Method for identifying influence threshold of urbanization on supply and demand of ecological function of coastal wetland

Abstract

The present application relates to the field of geographic information analysis, and particularly relates to a cityization influence threshold identification method and device for supply and demand of coastal wetland ecological function, based on spatial position coordinate data of a plurality of grid units in a coastal area, a cityization level comprehensive index, and a supply and demand ratio of comprehensive ecological function of the coastal wetland, a preset initial spatial weighted threshold identification model is fitted to identify a critical point of the cityization level on the supply and demand relationship change of the ecological function of the coastal wetland, to reflect the stage response characteristics of the supply and demand of the ecological function of the coastal wetland to the cityization and the spatial heterogeneity characteristics of the cityization influence mechanism in different geographic units, to effectively identify the spatial difference threshold characteristics of the supply and demand relationship of the ecological function of the coastal wetland in the response process to the cityization, and to provide a refined and partitioned scientific basis for regional ecological management strategies.

Description

Technical Field

[0001] This invention relates to the field of geographic information analysis, and in particular to a method, apparatus, computer equipment, and storage medium for identifying the threshold of the impact of urbanization on the supply and demand of ecological functions in coastal wetlands. Background Technology

[0002] The significant advancement of urbanization in coastal areas has reshaped the supply and demand relationship and spatial pattern of coastal wetland ecological functions. As a nonlinear, multi-steady-state complex system, coastal wetlands may exhibit a threshold effect in their response to urbanization; that is, when the urbanization level exceeds a certain threshold, the supply and demand relationship of their ecological functions will change significantly. It is worth noting that due to the significant spatial heterogeneity of urbanization, this threshold effect may exhibit spatially differentiated characteristics in regions with different urbanization levels. Some areas may experience abrupt changes in the supply and demand relationship of ecological functions at relatively low urbanization levels, while other areas may show greater resilience.

[0003] Current threshold identification methods, such as piecewise linear regression models, can effectively capture the global threshold characteristics of ecological processes, but they still have shortcomings in spatial representation and are difficult to reveal the spatial differentiation patterns of threshold effects. Summary of the Invention

[0004] Based on this, the purpose of this invention is to provide a method, apparatus, computer equipment, and storage medium for identifying the threshold of the impact of urbanization on the supply and demand of coastal wetland ecological functions. Based on the spatial coordinate data of several grid units in a coastal area, the comprehensive index of urbanization level, and the supply and demand ratio of the comprehensive ecological functions of coastal wetlands, a preset initial spatial weighted threshold identification model is fitted to identify the critical point of change in the supply and demand relationship of coastal wetland ecological functions caused by urbanization. This reflects the phased response characteristics of the supply and demand of coastal wetland ecological functions to urbanization and the spatial heterogeneity of the urbanization impact mechanism in different geographical units. This effectively identifies the spatially differentiated threshold characteristics of the supply and demand relationship of coastal wetland ecological functions in response to urbanization, providing a refined and regionalized scientific basis for regional ecological management strategies.

[0005] In a first aspect, embodiments of this application provide a method for identifying the threshold of the impact of urbanization on the supply and demand of coastal wetland ecological functions, comprising the following steps:

[0006] Obtain a coastal area and its impact data, wherein the coastal area comprises a plurality of grid cells, and the impact data comprises the impact data of the plurality of grid cells;

[0007] Based on the aforementioned impact data, an assessment of urbanization level and a supply-demand ratio of coastal wetland ecological functions are conducted to obtain the comprehensive urbanization level index and the comprehensive ecological function supply-demand ratio of coastal wetlands for each grid unit.

[0008] Taking each of the aforementioned grid cells as the center, obtain several neighboring grid cells corresponding to each of the aforementioned grid cells, and construct a neighboring grid cell dataset for each of the aforementioned grid cells. The neighboring grid cell dataset includes the spatial location coordinates of several neighboring grid cells, the comprehensive index of urbanization level, and the supply and demand ratio of the comprehensive ecological function of coastal wetlands.

[0009] The neighboring raster cell datasets and spatial location weight matrix of each raster cell are input into a preset initial spatial weighted threshold recognition model for model fitting to obtain the corresponding target spatial weighted threshold recognition model for each raster cell. The initial spatial weighted threshold recognition model is a piecewise linear regression model constructed using the urbanization level comprehensive index and spatial location coordinate data as independent variables, the supply-demand ratio of coastal wetland ecological functions as the dependent variable, and spatial threshold parameters and regression coefficients. The spatial threshold parameters indicate the change in the direction of urbanization's influence on the supply-demand relationship of coastal wetland ecological functions; the regression coefficients indicate the intensity and direction of the influence of changes in urbanization level on the supply-demand ratio of coastal wetland ecological functions.

[0010] Impact analysis is performed based on the spatial threshold parameters and regression coefficients of the target spatial weighted threshold identification model for each grid unit to obtain the results of the urbanization impact analysis.

[0011] Secondly, embodiments of this application provide a threshold identification device for the impact of urbanization on the supply and demand of coastal wetland ecological functions, including:

[0012] An impact data acquisition module is used to acquire a coastal area and its impact data, wherein the coastal area includes a plurality of raster cells, and the impact data includes the impact data of the plurality of raster cells.

[0013] The data evaluation module is used to evaluate the urbanization level and the supply and demand of the ecological functions of coastal wetlands based on the impact data, and to obtain the comprehensive urbanization index and the supply and demand ratio of the comprehensive ecological functions of coastal wetlands for each grid unit.

[0014] The dataset construction module is used to obtain several neighboring raster units corresponding to each of the raster units as the center, and construct the neighboring raster unit dataset of each of the raster units. The neighboring raster unit dataset includes the spatial location coordinate data of several neighboring raster units, the comprehensive index of urbanization level, and the supply and demand ratio of the comprehensive ecological function of coastal wetlands.

[0015] The model fitting module is used to input the neighboring raster cell datasets and spatial location weight matrix of each raster cell into a preset initial spatial weighted threshold recognition model for model fitting, thereby obtaining the corresponding target spatial weighted threshold recognition model for each raster cell. The initial spatial weighted threshold recognition model is a piecewise linear regression model constructed using the urbanization level comprehensive index and spatial location coordinate data as independent variables, the supply-demand ratio of coastal wetland ecological functions as the dependent variable, and spatial threshold parameters and regression coefficients. The spatial threshold parameters indicate the change in the direction of urbanization's influence on the supply-demand relationship of coastal wetland ecological functions; the regression coefficients indicate the intensity and direction of the influence of changes in urbanization level on the supply-demand ratio of coastal wetland ecological functions.

[0016] The impact analysis module is used to perform impact analysis based on the spatial threshold parameters and regression coefficients of the identification model corresponding to the target spatial weighted threshold of each grid cell, and to obtain the results of the urbanization impact analysis.

[0017] Thirdly, embodiments of this application provide a computer device, including a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the threshold identification method for the impact of urbanization on the supply and demand of coastal wetland ecological functions as described in the first aspect.

[0018] Fourthly, embodiments of this application provide a storage medium storing a computer program, which, when executed by a processor, implements the steps of the threshold identification method for the impact of urbanization on the supply and demand of coastal wetland ecological functions as described in the first aspect.

[0019] This application provides a method, apparatus, computer equipment, and storage medium for identifying the threshold of the impact of urbanization on the supply and demand of coastal wetland ecological functions. Based on the spatial coordinate data of several grid units in the coastal area, the comprehensive index of urbanization level, and the supply and demand ratio of the comprehensive ecological functions of coastal wetlands, a preset initial spatial weighted threshold identification model is fitted to identify the critical point of the change in the supply and demand relationship of coastal wetland ecological functions caused by urbanization level. This reflects the stage-by-stage response characteristics of the supply and demand of coastal wetland ecological functions to urbanization and the spatial heterogeneity of the urbanization impact mechanism in different geographical units. This effectively identifies the spatial difference threshold characteristics of the supply and demand relationship of coastal wetland ecological functions in response to urbanization, providing a refined and regionalized scientific basis for regional ecological management strategies.

[0020] To better understand and implement this invention, the following detailed description is provided in conjunction with the accompanying drawings. Attached Figure Description

[0021] Figure 1A flowchart illustrating a method for identifying the threshold of the impact of urbanization on the supply and demand of coastal wetland ecological functions, provided in one embodiment of this application;

[0022] Figure 2 This is a schematic diagram of step S2 in the process of the method for identifying the threshold of the impact of urbanization on the supply and demand of coastal wetland ecological functions, provided in one embodiment of this application.

[0023] Figure 3 This is a schematic diagram of step S4 in the process of the method for identifying the threshold of the impact of urbanization on the supply and demand of coastal wetland ecological functions, provided in one embodiment of this application.

[0024] Figure 4 This is a schematic diagram of step S42 in the process of the method for identifying the threshold of the impact of urbanization on the supply and demand of coastal wetland ecological functions according to an embodiment of this application;

[0025] Figure 5 This is a schematic diagram of step S5 in the process of the method for identifying the threshold of the impact of urbanization on the supply and demand of coastal wetland ecological functions according to an embodiment of this application;

[0026] Figure 6 A schematic diagram of step S5 in the process of the method for identifying the threshold of the impact of urbanization on the supply and demand of coastal wetland ecological functions, provided in another embodiment of this application;

[0027] Figure 7 A schematic diagram of the structure of a threshold identification device for the impact of urbanization on the supply and demand of coastal wetland ecological functions, provided in one embodiment of this application;

[0028] Figure 8 This is a schematic diagram of the structure of a computer device provided in one embodiment of this application. Detailed Implementation

[0029] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0030] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0031] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0032] Please see Figure 1 , Figure 1 This is a flowchart illustrating a method for identifying the threshold of the impact of urbanization on the supply and demand of coastal wetland ecological functions, provided in one embodiment of this application. The method includes the following steps:

[0033] S1: Obtain the coastal area and the impact data of the coastal area.

[0034] The execution subject of the method for identifying the threshold of the impact of urbanization on the supply and demand of coastal wetland ecological functions is the analysis device of the method for identifying the threshold of the impact of urbanization on the supply and demand of coastal wetland ecological functions (hereinafter referred to as the analysis device). In an optional embodiment, the analysis device may be a computer device, a server, or a server cluster composed of multiple computer devices.

[0035] In this embodiment, the analysis device can obtain coastal areas and their impact data from a preset database. The coastal areas comprise several grid cells, and the impact data includes the impact data of each grid cell. The impact data includes meteorological data, population density data, regional GDP data, built-up land data, carbon density data, carbon emission data, water consumption data, and water quality standard data.

[0036] Meteorological data reflects rainfall, sunshine, and temperature data, while population density reflects the quantity of demand for ecosystem services. The higher the population density, the greater the total demand for services. Regional GDP data reflects the affluence of a region and can indirectly reflect the level of human preference for enjoying ecosystem services. The stronger the regional economy, the higher the expected ecosystem services.

[0037] S2: Based on the impact data, conduct an assessment of urbanization level and a supply and demand assessment of coastal wetland ecological functions to obtain the comprehensive urbanization level index and the supply and demand ratio of comprehensive ecological functions of coastal wetlands for each grid unit.

[0038] In this embodiment, the analysis device performs an assessment of urbanization level and a supply and demand assessment of coastal wetland ecological functions based on the impact data, and obtains the comprehensive urbanization level index and the supply and demand ratio of comprehensive ecological functions of coastal wetlands for each grid unit.

[0039] Specifically, the analysis equipment performs a supply and demand assessment of the ecological functions of coastal wetlands based on the impact data and a preset comprehensive assessment model of ecosystem services and trade-offs, obtains supply and demand data of the ecological functions of coastal wetlands of several types for several grid units, calculates the supply and demand ratio based on the supply and demand data of the ecological functions of coastal wetlands of several types for several grid units, and obtains the comprehensive ecological function supply and demand ratio of coastal wetlands for each grid unit.

[0040] The integrated assessment model for ecosystem services and trade-offs is InVEST (Integrated Valuation of Ecosystem Services and Trade-offs), which integrates various biophysical and socioeconomic data to provide a range of ecosystem service assessment capabilities.

[0041] The aforementioned supply and demand data for several types of coastal wetland ecological functions include carbon sequestration supply and demand data, water resource supply and demand data, and water purification supply and demand data. Please refer to [link / reference needed]. Figure 2 , Figure 2 This is a schematic diagram of step S2 in the process of the method for identifying the threshold of the supply and demand impact of urbanization on the ecological function of coastal wetlands according to an embodiment of this application, including steps S21 to S25, as follows:

[0042] S21: The urbanization level comprehensive index of each grid cell is obtained by averaging the population density data, regional GDP data and building land data of the same grid cell.

[0043] In this embodiment, the analysis device performs cumulative averaging processing on the population density data, regional GDP data, and built-up land data of the same grid cell to obtain a comprehensive urbanization level index for each grid cell, wherein the comprehensive urbanization level index is:

[0044]

[0045] In the formula, CUI is the comprehensive urbanization index, PU is the population density data, EU is the regional GDP data, and LU is the building land data.

[0046] S22: Calculate the supply and demand data of carbon sequestration function based on the carbon density data, carbon emission data and population density data of the same grid cell to obtain the supply and demand data of carbon sequestration function for each grid cell.

[0047] In this embodiment, the analysis device calculates the supply and demand data for carbon sequestration based on the carbon density data, carbon emission data, and population density data of the same grid cell, obtaining the supply and demand data for carbon sequestration for each grid cell. The carbon density data includes aboveground biomass carbon density data, belowground biomass carbon density data, soil carbon density data, and dead organic matter carbon density data. The supply and demand data for carbon sequestration includes carbon sequestration supply data and carbon sequestration demand data. The carbon sequestration supply data is as follows:

[0048] CS tot =CS above +CS below +CS soil +CS dead

[0049] In the formula, CS tot Provide data for carbon sequestration function, CS above For aboveground biomass carbon density data, CS below For underground biogenic carbon density data, CS soil For soil carbon density data, CS dead This is data on the carbon density of dead organic matter.

[0050] The carbon emission data includes per capita carbon emission data, and the carbon sequestration function requirement data is as follows:

[0051] D cs =C p ×ρ i

[0052] In the formula, D cs For carbon sequestration function requirement data, C p For per capita carbon emissions data, ρ i This represents the population density data for the i-th raster cell.

[0053] S23: Calculate water supply and demand data for each grid cell based on meteorological data, water consumption data, and population density data.

[0054] In this embodiment, the analysis device calculates water supply and demand data for each grid cell based on meteorological data, water consumption data, and population density data. The meteorological data includes annual actual evapotranspiration and annual precipitation, and the water supply and demand data includes water supply data and water demand data. The water supply data is as follows:

[0055]

[0056] In the formula, WY represents water resource supply data, and AET... i Let P be the annual actual evapotranspiration of the i-th grid cell. i Let be the annual precipitation of the i-th grid cell.

[0057] The water consumption data includes per capita agricultural irrigation water consumption, per capita domestic water consumption, and per capita industrial water consumption. The water demand data is as follows:

[0058] D wy =ρ i ×(W agr +W dom +W ind )

[0059] In the formula, D wy For water resource demand data, W agr W represents the per capita agricultural irrigation water consumption. dom W represents the average domestic water consumption per capita. ind This refers to the per capita industrial water consumption.

[0060] S24: Calculate the water quality purification supply and demand data based on the water quality standard data of each grid unit to obtain the water quality purification supply and demand data of each grid unit.

[0061] In this embodiment, the analysis device calculates the water purification supply and demand data for each grid cell based on the water quality standard data of each grid cell, thereby obtaining the water purification supply and demand data for each grid cell. The water quality standard data includes pollutant load values ​​and pollutant output rates, and the water purification supply and demand data includes water purification supply data and water purification demand data. The water purification supply data is as follows:

[0062] WP = ALV i ×(1-E)

[0063] In the formula, WP represents the water purification supply data, and ALV represents the water quality purification supply data. i Let E be the pollutant load value of the i-th grid cell, and E be the pollutant output rate.

[0064] The water quality standard data also includes water production and the amount of pollutants allowed to be discharged under the water quality standards. The water purification requirement data is as follows:

[0065] D wp =Y i ×Q

[0066] In the formula, D wp Y provides data for water purification i Let Q be the water production of the i-th grid cell, and let Q be the amount of pollutants allowed to be discharged under the water quality standard.

[0067] S25: Based on the carbon sequestration supply and demand data, water resource supply and demand data, water purification supply and demand data of several grid cells, and a preset algorithm for calculating the supply and demand ratio of the comprehensive ecological function of coastal wetlands, obtain the supply and demand ratio of the comprehensive ecological function of coastal wetlands for each grid cell.

[0068] In this embodiment, the analysis device obtains the supply-demand ratio of the comprehensive ecological function of the coastal wetland for each of the several grid cells based on the carbon sequestration supply-demand data, water resource supply-demand data, water purification supply-demand data, and a preset algorithm for calculating the supply-demand ratio of the comprehensive ecological function of the coastal wetland. The supply-demand ratio of the comprehensive ecological function of the coastal wetland is as follows:

[0069]

[0070] In the formula, CESD i S represents the supply-demand ratio of the comprehensive ecological function of the coastal wetland in the i-th grid cell. i，l To provide data on the coastal wetland ecological function of the l-th type in the i-th raster cell, D i，l This represents the coastal wetland ecological function requirement data for the l-th type of the i-th raster cell, where L is the number of types.

[0071] S3: Taking each of the grid cells as the center, obtain several neighboring grid cells corresponding to each grid cell, and construct a neighboring grid cell dataset for each grid cell.

[0072] In this embodiment, the analysis device takes each of the grid cells as the center and obtains several neighboring grid cells corresponding to each grid cell, and constructs a neighboring grid cell dataset for each grid cell. The neighboring grid cell dataset includes the spatial location coordinates of several neighboring grid cells, the comprehensive urbanization level index, and the supply and demand ratio of the comprehensive ecological function of the coastal wetland.

[0073] S4: Input the neighboring grid cell datasets and spatial location weight matrix of each grid cell into the preset initial spatial weighted threshold recognition model for model fitting, and obtain the corresponding target spatial weighted threshold recognition model for each grid cell.

[0074] In this embodiment, the analysis device inputs the neighboring grid cell datasets and spatial location weight matrix of each grid cell into a preset initial spatial weighted threshold identification model for model fitting, thereby obtaining the corresponding target spatial weighted threshold identification model for each grid cell. The initial spatial weighted threshold identification model is a piecewise linear regression model constructed by using the comprehensive urbanization level index and spatial location coordinate data as independent variables, the supply and demand ratio of coastal wetland ecological functions as the dependent variable, and combining spatial threshold parameters and regression coefficients.

[0075] Specifically, the initial spatial weighted threshold identification model is constructed based on a piecewise linear regression model, incorporating spatial weight coefficients by combining the spatial location information of grid cells. It obtains the optimal spatial threshold parameters through dynamic search and fits the regression coefficients of the corresponding stages. The spatial threshold parameters indicate changes in the direction of urbanization's impact on the supply and demand relationship of coastal wetland ecological functions. Specifically, the spatial threshold parameters indicate key turning points where significant changes occur in the impact of urbanization on the supply and demand relationship of coastal wetland ecological functions within a local spatial range. The regression coefficients indicate the intensity and direction of the impact of changes in urbanization levels on the supply and demand ratio of coastal wetland ecological functions. The initial spatial weighted threshold identification model is as follows:

[0076]

[0077] In the formula, y i Let u be the supply-demand ratio of coastal wetland ecological functions for the i-th grid cell. i v i x represents the spatial coordinates of the i-th raster cell. i Let β0(u) be the comprehensive index of urbanization level of the i-th grid cell. i v i ) represents the spatial coordinate data of the i-th raster cell (u i v i The regression intercept term, β1(u) i v i ) represents the spatial coordinate data of the i-th raster cell (u i v i The pre-threshold regression coefficients of β2(u) i v i ) represents the spatial coordinate data of the i-th raster cell (u i v i The threshold regression coefficients, ∈ i For the error term of the i-th grid cell, (u i u i x represents the spatial coordinates of the i-th raster cell. i Let T(u) be the comprehensive index of urbanization level of the i-th grid cell. i ,v i ) represents the spatial coordinate data of the i-th raster cell (u i ,v i Spatial threshold parameter.

[0078] Based on the traditional piecewise linear regression model, a geospatial weighting function is introduced to construct an initial spatial weighted threshold identification model, which is used to reveal the spatial heterogeneity of the nonlinear impact of urbanization on the supply and demand relationship of coastal wetland ecological functions. This initial spatial weighted threshold identification model effectively considers the autocorrelation and local variability in spatial data by introducing a spatial weighting function, thereby improving the scientific rigor and spatial adaptability of threshold identification. In this initial spatial weighted threshold identification model, each set of regression parameters is adjusted according to spatial location. The analysis device inputs the neighboring raster unit datasets and the spatial location weight matrix of each raster unit into the preset initial spatial weighted threshold identification model for model fitting, thereby identifying whether there is a consistent critical point for the change in the supply and demand relationship of coastal wetland ecological functions due to urbanization. This reflects the stage-wise response characteristics of the supply and demand of coastal wetland ecological functions to urbanization, as well as the spatial heterogeneity of the urbanization impact mechanism in different geographical units. This effectively identifies the spatially differentiated threshold characteristics of the supply and demand relationship of coastal wetland ecological functions in response to urbanization, providing a refined and regionalized scientific basis for regional ecological management strategies.

[0079] Please see Figure 3 , Figure 3 This is a schematic diagram of step S4 in the process of the method for identifying the threshold of the supply and demand impact of urbanization on the ecological function of coastal wetlands according to an embodiment of this application, including steps S41 to S42, as follows:

[0080] S41: Extract several comprehensive urbanization level indices of neighboring raster units from the neighboring raster unit dataset of the raster unit as candidate spatial threshold parameters of the initial spatial weighted threshold identification model. Fit the initial spatial weighted threshold identification model according to the neighboring raster unit dataset of the raster unit and several candidate spatial threshold parameters to obtain several candidate spatial weighted threshold identification models corresponding to several raster units.

[0081] In this embodiment, the analysis device extracts several comprehensive urbanization level indices of neighboring grid cells from the neighboring grid cell dataset of the grid cell, which are respectively used as candidate spatial threshold parameters of the initial spatial weighted threshold identification model. Based on the neighboring grid cell dataset of the grid cell and the several candidate spatial threshold parameters, the initial spatial weighted threshold identification model is fitted to obtain several candidate spatial weighted threshold identification models corresponding to several grid cells.

[0082] S42: Based on the candidate space threshold parameters and candidate regression coefficients of the candidate space weighted threshold identification model, determine the target space weighted threshold identification model from several candidate space weighted threshold identification models, and obtain the target space weighted threshold identification model corresponding to each grid cell.

[0083] In this embodiment, the analysis device extracts several comprehensive urbanization level indices of neighboring grid cells from the neighboring grid cell dataset of the grid cell, which are respectively used as candidate spatial threshold parameters of the initial spatial weighted threshold identification model. Based on the neighboring grid cell dataset of the grid cell and the several candidate spatial threshold parameters, the initial spatial weighted threshold identification model is fitted to obtain several candidate spatial weighted threshold identification models corresponding to several grid cells.

[0084] Please see Figure 4 , Figure 4 The schematic diagram of step S42 in the method for identifying the threshold of the impact of urbanization on the supply and demand of coastal wetland ecological functions according to an embodiment of this application includes steps S421 to S424, as follows:

[0085] S421: Based on the comprehensive urbanization level index and spatial location coordinate data in the neighboring grid cell dataset of the grid cell, obtain the predicted supply-demand ratio of the coastal wetland ecological function of the neighboring grid cells output by the corresponding candidate spatial weighted threshold identification model of the grid cell.

[0086] In this embodiment, the analysis device obtains the predicted supply-demand ratio of coastal wetland ecological function of several neighboring grid cells, output by several candidate spatial weighted threshold identification models of the grid cell, based on the comprehensive urbanization level index and spatial location coordinate data in the neighboring grid cell dataset of the grid cell.

[0087] S422: Construct a local spatial position weight matrix for the grid cell based on the spatial position coordinate data of several neighboring grid cells in the grid cell and its neighboring grid cell dataset.

[0088] In this embodiment, the analysis device constructs a spatial position weight matrix for the grid cell based on the spatial position coordinate data of the grid cell and several neighboring grid cells in the dataset of the grid cell. The local spatial position weight matrix includes local spatial position weight parameters between the grid cell and its neighboring grid cells. The local spatial position weight parameters are:

[0089]

[0090] In the formula, w ij Let d be the local spatial location weight parameter between the i-th grid cell and its j-th neighboring grid cell. ijis the distance parameter between the i-th grid cell and the j-th neighboring grid cell, which is calculated based on the spatial coordinate data of the grid cell and its neighboring grid cells. b is the bandwidth parameter used to control the attenuation rate.

[0091] S423: Based on the spatial location weight matrix of the raster cell, the supply-demand ratio of coastal wetland ecological function of several neighboring raster cells in the dataset of neighboring raster cells of the raster cell, and the predicted supply-demand ratio of coastal wetland ecological function of several neighboring raster cells output by several candidate spatial weighted threshold identification models of the raster cell, a weighted residual sum of squares is calculated to obtain the corresponding weighted residual sum of squares of several candidate spatial weighted threshold identification models of the raster cell.

[0092] In this embodiment, the analysis device calculates a weighted residual sum of squares based on the spatial location weight matrix of the grid cell, the supply-demand ratio of the coastal wetland ecological function of several neighboring grid cells in the dataset of neighboring grid cells of the grid cell, and the predicted supply-demand ratio of the coastal wetland ecological function of several neighboring grid cells output by several candidate spatial weighted threshold identification models corresponding to the grid cell. This yields the corresponding weighted residual sum of squares for the several candidate spatial weighted threshold identification models of the grid cell. The weighted residual sum of squares is:

[0093]

[0094] In the formula, WRSS(T) is the weighted residual sum of squares corresponding to the candidate spatial weighted threshold recognition model for the raster cell, and y j The predicted supply-demand ratio of coastal wetland ecological function is used as the output of the j-th neighbor raster cell of the candidate spatial weighted threshold identification model. The supply and demand ratio of coastal wetland ecological functions for the j-th neighboring grid cell.

[0095] S424: Based on the weighted residual sum of squares of several candidate spatial weighted threshold identification models corresponding to the grid cell, determine the target spatial weighted threshold identification model from the several candidate spatial weighted threshold identification models, and obtain the target spatial weighted threshold identification model corresponding to each grid cell.

[0096] In this embodiment, the analysis device determines the candidate spatial weighted threshold identification model corresponding to the minimum weighted residual square sum based on the weighted residual square sum of several candidate spatial weighted threshold identification models corresponding to the grid cell, and then determines the target spatial weighted threshold identification model from the several candidate spatial weighted threshold identification models to obtain the target spatial weighted threshold identification model corresponding to each grid cell.

[0097] S5: Based on the spatial threshold parameters and regression coefficients of the target spatial weighted threshold identification model for each grid unit, an impact analysis is performed to obtain the results of the urbanization impact analysis.

[0098] In this embodiment, the analysis device performs an impact analysis based on the spatial threshold parameters and regression coefficients of the target spatial weighted threshold identification model for each grid cell, and obtains the results of the urbanization impact analysis.

[0099] The urbanization impact analysis results include a spatial clustering map of urbanization impact thresholds, which indicates regions within the coastal area with varying degrees of sensitivity to the key impacts of urbanization on the supply and demand of coastal wetland ecological functions; please refer to [link / reference]. Figure 5 , Figure 5 This is a schematic diagram of step S5 in the process of the method for identifying the threshold of the supply and demand impact of urbanization on the ecological function of coastal wetlands according to an embodiment of this application, including steps S51 to S52, as follows:

[0100] S51: Based on the spatial coordinate data of several grid cells, construct a global spatial position weight matrix for several grid cells; perform cluster analysis based on the spatial threshold parameters in the target spatial weighted threshold identification model of each grid cell and the global spatial position weight matrix to obtain the spatial threshold clustering index of several grid cells.

[0101] In this embodiment, the analysis device constructs a global spatial position weight matrix for several grid cells based on their spatial coordinate data. The global spatial position weight matrix includes global spatial position weight parameters between the grid cell and its neighboring grid cells. The global spatial position weight parameters are:

[0102]

[0103] In the formula, θ ik Let d be the global spatial position weight parameter between the i-th grid cell and the k-th grid cell. ik is the distance parameter between the i-th grid cell and the k-th grid cell, which is calculated based on the grid cell and its spatial coordinate data. b is the bandwidth parameter used to control the attenuation rate.

[0104] In this embodiment, the analysis device constructs a global spatial position weight matrix for several grid cells based on their spatial coordinate data; and performs cluster analysis based on the spatial threshold parameters in the target spatial weighted threshold identification model for each grid cell and the global spatial position weight matrix to obtain a spatial threshold clustering index for several grid cells, wherein the spatial threshold clustering index is:

[0105]

[0106] In the formula, Let be the spatial threshold clustering index for the i-th raster cell, and n be the number of raster cells. S is the mean of the spatial threshold parameters in the corresponding target space weighted threshold recognition model of the raster cell.

[0107] By calculating the spatial threshold parameters in the corresponding target spatial weighted threshold identification model of the grid cell, and combining the global spatial location weight matrix, the average threshold level of each grid cell and its neighborhood is quantified to be significantly higher or lower than the global average. This demonstrates the local threshold characteristics of the supply and demand relationship of urbanization on the ecological function of coastal wetlands, and presents the spatial differentiation pattern of the threshold impact of urbanization in different regions.

[0108] S52: Based on the spatial threshold clustering index of several grid units and the preset clustering index threshold, the several grid units are divided into high-value clustering units and low-value clustering units to construct a spatial clustering map of the urbanization impact threshold of the coastal area.

[0109] In this embodiment, the analysis device determines the corresponding grid cell as a high-value clustering unit based on the spatial threshold clustering index of several grid cells and a preset clustering index threshold. If the spatial threshold clustering index is greater than the clustering index threshold, the corresponding grid cell is determined as a low-value clustering unit. The several grid cells are divided into high-value clustering units and low-value clustering units to construct a spatial clustering map of the urbanization impact threshold of the coastal area.

[0110] Based on the spatial clustering characteristics of each grid unit, a spatial clustering map of urbanization impact thresholds in the coastal area is constructed. By revealing the spatial clustering patterns of similar thresholds, it helps to identify highly sensitive areas where urbanization has a key impact on the supply and demand of coastal wetland ecological functions, providing a basis for regionally differentiated ecological regulation and early warning management.

[0111] The urbanization impact analysis results include a spatial classification map of urbanization impact patterns, which indicates the spatial heterogeneity of the impact patterns of urbanization on the ecological functions of coastal wetlands in the coastal area; the regression coefficients include pre-threshold regression coefficients and post-threshold regression coefficients; please refer to [link to relevant documentation]. Figure 6 , Figure 6 A schematic diagram of step S5 in the flowchart of the method for identifying the threshold of the supply and demand impact of urbanization on the ecological function of coastal wetlands provided in another embodiment of this application, including steps S53 to S54, as follows:

[0112] S53: Based on the regression coefficients of the target space weighted threshold recognition model corresponding to each of the grid units, the threshold type is determined, and the threshold type determination results corresponding to several of the grid units are obtained.

[0113] In this embodiment, the analysis device determines the threshold type based on the regression coefficients of the target space weighted threshold identification model corresponding to each of the grid cells, and obtains threshold type determination results for several of the grid cells. The threshold type determination results include abrupt threshold determination results and progressive threshold determination results.

[0114] Specifically, the analysis device multiplies the pre-threshold regression coefficient and post-threshold regression coefficient of the target space weighted threshold recognition model corresponding to the same grid cell. If the result of the product is negative, the threshold type judgment result is a mutation threshold judgment result; if the result of the product is positive, the threshold type judgment result is a progressive threshold judgment result.

[0115] S54: Based on the threshold type judgment results of several grid cells, the pre-threshold regression coefficient and post-threshold regression coefficient of the target space weighted threshold recognition model of the grid cells, obtain the influence pattern judgment results of several grid cells, and construct the spatial classification map of the urbanization influence pattern of the coastal area.

[0116] In this embodiment, the analysis device obtains the impact pattern judgment results corresponding to several grid cells based on the threshold type judgment results corresponding to several grid cells, the pre-threshold regression coefficient and the post-threshold regression coefficient of the target space weighted threshold recognition model corresponding to the grid cells, and constructs a spatial classification map of the urbanization impact pattern of the coastal area. The impact pattern judgment results include positive transformation impact results, negative transformation impact results, enhanced gradual impact results and weakened gradual impact results.

[0117] Specifically, if the threshold type judgment result of the grid cell is a mutation threshold judgment result, and the pre-threshold regression coefficient of the target space weighted threshold recognition model of the grid cell is less than 0, and the post-threshold regression coefficient of the target space weighted threshold recognition model of the grid cell is greater than 0, the analysis device judges that the influence mode judgment result of the grid cell is a positive transformation influence result.

[0118] If the threshold type judgment result of the corresponding grid cell is a mutation threshold judgment result, and the pre-threshold regression coefficient of the target space weighted threshold recognition model of the corresponding grid cell is greater than 0, and the post-threshold regression coefficient of the target space weighted threshold recognition model of the corresponding grid cell is less than 0, the analysis device judges that the influence mode judgment result of the corresponding grid cell is a negative transformation influence result.

[0119] If the threshold type judgment result of the grid cell is a progressive threshold judgment result, and the pre-threshold regression coefficient of the target space weighted threshold recognition model of the grid cell is greater than the post-threshold regression coefficient of the target space weighted threshold recognition model of the grid cell, the analysis device judges the influence mode judgment result of the grid cell to be an enhanced progressive influence result.

[0120] If the threshold type judgment result of the grid cell is a progressive threshold judgment result, and the pre-threshold regression coefficient of the target space weighted threshold recognition model of the grid cell is less than the post-threshold regression coefficient of the target space weighted threshold recognition model of the grid cell, the analysis device judges that the influence mode judgment result of the grid cell is a weakened progressive influence result.

[0121] Based on the impact pattern judgment results of each grid cell and the spatial coordinate location data, the analysis equipment constructs a spatial classification map of the urbanization impact pattern in the coastal area. This map visually demonstrates the spatial heterogeneity of the impact pattern of urbanization on the ecological function of coastal wetlands. It accurately describes the impact characteristics of urbanization on the ecological function of coastal wetlands at the grid cell level, and can precisely analyze the spatial heterogeneity characteristics of the supply and demand response of the ecological function of coastal wetlands, providing a scientific basis for formulating differentiated coastal ecological management strategies.

[0122] Please refer to Figure 7 , Figure 7 This is a schematic diagram of a threshold identification device for the impact of urbanization on the supply and demand of coastal wetland ecological functions, provided in one embodiment of this application. This device can be implemented in whole or in part through software, hardware, or a combination of both. The device 7 includes:

[0123] Impact data acquisition module 71 is used to acquire a coastal area and impact data of the coastal area, wherein the coastal area includes a number of grid cells, and the impact data includes the impact data of the number of grid cells.

[0124] Data evaluation module 72 is used to evaluate the urbanization level and the supply and demand of coastal wetland ecological functions based on the impact data, and to obtain the comprehensive urbanization level index and the supply and demand ratio of the comprehensive ecological functions of coastal wetlands for each grid unit.

[0125] The dataset construction module 73 is used to obtain several neighboring raster units corresponding to each of the raster units as centers, and construct the neighboring raster unit dataset of each of the raster units. The neighboring raster unit dataset includes the spatial location coordinate data of several neighboring raster units, the comprehensive index of urbanization level, and the supply and demand ratio of the comprehensive ecological function of coastal wetlands.

[0126] The model fitting module 74 is used to input the neighboring grid cell datasets and spatial location weight matrix of each grid cell into a preset initial spatial weighted threshold recognition model for model fitting, thereby obtaining the corresponding target spatial weighted threshold recognition model for each grid cell; wherein, the initial spatial weighted threshold recognition model is a piecewise linear regression model constructed by combining spatial threshold parameters and regression coefficients, with the urbanization level comprehensive index and spatial location coordinate data as independent variables and the supply-demand ratio of coastal wetland ecological functions as the dependent variable; the spatial threshold parameters are used to indicate the change in the direction of the impact of urbanization on the supply-demand relationship of coastal wetland ecological functions; the regression coefficients are used to indicate the intensity and direction of the impact of changes in urbanization level on the supply-demand ratio of coastal wetland ecological functions;

[0127] The impact analysis module 75 is used to perform impact analysis based on the spatial threshold parameters and regression coefficients of the identification model corresponding to the target spatial weighted threshold of each grid cell, and to obtain the results of the urbanization impact analysis.

[0128] In this embodiment, an impact data acquisition module obtains the coastal area and its impact data, wherein the coastal area comprises several raster cells, and the impact data comprises the impact data of several raster cells; a data evaluation module performs an urbanization level assessment and a supply-demand assessment of the coastal wetland ecological function based on the impact data, obtaining the comprehensive urbanization level index and the supply-demand ratio of the comprehensive ecological function of the coastal wetland for each raster cell; a dataset construction module obtains several neighboring raster cells corresponding to each raster cell as the center, constructing a neighboring raster cell dataset for each raster cell, wherein the neighboring raster cell dataset includes the spatial location coordinates of several neighboring raster cells, the comprehensive urbanization level index, and the supply-demand ratio of the comprehensive ecological function of the coastal wetland; and a model fitting module integrates the impact data of each raster cell into the model data. The neighboring raster cell datasets and spatial location weight matrix of each raster cell are input into a preset initial spatial weighted threshold identification model for model fitting, thereby obtaining the corresponding target spatial weighted threshold identification model for each raster cell. The initial spatial weighted threshold identification model is a piecewise linear regression model constructed using the comprehensive urbanization level index and spatial location coordinate data as independent variables, the supply-demand ratio of coastal wetland ecological functions as the dependent variable, and spatial threshold parameters and regression coefficients. The spatial threshold parameters indicate the change in the direction of urbanization's impact on the supply-demand relationship of coastal wetland ecological functions; the regression coefficients indicate the intensity and direction of the impact of changes in urbanization level on the supply-demand ratio of coastal wetland ecological functions. Through the impact analysis module, impact analysis is performed based on the spatial threshold parameters and regression coefficients of the corresponding target spatial weighted threshold identification model for each raster cell, yielding the urbanization impact analysis results. Based on the spatial coordinate data of several grid units in the coastal area, the comprehensive urbanization index, and the supply-demand ratio of the comprehensive ecological functions of coastal wetlands, a model is fitted to a preset initial spatial weighted threshold identification model to identify the critical point of change in the supply-demand relationship of coastal wetland ecological functions caused by urbanization. This reflects the phased response characteristics of the supply-demand relationship of coastal wetland ecological functions to urbanization and the spatial heterogeneity of the urbanization impact mechanism in different geographical units. This effectively identifies the spatial difference threshold characteristics of the supply-demand relationship of coastal wetland ecological functions in response to urbanization, providing a refined and zonal scientific basis for regional ecological management strategies.

[0129] Please refer to Figure 8 , Figure 8 This is a schematic diagram of the structure of a computer device provided in one embodiment of this application. The computer device 8 includes: a processor 81, a memory 82, and a computer program 83 stored in the memory 82 and executable on the processor 81; the computer device can store multiple instructions, which are adapted to be loaded and executed by the processor 81. Figures 1 to 6The method steps of the illustrated embodiment can be found in the following documentation for detailed execution. Figures 1 to 6 The specific details of the illustrated embodiments will not be elaborated here.

[0130] The processor 81 may include one or more processing cores. The processor 81 connects to various parts of the server using various interfaces and lines. It executes instructions, programs, code sets, or instruction sets stored in the memory 82, and retrieves data from the memory 82. It performs various functions and processes data for the urbanization impact threshold identification device 7. Optionally, the processor 81 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 81 may integrate one or more of the following: a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), and a modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content displayed on the touch screen; and the modem handles wireless communication. It is understood that the modem may also be implemented as a separate chip without being integrated into the processor 81.

[0131] The memory 82 may include random access memory (RAM) or read-only memory. Optionally, the memory 82 may include a non-transitory computer-readable storage medium. The memory 82 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 82 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch instructions), instructions for implementing the various method embodiments described above, etc.; the data storage area may store data involved in the various method embodiments described above, etc. Optionally, the memory 82 may also be at least one storage device located remotely from the aforementioned processor 81.

[0132] This application embodiment also provides a storage medium that can store multiple instructions, which are adapted to be loaded and executed by a processor as described above. Figures 1 to 6For specific steps and execution processes of the illustrated embodiment, please refer to [link / reference]. Figures 1 to 6 The specific details of the illustrated embodiments will not be elaborated here.

[0133] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0134] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0135] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the algorithm. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0136] In the embodiments provided by this invention, it should be understood that the disclosed apparatus / terminal devices and methods can be implemented in other ways. For example, the apparatus / terminal device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0137] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0138] Furthermore, in the various embodiments of the present invention, the functional units can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0139] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms.

[0140] This invention is not limited to the above-described embodiments. If any modifications or variations to this invention do not depart from the spirit and scope of this invention, and if such modifications and variations fall within the scope of the claims and equivalent technologies of this invention, then this invention also intends to include such modifications and variations.

Claims

1. A method for identifying the threshold of the impact of urbanization on the supply and demand of coastal wetland ecological functions, characterized in that, Includes the following steps: Obtain a coastal area and its impact data, wherein the coastal area comprises a plurality of grid cells, and the impact data comprises the impact data of the plurality of grid cells; Based on the aforementioned impact data, an assessment of urbanization level and a supply-demand ratio of coastal wetland ecological functions are conducted to obtain the comprehensive urbanization level index and the comprehensive ecological function supply-demand ratio of coastal wetlands for each grid unit. Taking each of the aforementioned grid cells as the center, obtain several neighboring grid cells corresponding to each of the aforementioned grid cells, and construct a neighboring grid cell dataset for each of the aforementioned grid cells. The neighboring grid cell dataset includes the spatial location coordinates of several neighboring grid cells, the comprehensive index of urbanization level, and the supply and demand ratio of the comprehensive ecological function of coastal wetlands. The neighboring raster cell datasets and spatial location weight matrix of each raster cell are input into a preset initial spatial weighted threshold recognition model for model fitting to obtain the corresponding target spatial weighted threshold recognition model for each raster cell. The initial spatial weighted threshold recognition model is a piecewise linear regression model constructed using the urbanization level comprehensive index and spatial location coordinate data as independent variables, the supply-demand ratio of coastal wetland ecological functions as the dependent variable, and spatial threshold parameters and regression coefficients. The spatial threshold parameters indicate the change in the direction of urbanization's influence on the supply-demand relationship of coastal wetland ecological functions; the regression coefficients indicate the intensity and direction of the influence of changes in urbanization level on the supply-demand ratio of coastal wetland ecological functions. Impact analysis is performed based on the spatial threshold parameters and regression coefficients of the target spatial weighted threshold identification model for each grid unit to obtain the results of the urbanization impact analysis.

2. The method for identifying the threshold of the impact of urbanization on the supply and demand of coastal wetland ecological functions according to claim 1, characterized in that: The impact data includes meteorological data, population density data, regional GDP data, building land data, carbon density data, carbon emission data, water consumption data, and water quality standard data. The process of assessing urbanization level and supply and demand of coastal wetland ecological functions based on the impact data to obtain the comprehensive urbanization level index and supply and demand ratio of coastal wetland comprehensive ecological functions for each grid unit includes the following steps: The urbanization level comprehensive index of each grid cell is obtained by averaging the population density data, regional GDP data, and building land data of the same grid cell. The carbon sequestration function supply and demand data of each grid cell are calculated based on the carbon density data, carbon emission data and population density data of the same grid cell. Water supply and demand data for each grid cell are calculated based on meteorological data, water consumption data, and population density data. Water quality purification supply and demand data are calculated based on the water quality standard data of each grid unit to obtain the water quality purification supply and demand data of each grid unit. Based on the carbon sequestration supply and demand data, water resource supply and demand data, water purification supply and demand data of several grid units, and a preset algorithm for calculating the supply and demand ratio of the comprehensive ecological function of coastal wetlands, the supply and demand ratio of the comprehensive ecological function of coastal wetlands for each grid unit is obtained.

3. The method for identifying the threshold of the impact of urbanization on the supply and demand of coastal wetland ecological functions according to claim 2, characterized in that, The step of inputting the neighboring grid cell datasets and spatial location weight matrix of each grid cell into a preset initial spatial weighted threshold recognition model for model fitting to obtain the corresponding target spatial weighted threshold recognition model for each grid cell includes the following steps: Several comprehensive urbanization level indices of neighboring raster units are extracted from the neighboring raster unit dataset of the raster unit and used as candidate spatial threshold parameters of the initial spatial weighted threshold identification model. Based on the neighboring raster unit dataset of the raster unit and several candidate spatial threshold parameters, the initial spatial weighted threshold identification model is fitted to obtain several candidate spatial weighted threshold identification models corresponding to several raster units. Based on the candidate space threshold parameters and candidate regression coefficients of the candidate space weighted threshold identification model, the target space weighted threshold identification model is determined from several candidate space weighted threshold identification models, and the corresponding target space weighted threshold identification model for each grid cell is obtained.

4. The method for identifying the threshold of the impact of urbanization on the supply and demand of coastal wetland ecological functions according to claim 3, characterized in that, The step of determining the target spatial weighted threshold identification model from several candidate spatial weighted threshold identification models based on the candidate spatial threshold parameters and candidate regression coefficients of the candidate spatial weighted threshold identification model, and obtaining the corresponding target spatial weighted threshold identification model for each grid cell, includes the following steps: Based on the comprehensive urbanization index and spatial location coordinate data in the neighboring grid cell dataset of the grid cell, the predicted supply-demand ratio of the coastal wetland ecological function of the neighboring grid cells is obtained from the output of several candidate spatial weighted threshold identification models of the corresponding grid cell. Based on the spatial coordinate data of the raster cell and several neighboring raster cells in the dataset, a local spatial position weight matrix is ​​constructed for the raster cell. This local spatial position weight matrix includes local spatial position weight parameters between the raster cell and its neighboring raster cells. The local spatial position weight parameters are: In the formula, For the first i The grid cell and the first j Local spatial location weight parameters between neighboring raster cells For the first i The grid cell and the first j The distance parameter between neighboring raster cells is calculated based on the spatial coordinate data of the raster cell and its neighboring raster cells. b This is a bandwidth parameter used to control the attenuation rate; The weighted residual sum of squares is calculated based on the local spatial location weight matrix of the raster cell, the supply-demand ratio of the coastal wetland ecological function of several neighboring raster cells in the dataset of the neighboring raster cells of the raster cell, and the predicted supply-demand ratio of the coastal wetland ecological function of several neighboring raster cells output by several candidate spatial weighted threshold identification models of the raster cell, to obtain the weighted residual sum of squares corresponding to several candidate spatial weighted threshold identification models of the raster cell; Based on the weighted residual sum of squares of several candidate spatial weighted threshold recognition models corresponding to the grid cell, the target spatial weighted threshold recognition model is determined from the several candidate spatial weighted threshold recognition models, and the target spatial weighted threshold recognition model corresponding to each grid cell is obtained.

5. The method for identifying the threshold of the impact of urbanization on the supply and demand of coastal wetland ecological functions according to claim 4, characterized in that: The urbanization impact analysis results include a spatial clustering map of urbanization impact thresholds, which is used to indicate the different sensitivities of urbanization in the coastal area to the key impacts of urbanization on the supply and demand of coastal wetland ecological functions. The step of performing an impact analysis based on the spatial threshold parameters and regression coefficients of the target spatial weighted threshold identification model for each of the grid units to obtain the urbanization impact analysis results includes the following steps: Based on the spatial coordinate data of several grid cells, a global spatial position weight matrix for several grid cells is constructed, wherein the global spatial position weight matrix includes global spatial position weight parameters between the grid cell and several neighboring grid cells; the global spatial position weight parameters are: In the formula, For the first i The grid cell and the first k Global spatial location weight parameters between grid cells For the first i The grid cell and the first k The distance parameter between each grid cell is calculated based on the grid cells and their spatial coordinate data. b This is a bandwidth parameter used to control the attenuation rate; Cluster analysis is performed based on the spatial threshold parameters in the target spatial weighted threshold identification model corresponding to each grid cell and the global spatial location weight matrix to obtain the spatial threshold clustering index of several grid cells; Based on the spatial threshold clustering index of several grid units and a preset clustering index threshold, several grid units are divided into high-value clustering units and low-value clustering units to construct a spatial clustering map of the urbanization impact threshold of the coastal area.

6. The method for identifying the threshold of the impact of urbanization on the supply and demand of coastal wetland ecological functions according to claim 4, characterized in that: The urbanization impact analysis results include a spatial classification map of urbanization impact patterns, which is used to indicate the spatial heterogeneity of the impact patterns of urbanization on the ecological function of coastal wetlands in the coastal area; the regression coefficients include pre-threshold regression coefficients and post-threshold regression coefficients. The step of performing an impact analysis based on the spatial threshold parameters and regression coefficients of the target spatial weighted threshold identification model for each of the grid cells to obtain the impact analysis results includes the following steps: Threshold type determination is performed based on the regression coefficients of the target space weighted threshold recognition model corresponding to each of the grid cells, and threshold type determination results corresponding to several grid cells are obtained. The threshold type determination results include abrupt threshold determination results and progressive threshold determination results. Based on the threshold type judgment results of several grid cells, the pre-threshold regression coefficient and post-threshold regression coefficient of the target space weighted threshold recognition model of the grid cells, the impact mode judgment results of several grid cells are obtained, and a spatial classification map of the urbanization impact mode of the coastal area is constructed. The impact mode judgment results include positive transformation impact results, negative transformation impact results, enhanced gradual impact results, and weakened gradual impact results.

7. The method for identifying the threshold of the impact of urbanization on the supply and demand of coastal wetland ecological functions according to claim 6, characterized in that, The step of obtaining the influence mode judgment result of several grid cells based on the threshold type judgment result of several grid cells, the pre-threshold regression coefficient and the post-threshold regression coefficient of the target space weighted threshold recognition model of the grid cells, includes the following steps: If the threshold type determination result is a mutation threshold determination result, the determination is made based on the pre-threshold regression coefficient, post-threshold regression coefficient, and preset influence mode threshold of the target space weighted threshold recognition model of the grid cell. If the pre-threshold regression coefficient is less than the influence mode threshold and the post-threshold regression coefficient is greater than the influence mode threshold, the corresponding positive transformation influence result of the grid cell is obtained; if the pre-threshold regression coefficient is greater than the influence mode threshold and the post-threshold regression coefficient is less than the influence mode threshold, the corresponding negative transformation influence result of the grid cell is obtained. If the threshold type judgment result is a progressive threshold judgment result, the judgment is made based on the pre-threshold regression coefficient and post-threshold regression coefficient of the target space weighted threshold recognition model of the grid cell. If the pre-threshold regression coefficient is greater than the post-threshold regression coefficient, the corresponding enhanced progressive influence result of the grid cell is obtained; if the pre-threshold regression coefficient is less than the post-threshold regression coefficient, the corresponding weakened progressive influence result of the grid cell is obtained.

8. A threshold identification device for the impact of urbanization on the supply and demand of coastal wetland ecological functions, characterized in that, include: An impact data acquisition module is used to acquire a coastal area and its impact data, wherein the coastal area includes a plurality of raster cells, and the impact data includes the impact data of the plurality of raster cells. The data evaluation module is used to evaluate the urbanization level and the supply and demand of the ecological functions of coastal wetlands based on the impact data, and to obtain the comprehensive urbanization index and the supply and demand ratio of the comprehensive ecological functions of coastal wetlands for each grid unit. The dataset construction module is used to obtain several neighboring raster units corresponding to each of the raster units as the center, and construct the neighboring raster unit dataset of each of the raster units. The neighboring raster unit dataset includes the spatial location coordinate data of several neighboring raster units, the comprehensive index of urbanization level, and the supply and demand ratio of the comprehensive ecological function of coastal wetlands. The model fitting module is used to input the neighboring raster cell datasets and spatial location weight matrix of each raster cell into a preset initial spatial weighted threshold recognition model for model fitting, thereby obtaining the corresponding target spatial weighted threshold recognition model for each raster cell. The initial spatial weighted threshold recognition model is a piecewise linear regression model constructed using the urbanization level comprehensive index and spatial location coordinate data as independent variables, the supply-demand ratio of coastal wetland ecological functions as the dependent variable, and spatial threshold parameters and regression coefficients. The spatial threshold parameters indicate the change in the direction of urbanization's influence on the supply-demand relationship of coastal wetland ecological functions; the regression coefficients indicate the intensity and direction of the influence of changes in urbanization level on the supply-demand ratio of coastal wetland ecological functions. The impact analysis module is used to perform impact analysis based on the spatial threshold parameters and regression coefficients of the identification model corresponding to the target spatial weighted threshold of each grid cell, and to obtain the results of the urbanization impact analysis.

9. A computer device, characterized in that, The device includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the method for identifying the threshold of the impact of urbanization on the supply and demand of coastal wetland ecological functions as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for identifying the threshold of the impact of urbanization on the supply and demand of coastal wetland ecological functions as described in any one of claims 1 to 7.

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