic characteristic data of the watershed, the self-organizing feature mapping neural network model is used to classify the current watershed and obtain different classifications of the current watershed; Establish a mapping relationship data table between different watershed classifications and compensation measures, and index the corresponding compensation mechanism measures in the mapping relationship data table according to the current watershed classification; The obtained compensation scale and compensation mechanism measures are saved as the final results for output.

2. The method for matching the grading and classification standards of watershed horizontal ecological protection compensation according to claim 1 is characterized in that: The aforementioned classification of watersheds and determination of their levels using the water system hierarchy method combined with watershed area indicators includes: The current watershed is naturally graded using the water system hierarchy method to obtain an initial grade; Obtain the first area of the current watershed and the second area of the same-level watershed in historical data. If the first area is larger than the second area, raise the current initial level to the next level with a larger compensation scale. If the first area is smaller than the second area, the current initial level is reduced to the next level with a smaller compensation scale; If the first area is equal to the second area, the current initial level remains unchanged.

3. The method for matching the grading and classification standards of watershed horizontal ecological protection compensation according to claim 2 is characterized in that: The hydrological characteristic data include river flow data, river length data and basin area data of the basin; the ecological characteristic data include normalized difference vegetation index, annual precipitation data, water conservation service data; the terrain characteristic data include elevation data, slope data, and terrain undulation data; the socio-economic characteristic data include population data and economic data of the administrative regions through which the basin passes.

4. The method for matching the grading and classification standards of watershed horizontal ecological protection compensation according to claim 3 is characterized in that: The water conservation service data includes: Where, is the annual water yield on the time grid for land use type, is the actual annual average evapotranspiration, is the average annual precipitation of the grid.

5. The method for matching grading and classification standards for watershed horizontal ecological protection compensation according to claim 4 is characterized in that: The slope data includes: in, is the slope, and The DEM grid cells are and Directional elevation gradient; The terrain relief data includes: in, is the terrain relief, is the number of grid cells in the DEM data; It is the elevation difference of each grid, which represents the elevation difference between a grid and its adjacent grids.

6. The method for matching the grading and classification standards of watershed horizontal ecological protection compensation according to claim 4 is characterized in that: The self-organizing feature map neural network model is used to classify the current watershed, including: Set judgment thresholds for different classifications, obtain hydrological characteristic data, ecological characteristic data, topographic characteristic data, and socioeconomic characteristic data, and perform normalization preprocessing; Construct a SOFM neural network model, determine the input layer and output layer, and calculate the Euclidean distance between input neurons; Select the minimum distance and the winning neuron as the best matching unit, and output the current matching result.

7. The method for matching grading and classification standards for watershed horizontal ecological protection compensation according to claim 4 is characterized in that: The compensation scale of the current basin obtained according to the classification of the current basin includes: Setting up first-level river basins, second-level river basins to n-level river basins, with the compensation scales of the first-level river basins, second-level river basins to n-level river basins decreasing in sequence; Determine the compensation scale for the first-level watershed: Determine the compensation scale for secondary watersheds: Determine the compensation scale for the n-level watershed: Where, is the compensation scale of the first-level basin, is the basic coefficient, is the first-level watershed area, 、 and Calculate the weights respectively. is the hydrological regulation coefficient, is the ecological sensitivity coefficient, is the economic coefficient, is the median of the average flow of the same level basin in several years in historical data, is the actual flow of the current first-level basin, is the NDVI data of the current first-level watershed, is the compensation scale of the secondary watershed, is the area of the secondary watershed, is the functional condition factor, is the compensation scale of the n-level watershed, is the area of the n-level watershed, is the type connection coefficient, is the type unit price, is the total GDP value of the upper reaches of the current basin, It is the total GDP value of the current downstream of the basin.

8. The method for matching grading and classification standards for watershed horizontal ecological protection compensation according to claim 7 is characterized in that: The functional condition factors include: Determine whether the current watershed is a water source or ecological protection area. If so, If not, determine whether the current basin is an important agricultural settlement area or a population settlement area. If so, then 1.2, if not, then ; The watershed types include ecological tributary type, plain water-rich type, mountain ecological type and regional balanced type. The type connection coefficients corresponding to the watershed types are 1.2, 1.0, 1.1 and 0.9 respectively.

9. The basin horizontal ecological protection compensation classification standard matching system is characterized by: include: A classification module is configured to classify the watershed using a water system hierarchy method combined with a watershed area index and determine the level of the watershed, obtain the compensation scale of the current watershed based on the current watershed classification, and determine the upper limit of the overall scale of the ecological compensation fund for the current watershed based on the compensation scale; A classification module, combining the hydrological characteristic data, ecological characteristic data, topographic characteristic data and socio-economic characteristic data of the watershed, using a self-organizing feature mapping neural network model to classify the current watershed, obtain different classifications of the current watershed, establish a mapping relationship data table between different watershed classifications and compensation measures, and index the corresponding compensation measures in the mapping relationship data table according to the classification of the current watershed; The output module is configured to save the obtained compensation scale and compensation measures as final results for output.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the basin horizontal ecological protection compensation grading and classification standard matching method according to any one of claims 1 to 8.

Images