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

Abstract

The invention relates to the field of geographic information analysis, in particular to a method and a device for identifying a threshold value of an influence of urbanization on supply and demand of a coastal wetland ecological function, and the method and the device are based on spatial position coordinate data of a plurality of grid units of a coastal area, an urbanization level comprehensive index and a coastal wetland comprehensive ecological function supply and demand ratio. Performing model fitting on a preset initial space weighted threshold identification model to identify a critical point of the urbanization level to the change of the supply and demand relationship of the coastal wetland ecological function, and reflect the staged response characteristics of the supply and demand of the coastal wetland ecological function to urbanization and the spatial heterogeneity characteristics of the urbanization influence mechanism in different geographic units; the spatial difference threshold value characteristics of the coastal wetland ecological function supply and demand relation in the urbanization response process are effectively identified, and a refined and zoned scientific basis is provided for a regional ecological management strategy.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of geographic information analysis, and in particular to a method and device for identifying a threshold value of an influence of urbanization on an ecological function supply-demand relationship of a coastal wetland, a computer device, and a storage medium. BACKGROUND

[0002] The significant advancement of the urbanization process in the coastal zone has reshaped the supply-demand relationship and spatial pattern of the ecological function of the coastal wetland. As a nonlinear multi-stable complex system, the response of the coastal wetland to urbanization may have a threshold effect, that is, when the urbanization level exceeds a certain threshold value, the supply-demand relationship of its ecological function will change significantly. It is worth noting that due to the significant spatial heterogeneity of urbanization development, this threshold effect may exhibit spatial differentiation characteristics in different urbanization levels. Some regions may have a mutation in the ecological function supply-demand relationship at a lower urbanization level, while other regions may exhibit greater resilience.

[0003] The current threshold identification methods such as piecewise linear regression models can effectively capture the global threshold characteristics of ecological processes, but they still have deficiencies in spatial performance and are difficult to reveal the spatial differentiation rules of the threshold effect. SUMMARY

[0004] Therefore, the purpose of the present application is to provide a method and device for identifying a threshold value of an influence of urbanization on an ecological function supply-demand relationship of a coastal wetland, a computer device, and a storage medium. Based on spatial position coordinate data of a plurality of grid cells in a coastal area, a comprehensive urbanization level index, and a comprehensive ecological function supply-demand ratio of the coastal wetland, an initial spatial weighted threshold identification model is fitted to identify the critical point of the change in the ecological function supply-demand relationship of the coastal wetland caused by the urbanization level, reflect the stage response characteristics of the ecological function supply-demand relationship of the coastal wetland to urbanization and the spatial heterogeneity characteristics of the urbanization influence mechanism in different geographical units, and effectively identify the spatial difference threshold characteristics of the ecological function supply-demand relationship of the coastal wetland in the response process to urbanization, thereby providing a refined and zoned scientific basis for regional ecological management strategies.

[0005] In a first aspect, the embodiments of the present application provide a method for identifying a threshold value of an influence of urbanization on an ecological function supply-demand relationship of a coastal wetland, comprising the following steps:

[0006] obtaining a coastal area and influence data of the coastal area, wherein the coastal area includes a plurality of grid cells, and the influence data includes influence data of the plurality of grid cells;

[0007] performing urbanization level evaluation and coastal wetland ecological function supply-demand evaluation according to the influence data to obtain a comprehensive urbanization level index and a comprehensive ecological function supply-demand ratio of the coastal wetland for each grid cell;

[0008] obtaining a plurality of neighbor grid cells corresponding to each of the grid cells respectively as a center, and constructing a neighbor grid cell data set of each of the grid cells, wherein the neighbor grid cell data set comprises spatial position coordinate data, a comprehensive index of urbanization level, and a comprehensive ecological function supply-demand ratio of the plurality of neighbor grid cells;

[0009] inputting the neighbor grid cell data set of each of the grid cells and the spatial position weight matrix into a preset initial spatial weighted threshold identification model for model fitting, and obtaining a target spatial weighted threshold identification model corresponding to each of the grid cells; wherein the initial spatial weighted threshold identification model is a piecewise linear regression model constructed by taking the comprehensive index of urbanization level and the spatial position coordinate data as independent variables, and taking the ecological function supply-demand ratio of the coastal wetland as a dependent variable, in combination with a spatial threshold parameter and a regression coefficient; the spatial threshold parameter is used to indicate a change in an influence direction of urbanization on the ecological function supply-demand relationship of the coastal wetland; and the regression coefficient is used to indicate an influence intensity and an influence direction of a change in the urbanization level on the ecological function supply-demand ratio of the coastal wetland;

[0010] performing influence analysis according to the spatial threshold parameter and the regression coefficient of the target spatial weighted threshold identification model corresponding to each of the grid cells, and obtaining an urbanization influence analysis result.

[0011] In a second aspect, an embodiment of the present application provides a device for identifying an influence threshold of urbanization on ecological function supply-demand of a coastal wetland, comprising:

[0012] an influence data obtaining module, configured to obtain influence data of a coastal area and the coastal area, wherein the coastal area comprises a plurality of grid cells, and the influence data comprises influence data of the plurality of grid cells;

[0013] a data evaluation module, configured to perform urbanization level evaluation and ecological function supply-demand evaluation of the coastal wetland according to the influence data, and obtain a comprehensive index of urbanization level and a comprehensive ecological function supply-demand ratio of the coastal wetland for each of the grid cells;

[0014] a data set constructing module, configured to obtain a plurality of neighbor grid cells corresponding to each of the grid cells respectively as a center, and construct a neighbor grid cell data set of each of the grid cells, wherein the neighbor grid cell data set comprises spatial position coordinate data, a comprehensive index of urbanization level, and a comprehensive ecological function supply-demand ratio of the plurality of neighbor grid cells;

[0015] a model fitting module, configured to input the neighbor grid cell data set of each of the grid cells and the spatial position weight matrix into a preset initial spatial weight threshold identification model for model fitting, to obtain a corresponding target spatial weight threshold identification model of each of the grid cells; wherein the initial spatial weight threshold identification model is a piecewise linear regression model constructed by taking the urbanization level comprehensive index and the spatial position coordinate data as independent variables, and taking the coastal wetland ecological function supply-demand ratio as a dependent variable, and combining a spatial threshold parameter and a regression coefficient; the spatial threshold parameter is used to indicate a change in an influence direction of urbanization on the supply-demand relationship of the coastal wetland ecological function; and the regression coefficient is used to indicate an influence intensity and an influence direction of a change in the urbanization level on the coastal wetland ecological function supply-demand ratio.

[0016] an influence analysis module, configured to perform influence analysis according to the spatial threshold parameter and the regression coefficient of the corresponding target spatial weight threshold identification model of each of the grid cells, to obtain an urbanization influence analysis result.

[0017] In a third aspect, an embodiment of the present application provides a computer device, including a processor, a memory, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the urbanization influence threshold identification method for the supply-demand relationship of a coastal wetland ecological function when executing the computer program.

[0018] In a fourth aspect, an embodiment of the present application provides a storage medium, which stores a computer program, and the computer program implements the steps of the urbanization influence threshold identification method for the supply-demand relationship of a coastal wetland ecological function when executed by a processor.

[0019] In the embodiments of the present application, a method, device, computer device, and storage medium for identifying an influence threshold of urbanization on the supply-demand relationship of a coastal wetland ecological function are provided. Based on the spatial position coordinate data of a plurality of grid cells in a coastal area, the urbanization level comprehensive index, and the comprehensive ecological function supply-demand ratio of the coastal wetland, a preset initial spatial weight threshold identification model is fitted to identify a critical point of the change in the supply-demand relationship of the coastal wetland ecological function caused by the urbanization level, reflect the stage response characteristics of the supply-demand relationship of the coastal wetland ecological function to urbanization and the spatial heterogeneity characteristics of the urbanization influence mechanism in different geographical units, and effectively identify the spatial difference threshold characteristics of the supply-demand relationship of the coastal wetland ecological function in the response process to urbanization, so as to provide a refined and zoned scientific basis for regional ecological management strategies.

[0020] For better understanding and implementation, the present application is described in detail below with reference to the drawings. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1A flowchart of a method for identifying an influence threshold of urbanization on supply and demand of ecological functions of a coastal wetland is provided for an embodiment of the present application.

[0022] Figure 2 A schematic diagram of S2 in the flowchart of the method for identifying an influence threshold of urbanization on supply and demand of ecological functions of a coastal wetland is provided for an embodiment of the present application.

[0023] Figure 3 A schematic diagram of S4 in the flowchart of the method for identifying an influence threshold of urbanization on supply and demand of ecological functions of a coastal wetland is provided for an embodiment of the present application.

[0024] Figure 4 A schematic diagram of S42 in the flowchart of the method for identifying an influence threshold of urbanization on supply and demand of ecological functions of a coastal wetland is provided for an embodiment of the present application.

[0025] Figure 5 A schematic diagram of S5 in the flowchart of the method for identifying an influence threshold of urbanization on supply and demand of ecological functions of a coastal wetland is provided for an embodiment of the present application.

[0026] Figure 6 A schematic diagram of S5 in the flowchart of the method for identifying an influence threshold of urbanization on supply and demand of ecological functions of a coastal wetland is provided for another embodiment of the present application.

[0027] Figure 7 A structural schematic diagram of a device for identifying an influence threshold of urbanization on supply and demand of ecological functions of a coastal wetland is provided for an embodiment of the present application.

[0028] Figure 8 A structural schematic diagram of a computer device is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0029] The exemplary embodiments will be described in detail herein with reference to the attached drawings. In the following description, the same numbers are used to indicate the same or similar components. The embodiments described in the following exemplary embodiments do not represent all the embodiments consistent with the present application. Instead, they only represent examples of devices and methods consistent with some aspects of the present application, as detailed in the appended claims.

[0030] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

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

[0032] Referring to Figure 1 , Figure 1 A flowchart of a method for identifying an influence threshold of urbanization on the supply and demand of the ecological function of a coastal wetland is provided for an embodiment of the present application. The method comprises the following steps:

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

[0034] The execution subject of the method for identifying an influence threshold of urbanization on the supply and demand of the ecological function of a coastal wetland is an analysis device (hereinafter referred to as an analysis device) of the method for identifying an influence threshold of urbanization on the supply and demand of the ecological function of a coastal wetland. In an optional embodiment, the analysis device can be a computer device, can be a server, or a server cluster formed by a plurality of computer devices.

[0035] In the present embodiment, the analysis device can obtain a coastal area and influence data of the coastal area in a preset database, wherein the coastal area comprises a plurality of grid cells, and the influence data comprises influence data of the plurality of grid cells. The influence data comprises meteorological data, population density data, gross regional product data, building land data, carbon density data, carbon emission data, water resource consumption data, and water quality standard data.

[0036] The meteorological data reflects rainfall data, sunshine, temperature data, etc. The population density can reflect the quantity of the demand for ecosystem services. The greater the population density, the greater the total demand for services. The gross regional product data reflects the richness of the region and can indirectly reflect the level of human preference for enjoying ecosystem services. The stronger the regional economic strength, the higher the expected ecosystem services.

[0037] S2: According to the influence data, evaluate the urbanization level and evaluate the supply and demand of the ecological function of the coastal wetland to obtain a comprehensive index of the urbanization level of each grid cell and a comprehensive supply and demand ratio of the ecological function of the coastal wetland.

[0038] In the embodiment, the analysis device performs urbanization level evaluation and coastal wetland ecological function supply and demand evaluation according to the influence data, and obtains urbanization level comprehensive indexes of each grid unit and coastal wetland comprehensive ecological function supply and demand ratios.

[0039] Specifically, the analysis device performs coastal wetland ecological function supply and demand evaluation according to the influence data and a preset comprehensive evaluation model of ecosystem services and trade-offs, obtains coastal wetland ecological function supply and demand data of several types of several grid units, performs supply and demand ratio calculation according to the coastal wetland ecological function supply and demand data of the several types of the several grid units, and obtains a coastal wetland comprehensive ecological function supply and demand ratio of each grid unit.

[0040] The comprehensive evaluation model of ecosystem services and trade-offs is InVEST (Integrated Valuation of Ecosystem Services and Trade-offs), which integrates various biological physical and socio-economic data and provides a series of ecosystem service evaluation capabilities.

[0041] The coastal wetland ecological function supply and demand data of the several types include carbon fixation function supply and demand data, water resource supply and demand data, and water quality purification supply and demand data. Please refer to Figure 2 , Figure 2 FIG. 2 is a schematic diagram of S2 in a flow of a method for identifying an urbanization influence threshold on coastal wetland ecological function supply and demand provided by an embodiment of the present application, including steps S21-S25, which are specifically as follows:

[0042] S21: According to population density data, regional gross domestic product data, and building land data of the same grid unit, cumulative average processing is performed to obtain urbanization level comprehensive indexes of each grid unit.

[0043] In the embodiment, the analysis device performs cumulative average processing according to population density data, regional gross domestic product data, and building land data of the same grid unit to obtain urbanization level comprehensive indexes of each grid unit, wherein the urbanization level comprehensive index is:

[0044]

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

[0046] S22: According to carbon density data, carbon emission data, and population density data of the same grid unit, carbon fixation function supply and demand data calculation is performed to obtain carbon fixation function supply and demand data of each grid unit.

[0047] In the embodiment, the analysis device calculates the carbon fixation function supply and demand data according to the carbon density data, the carbon emission data and the population density data of the same grid unit, obtains the carbon fixation function supply and demand data of each grid unit, wherein the carbon density data includes aboveground biological carbon density data, underground biological carbon density data, soil carbon density data and dead organic matter carbon density data, the carbon fixation function supply and demand data includes carbon fixation function supply data and carbon fixation function demand data, and the carbon fixation function supply data is:

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

[0049] In the formula, CS tot is the carbon fixation function supply data, CS above is the aboveground biological carbon density data, CS below is the underground biological carbon density data, CS soil is the soil carbon density data, and CS dead is the dead organic matter carbon density data.

[0050] The carbon emission data includes per capita carbon emission data, and the carbon fixation function demand data is:

[0051] D cs = C p × ρ i

[0052] In the formula, D cs is the carbon fixation function demand data, C p is the per capita carbon emission data, and ρ i is the population density data of the i-th grid unit.

[0053] S23: Calculate water resource supply and demand data according to the meteorological data, water resource consumption data and population density data of each grid unit, and obtain water resource supply and demand data of each grid unit.

[0054] In the embodiment, the analysis device calculates the water resource supply and demand data according to the meteorological data, water resource consumption data and population density data of each grid unit, and obtains the water resource supply and demand data of each grid unit, wherein the meteorological data includes annual actual evapotranspiration and annual precipitation, the water resource supply and demand data includes water resource supply data and water resource demand data, and the water resource supply data is:

[0055]

[0056] WY is water resource supply data, AET i is the annual actual evapotranspiration of the i-th grid unit, P i is the annual precipitation of the i-th grid unit.

[0057] The water resource consumption data includes agricultural irrigation water per capita, domestic water per capita, and industrial water per capita, and the water resource demand data is:

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

[0059] D wy = ρ agr × (W dom +W ind +W i )

[0060] S24: Water quality purification supply and demand data is calculated according to water quality standard data of each grid unit, and water quality purification supply and demand data of each grid unit is obtained.

[0061] In this embodiment, the analysis device calculates water quality purification supply and demand data according to water quality standard data of each grid unit, and obtains water quality purification supply and demand data of each grid unit, wherein the water quality standard data includes pollutant load value and pollutant output rate, the water quality purification supply and demand data includes water quality purification supply data and water quality purification demand data, and the water quality purification supply data is:

[0062] WP = ALV i × (1-E)

[0063] WP = ALV i × (1-E)

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

[0064] The water quality standard data further includes water production and pollutant amount allowed to be discharged under water quality standard, and the water quality purification demand data is:

[0065] D wp = Y i × Q

[0066] D wp = Y i × Q

[0066] In the formula, D wp is water quality purification supply data, Y i is the water production of the i-th grid unit, and Q is the pollutant amount allowed to be discharged under water quality standard.

[0067] S25: Obtain the coastal wetland comprehensive ecological function supply-demand ratio of each grid unit according to the carbon sequestration function supply-demand data, water resource supply-demand data, water quality purification supply-demand data of a plurality of grid units, and a preset calculation algorithm of the coastal wetland comprehensive ecological function supply-demand ratio.

[0068] In this embodiment, the analysis device obtains the coastal wetland comprehensive ecological function supply-demand ratio of each grid unit according to the carbon sequestration function supply-demand data, water resource supply-demand data, water quality purification supply-demand data of a plurality of grid units, and a preset calculation algorithm of the coastal wetland comprehensive ecological function supply-demand ratio, wherein the coastal wetland comprehensive ecological function supply-demand ratio is:

[0069]

[0070] In the formula, CESD i is the coastal wetland comprehensive ecological function supply-demand ratio of the i-th grid unit, S i，l is the coastal wetland ecological function supply data of the i-th grid unit of the l-th type, D i，l is the coastal wetland ecological function demand data of the i-th grid unit of the l-th type, L is the number of types

[0071] S3: Obtain a plurality of neighbor grid units corresponding to each grid unit respectively as a center, and construct a neighbor grid unit data set of each grid unit.

[0072] In this embodiment, the analysis device obtains a plurality of neighbor grid units corresponding to each grid unit respectively as a center, and constructs a neighbor grid unit data set of each grid unit, wherein the neighbor grid unit data set includes spatial position coordinate data, urbanization level comprehensive index, and coastal wetland comprehensive ecological function supply-demand ratio of a plurality of neighbor grid units.

[0073] S4: Input the neighbor grid unit data set of each grid unit and the spatial position weight matrix into a preset initial spatial weighted threshold identification model for model fitting, and obtain a target spatial weighted threshold identification model corresponding to each grid unit.

[0074] In this embodiment, the analysis device inputs the neighbor grid unit data set of each grid unit and the spatial position weight matrix into a preset initial spatial weighted threshold identification model for model fitting, and obtains a target spatial weighted threshold identification model corresponding to each grid unit, wherein the initial spatial weighted threshold identification model is a segmented linear regression model constructed by taking the urbanization level comprehensive index and the spatial position coordinate data as independent variables, and the coastal wetland ecological function supply-demand ratio as dependent variable, and combining the spatial threshold parameter and the regression coefficient.

[0075] Specifically, the initial spatial weighted threshold identification model is constructed on the basis of a piecewise linear regression model, combined with the spatial position information of the grid unit, introducing a spatial weight coefficient, acquiring an optimal spatial threshold parameter through dynamic search, and fitting a regression coefficient of a corresponding stage. The spatial threshold parameter is used to indicate the change of the influence direction of urbanization on the supply-demand relationship of coastal wetland ecological function. Specifically, the spatial threshold parameter is used to indicate the key turning point of the significant change of the influence of urbanization on the supply-demand relationship of coastal wetland ecological function in a local spatial range. The regression coefficient is used to indicate the influence intensity and direction of the change of urbanization level on the supply-demand ratio of coastal wetland ecological function. The initial spatial weighted threshold identification model is as follows:

[0076]

[0077] wherein, y i is the supply-demand ratio of coastal wetland ecological function of the i-th grid unit, (u i , v i ) is the spatial position coordinate data of the i-th grid unit, x i is the comprehensive index of urbanization level of the i-th grid unit, β0(u i , v i ) is the regression intercept term of the spatial position coordinate data (u i , v i ) of the i-th grid unit, β1(u i , v i ) is the regression coefficient before the threshold of the spatial position coordinate data (u i , v i ) of the i-th grid unit, β2(u i , v i ) is the regression coefficient after the threshold of the spatial position coordinate data (u i , v i ) of the i-th grid unit, ∈ i is the error term of the i-th grid unit, (u i , u i ) is the spatial position coordinate data of the i-th grid unit, x i is the comprehensive index of urbanization level of the i-th grid unit, and T(u i , v i ) is the spatial threshold parameter of the spatial position coordinate data (u i , v i ) of the i-th grid unit.

[0078] On the basis of the traditional piecewise linear regression model, a geographical spatial weight function is introduced to construct an initial spatial weighted threshold identification model for revealing the spatial heterogeneity of the nonlinear influence of urbanization on the supply-demand relationship of coastal wetland ecological functions. The initial spatial weighted threshold identification model effectively considers the autocorrelation and local variability in spatial data by introducing a spatial weight function, thereby improving the scientificity and spatial adaptability of threshold identification. In the initial spatial weighted threshold identification model, each set of regression parameters is adjusted according to the spatial location. The analysis device inputs the neighbor grid cell data set of each grid cell and the spatial location weight matrix into the preset initial spatial weighted threshold identification model for model fitting, thereby identifying whether there is a consistent critical point in the change of the supply-demand relationship of coastal wetland ecological functions under the influence of urbanization level, reflecting the stage response characteristics of the supply-demand relationship of coastal wetland ecological functions to urbanization, and reflecting the spatial heterogeneity of the urbanization influence mechanism in different geographical units. The method can effectively identify the spatial difference threshold characteristics of the supply-demand relationship of coastal wetland ecological functions in the response process to urbanization, and can provide a fine and zoned scientific basis for regional ecological management strategies.

[0079] Referring to Figure 3 , Figure 3 FIG. 4 is a schematic diagram of step S4 in the flow of the urbanization influence threshold identification method for the supply-demand relationship of coastal wetland ecological functions according to an embodiment of the present application, including steps S41-S42, which are as follows:

[0080] S41: Extracting a plurality of urbanization level comprehensive indices of neighbor grid cells from the neighbor grid cell data set of the grid cell as candidate spatial threshold parameters of the initial spatial weighted threshold identification model, fitting the initial spatial weighted threshold identification model according to the neighbor grid cell data set of the grid cell and the plurality of candidate spatial threshold parameters, and obtaining a plurality of candidate spatial weighted threshold identification models corresponding to the plurality of grid cells.

[0081] In this embodiment, the analysis device extracts a plurality of urbanization level comprehensive indices of neighbor grid cells from the neighbor grid cell data set of the grid cell as candidate spatial threshold parameters of the initial spatial weighted threshold identification model, fits the initial spatial weighted threshold identification model according to the neighbor grid cell data set of the grid cell and the plurality of candidate spatial threshold parameters, and obtains a plurality of candidate spatial weighted threshold identification models corresponding to the plurality of grid cells.

[0082] S42: Determining a target spatial weighted threshold identification model from the plurality of candidate spatial weighted threshold identification models according to the candidate spatial threshold parameters and the candidate regression coefficients of the candidate spatial weighted threshold identification model, and obtaining a target spatial weighted threshold identification model corresponding to each grid cell.

[0083] In the embodiment, the analysis device extracts the urbanization level comprehensive indexes of a plurality of neighbor grid cells from the neighbor grid cell data set of the grid cell as candidate spatial threshold parameters of the initial spatial weighted threshold identification model respectively, and fits the initial spatial weighted threshold identification model according to the neighbor grid cell data set of the grid cell and the plurality of candidate spatial threshold parameters, to obtain a plurality of candidate spatial weighted threshold identification models corresponding to the grid cell.

[0084] Referring to Figure 4 , Figure 4 FIG. 4 is a schematic diagram of S42 in the flow of the urbanization influence threshold identification method for the supply and demand of coastal wetland ecological functions according to an embodiment of the present application, including steps S421-S424, which are as follows:

[0085] S421: According to the urbanization level comprehensive index and the spatial position coordinate data in the neighbor grid cell data set of the grid cell, a plurality of predicted supply and demand ratios of coastal wetland ecological functions of the neighbor grid cells output by a plurality of candidate spatial weighted threshold identification models corresponding to the grid cell are obtained.

[0086] In the embodiment, the analysis device obtains a plurality of predicted supply and demand ratios of coastal wetland ecological functions of the neighbor grid cells output by a plurality of candidate spatial weighted threshold identification models corresponding to the grid cell according to the urbanization level comprehensive index and the spatial position coordinate data in the neighbor grid cell data set of the grid cell.

[0087] S422: According to the grid cell and the spatial position coordinate data of a plurality of neighbor grid cells in the neighbor grid cell data set of the grid cell, a local spatial position weight matrix of the grid cell is constructed.

[0088] In the embodiment, the analysis device constructs a spatial position weight matrix of the grid cell according to the grid cell and the spatial position coordinate data of a plurality of neighbor grid cells in the neighbor grid cell data set of the grid cell, wherein the local spatial position weight matrix includes local spatial position weight parameters between the grid cell and the plurality of neighbor grid cells; the local spatial position weight parameters are as follows:

[0089]

[0090] In the formula, w ij is the local spatial position weight parameter between the i th grid cell and the j th neighbor grid cell, d ijis a distance parameter between the i-th grid cell and the j-th neighbor grid cell, is obtained based on spatial position coordinate data of the grid cell and the neighbor grid cell, b is a bandwidth parameter for controlling the decay speed.

[0091] S423: Perform weighted residual sum of squares calculation according to the spatial position weight matrix of the grid cell, the coastal wetland ecological function supply and demand ratio of the several neighbor grid cells in the neighbor grid cell data set of the grid cell, and the predicted coastal wetland ecological function supply and demand ratio of the several neighbor grid cells output by the several candidate spatially-weighted threshold identification models corresponding to the grid cell, to obtain the weighted residual sum of squares corresponding to the several candidate spatially-weighted threshold identification models corresponding to the grid cell.

[0092] In this embodiment, the analysis device performs weighted residual sum of squares calculation according to the spatial position weight matrix of the grid cell, the coastal wetland ecological function supply and demand ratio of the several neighbor grid cells in the neighbor grid cell data set of the grid cell, and the predicted coastal wetland ecological function supply and demand ratio of the several neighbor grid cells output by the several candidate spatially-weighted threshold identification models corresponding to the grid cell, to obtain the weighted residual sum of squares corresponding to the several candidate spatially-weighted threshold identification models corresponding to the grid cell, wherein 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 spatially-weighted threshold identification model corresponding to the grid cell, y j is the predicted coastal wetland ecological function supply and demand ratio of the j-th neighbor grid cell output by the candidate spatially-weighted threshold identification model, is the coastal wetland ecological function supply and demand ratio of the j-th neighbor grid cell.

[0095] S424: Determine the target spatially-weighted threshold identification model from the several candidate spatially-weighted threshold identification models according to the weighted residual sum of squares corresponding to the several candidate spatially-weighted threshold identification models corresponding to the grid cell, to obtain the target spatially-weighted threshold identification model corresponding to each grid cell.

[0096] In this embodiment, the analysis device determines the candidate spatially-weighted threshold identification model corresponding to the minimum weighted residual sum of squares as the target spatially-weighted threshold identification model according to the weighted residual sum of squares corresponding to the several candidate spatially-weighted threshold identification models corresponding to the grid cell, determines the target spatially-weighted threshold identification model from the several candidate spatially-weighted threshold identification models, and obtains the target spatially-weighted threshold identification model corresponding to each grid cell.

[0097] S5: Perform influence analysis according to the spatial threshold value parameters and regression coefficients of the target spatial weighted threshold value identification model corresponding to each grid unit, and obtain urbanization influence analysis results.

[0098] In this embodiment, the analysis device performs influence analysis according to the spatial threshold value parameters and regression coefficients of the target spatial weighted threshold value identification model corresponding to each grid unit, and obtains urbanization influence analysis results.

[0099] The urbanization influence analysis results include a urbanization influence threshold value spatial clustering map, which is used to indicate regions in the coastal area that are of different sensitivity degrees of urbanization having a key influence on the supply and demand of coastal wetland ecological functions; please refer to Figure 5 , Figure 5 The flowchart of the urbanization influence threshold value identification method provided by one embodiment of the present application is shown in FIG. 5, which includes steps S51-S52, and the details are as follows.

[0100] S51: Construct a global spatial position weight matrix of the grid units according to the spatial position coordinate data of the grid units; and perform clustering analysis according to the spatial threshold value parameters in the target spatial weighted threshold value identification model corresponding to each grid unit and the global spatial position weight matrix, to obtain spatial threshold value clustering indexes of the grid units.

[0101] In this embodiment, the analysis device constructs a global spatial position weight matrix of the grid units according to the spatial position coordinate data of the grid units, wherein the global spatial position weight matrix includes global spatial position weight parameters between the grid units and neighbor grid units; the global spatial position weight parameters are as follows:

[0102]

[0103] wherein, θ ik is the global spatial position weight parameter between the i th grid unit and the k th grid unit, d ik is the distance parameter between the i th grid unit and the k th grid unit, is calculated based on the spatial position coordinate data of the grid units and the neighbor grid units, and b is a bandwidth parameter used to control the attenuation speed.

[0104] In the embodiment, the analysis device constructs a global spatial position weight matrix of the plurality of grid cells according to spatial position coordinate data of the plurality of grid cells; performs clustering analysis according to the spatial threshold value parameter in the corresponding target spatial weighted threshold value identification model of each grid cell and the global spatial position weight matrix to obtain a spatial threshold value clustering index of the plurality of grid cells, wherein the spatial threshold value clustering index is:

[0105]

[0106] In the formula, is the spatial threshold value clustering index of the i-th grid cell, n is the number of grid cells, is the mean value of the spatial threshold value parameter in the corresponding target spatial weighted threshold value identification model of the grid cell, and S is the mean value of the spatial threshold value parameter in the corresponding target spatial weighted threshold value identification model of the grid cell.

[0107] By calculating the spatial threshold value parameter in the corresponding target spatial weighted threshold value identification model of the grid cell and combining the global spatial position weight matrix, whether the average threshold value in each grid cell and its neighborhood is significantly higher or lower than the global mean value is quantified, the local threshold value characteristics of the urbanization influence on the supply-demand relationship of the coastal wetland ecological function are displayed, and the spatial differentiation pattern of the threshold value influenced by urbanization in different regions is presented.

[0108] S52: According to the spatial threshold value clustering index of the plurality of grid cells and the preset clustering index threshold value, the plurality of grid cells are divided into high-value aggregation units and low-value aggregation units, and a city influence threshold value spatial clustering map of the coastal area is constructed.

[0109] In the embodiment, according to the spatial threshold value clustering index of the plurality of grid cells and the preset clustering index threshold value, if the spatial threshold value clustering index is greater than the clustering index threshold value, the corresponding grid cell is judged as a high-value aggregation unit, and if the spatial threshold value clustering index is less than the clustering index threshold value, the corresponding grid cell is judged as a low-value aggregation unit, the plurality of grid cells are divided into high-value aggregation units and low-value aggregation units, and a city influence threshold value spatial clustering map of the coastal area is constructed.

[0110] According to the spatial clustering characteristics corresponding to each grid cell, a city influence threshold value spatial clustering map of the coastal area is constructed. By revealing the agglomeration pattern of similar threshold values in space, it is helpful to identify the high-sensitivity region of the city influence on the supply-demand of the coastal wetland ecological function, and to provide a basis for the differentiated ecological regulation and early warning management of the region.

[0111] The urbanization influence analysis result includes a urbanization influence mode space classification map, wherein the urbanization influence mode space classification map is used to indicate spatial heterogeneity of an urbanization influence mode on the coastal wetland ecological function in the coastal area; the regression coefficients include a pre-threshold regression coefficient and a post-threshold regression coefficient; please refer to Figure 6 , Figure 6 The schematic diagram of S5 in the flow of the urbanization influence threshold identification method for the supply and demand of the ecological function of the coastal wetland provided by another embodiment of the present application includes steps S53-S54, and the details are as follows:

[0112] S53: According to the regression coefficients of the target space-weighted threshold identification model corresponding to each grid unit, threshold type judgment is performed to obtain threshold type judgment results corresponding to a plurality of grid units.

[0113] In this embodiment, the analysis device performs threshold type judgment according to the regression coefficients of the target space-weighted threshold identification model corresponding to each grid unit to obtain threshold type judgment results corresponding to a plurality of grid units, wherein the threshold type judgment results include a sudden change type threshold judgment result and a gradual change type threshold judgment result.

[0114] Specifically, the analysis device multiplies the pre-threshold regression coefficient and the post-threshold regression coefficient of the target space-weighted threshold identification model corresponding to the same grid unit, if the product of the two is negative, it is judged that the threshold type judgment result is a sudden change type threshold judgment result; if the product of the two is positive, it is judged that the threshold type judgment result is a gradual change type threshold judgment result.

[0115] S54: According to the threshold type judgment results corresponding to a plurality of grid units, the pre-threshold regression coefficient and the post-threshold regression coefficient of the target space-weighted threshold identification model corresponding to the grid unit, the influence mode judgment results corresponding to a plurality of grid units are obtained, and the urbanization influence mode space classification map of the coastal area is constructed.

[0116] In this embodiment, the analysis device obtains the influence mode judgment results corresponding to a plurality of grid units according to the threshold type judgment results corresponding to a plurality of grid units, the pre-threshold regression coefficient and the post-threshold regression coefficient of the target space-weighted threshold identification model corresponding to the grid unit, and constructs the urbanization influence mode space classification map of the coastal area, wherein the influence mode judgment results include a positive conversion type influence result, a negative conversion type influence result, an enhanced gradual change type influence result, and a weakened gradual change type influence result.

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

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

[0119] If the threshold type judgment result corresponding to the grid cell is a gradual threshold judgment result, and the threshold pre-regression coefficient of the target space-weighted threshold value identification model corresponding to the grid cell is greater than the threshold post-regression coefficient of the target space-weighted threshold value identification model corresponding to the grid cell, the analysis device determines that the influence mode judgment result corresponding to the grid cell is an enhanced gradual influence result.

[0120] If the threshold type judgment result corresponding to the grid cell is a gradual threshold judgment result, and the threshold pre-regression coefficient of the target space-weighted threshold value identification model corresponding to the grid cell is less than the threshold post-regression coefficient of the target space-weighted threshold value identification model corresponding to the grid cell, the analysis device determines that the influence mode judgment result corresponding to the grid cell is a weakened gradual influence result.

[0121] The analysis device constructs a city influence mode spatial classification map of the coastal area according to the influence mode judgment result corresponding to each grid cell and the spatial coordinate position data, directly displays the spatial heterogeneity of the influence mode of urbanization on the coastal wetland ecological function, accurately describes the influence characteristics of urbanization on the coastal wetland ecological function at the grid cell level, and accurately analyzes the spatial heterogeneity characteristics of the supply and demand response of the coastal wetland ecological function, thereby providing a scientific basis for formulating differentiated ecological management strategies for the coastal zone.

[0122] Please refer to Figure 7 , Figure 7 The structure diagram of the city influence threshold identification device for the supply and demand of the coastal wetland ecological function provided by an embodiment of the present application is shown. The device can realize all or part of the city influence threshold identification device for the supply and demand of the coastal wetland ecological function through software, hardware, or a combination of the two. The device 7 includes:

[0123] An influence data obtaining module 71 is configured to obtain influence data of coastal areas and the coastal areas, wherein the coastal areas include a plurality of grid cells, and the influence data includes influence data of the plurality of grid cells;

[0124] A data evaluation module 72 is configured to evaluate urbanization levels and supply-demand ratios of coastal wetland ecological functions according to the influence data, and obtain comprehensive indexes of urbanization levels and comprehensive supply-demand ratios of coastal wetland ecological functions of each of the grid cells;

[0125] A data set construction module 73 is configured to obtain a plurality of neighbor grid cells of each of the grid cells respectively as a center, and construct a neighbor grid cell data set of each of the grid cells, wherein the neighbor grid cell data set includes spatial position coordinate data, comprehensive indexes of urbanization levels and comprehensive supply-demand ratios of coastal wetland ecological functions of the plurality of neighbor grid cells;

[0126] A model fitting module 74 is configured to input the neighbor grid cell data set of each of the grid cells and a spatial position weight matrix into a preset initial spatial weighted threshold identification model to perform model fitting, and obtain a target spatial weighted threshold identification model corresponding to each of the grid cells; wherein the initial spatial weighted threshold identification model is a segmented linear regression model constructed by taking the comprehensive indexes of urbanization levels and the spatial position coordinate data as independent variables, taking the supply-demand ratios of coastal wetland ecological functions as dependent variables, and combining a spatial threshold parameter and a regression coefficient; the spatial threshold parameter is used to indicate a change in an influence direction of urbanization on the supply-demand relationship of coastal wetland ecological functions; and the regression coefficient is used to indicate an influence intensity and an influence direction of changes in urbanization levels on the supply-demand ratio of coastal wetland ecological functions;

[0127] An influence analysis module 75 is configured to perform influence analysis according to the spatial threshold parameter and the regression coefficient of the target spatial weighted threshold identification model corresponding to each of the grid cells, and obtain an urbanization influence analysis result.

[0128] In the embodiment of the present application, the influence data of the coastal area and the coastal area are obtained by the data obtaining module, wherein the coastal area comprises a plurality of grid units, and the influence data comprises influence data of a plurality of grid units; the urbanization level evaluation and the coastal wetland ecological function supply and demand evaluation are performed according to the influence data by the data evaluation module, so as to obtain the urbanization level comprehensive index of each grid unit and the coastal wetland comprehensive ecological function supply and demand ratio; the neighbor grid unit data set of each grid unit is obtained by taking each grid unit as the center respectively by the data set construction module, wherein the neighbor grid unit data set comprises the spatial position coordinate data, the urbanization level comprehensive index and the coastal wetland comprehensive ecological function supply and demand ratio of a plurality of neighbor grid units; the neighbor grid unit data set of each grid unit and the spatial position weight matrix are input into the preset initial spatial weighted threshold identification model for model fitting by the model fitting module, so as to obtain the target spatial weighted threshold identification model corresponding to each grid unit; wherein the initial spatial weighted threshold identification model is a piecewise linear regression model taking the urbanization level comprehensive index and the spatial position coordinate data as independent variables, and taking the coastal wetland ecological function supply and demand ratio as dependent variable, and combining the spatial threshold parameter and the regression coefficient; the spatial threshold parameter is used to indicate the change of the influence direction of urbanization on the relationship between the coastal wetland ecological function supply and demand; the regression coefficient is used to indicate the influence intensity and the influence direction of the urbanization level change on the coastal wetland ecological function supply and demand ratio; the influence analysis result is obtained by the influence analysis module according to the spatial threshold parameter and the regression coefficient of the target spatial weighted threshold identification model corresponding to each grid unit. Based on the spatial position coordinate data, the urbanization level comprehensive index and the coastal wetland comprehensive ecological function supply and demand ratio of a plurality of grid units in the coastal area, the model fitting is performed on the preset initial spatial weighted threshold identification model, so as to identify the critical point of the change of the relationship between the coastal wetland ecological function supply and demand and urbanization, reflect the stage response characteristics of the coastal wetland ecological function supply and demand to urbanization and the spatial heterogeneity characteristics of the urbanization influence mechanism in different geographical units, and effectively identify the spatial difference threshold characteristics of the response process of the relationship between the coastal wetland ecological function supply and demand and urbanization, so as to provide a refined and partitioned scientific basis for regional ecological management strategy.

[0129] Please refer to Figure 8 , Figure 8 The structural schematic diagram of the computer device provided in an embodiment of the present application, the computer device 8 comprises a processor 81, a memory 82, and a computer program 83 stored in the memory 82 and capable of running on the processor 81; the computer device can store a plurality of instructions, the instructions are suitable for being loaded and executed by the processor 81 to perform the above Figures 1 to 6The method steps of the embodiment shown can refer to the specific implementation process Figures 1 to 6 The specific description of the embodiment shown will not be repeated here.

[0130] The processor 81 can include one or more processing cores. The processor 81 connects various parts within the server through various interfaces and lines, and performs various functions and processes data of the urbanization influence threshold identification device 7 for the supply and demand of the ecological function of coastal wetlands by running or executing instructions, programs, code sets or instruction sets stored in the memory 82, and calling data in the memory 82. Optionally, the processor 81 can be implemented in at least one of the hardware forms of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programble Logic Array, PLA). The processor 81 can be integrated with one or a combination of central processing units (Central Processing Unit, CPU), graphics processing units (Graphics Processing Unit, GPU), and modems. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content displayed on the touch display; and the modem is used for processing wireless communication. It can be understood that the above-mentioned modem can also not be integrated into the processor 81, but can be realized by a separate chip.

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

[0132] The embodiment of the present application also provides a storage medium, which can store a plurality of instructions, the instructions being suitable for being loaded and executed by a processor to implement the above-mentioned Figures 1 to 6The specific steps and specific implementation process of the embodiments shown can be seen from Figures 1 to 6 The specific description of the embodiments shown is not described here.

[0133] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is exemplified, and in actual application, the above-mentioned functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be realized in the form of hardware or software function unit. In addition, the specific name of each functional unit and module is only for the convenience of mutual distinction, and does not limit the protection scope of the present application. The specific working process of the unit and module in the above-mentioned system can refer to the corresponding process in the foregoing method embodiment, which will not be described here.

[0134] In the above embodiments, the description of each embodiment has its own emphasis, and the parts not described or recorded in a certain embodiment can be referred to the related description of other embodiments.

[0135] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are executed in hardware or software depends on the specific application and design constraints of the algorithm. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0136] In the embodiments provided by the present application, it should be understood that the disclosed device / terminal equipment and method can be implemented by other ways. For example, the above-mentioned device / terminal equipment embodiments are only schematic, for example, the division of the modules or units is only a logical function division, and actual implementation can have another division way, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0137] The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0138] In addition, in various embodiments of the present application, each functional unit can be integrated into a processing unit, or each unit can exist physically, or two or more units can be integrated into one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0139] The integrated module / unit, if realized in the form of a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer-readable storage medium. The computer program can implement the steps of each method embodiment when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms, etc.

[0140] The present application is not limited to the above-described embodiments, and various modifications or changes can be made to the present application without departing from the spirit and scope of the present application. If the modifications and changes belong to the scope of the claims of the present application and the equivalent technical scope, the present application also intends to include these modifications and changes.

Claims

1. A threshold identification method for the impact of urbanization on the supply and demand of coastal wetland ecological functions, characterized by: The following steps are involved: Obtaining a coastal area and impact data of the coastal area, wherein the coastal area includes a plurality of grid units, and the impact data includes impact data of the plurality of grid units; Performing an urbanization level assessment and a coastal wetland ecological function supply and demand assessment based on the impact data to obtain a comprehensive urbanization level index and a coastal wetland comprehensive ecological function supply and demand ratio for each grid unit; Taking each grid cell as the center, obtaining a number of neighboring grid cells corresponding to each grid cell, and constructing a neighboring grid cell dataset of each grid cell, wherein the neighboring grid cell dataset includes spatial position coordinate data of the plurality of neighboring grid cells, a comprehensive urbanization level index, and a supply-demand ratio of comprehensive ecological functions of coastal wetlands; The neighbor grid cell dataset and the spatial position weight matrix of each grid cell are input into a preset initial spatial weighted threshold recognition model for model fitting to obtain a target spatial weighted threshold recognition model corresponding to each grid cell; wherein the initial spatial weighted threshold recognition model is a piecewise linear regression model constructed by combining a comprehensive index of urbanization level and spatial position coordinate data as independent variables and the supply and demand ratio of coastal wetland ecological functions as a dependent variable, with spatial threshold parameters and regression coefficients; the spatial threshold parameters are used to indicate changes in the direction of the impact of urbanization on the supply and 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 levels on the supply and demand ratio of coastal wetland ecological functions; An impact analysis is performed based on the spatial threshold parameters and regression coefficients of the target spatial weighted threshold recognition model corresponding to each grid unit to obtain an urbanization impact analysis result.

2. The method for identifying thresholds for the impact of urbanization on the supply and demand of coastal wetland ecological functions according to claim 1 is characterized by: The impact data include meteorological data, population density data, regional GDP data, building land data, carbon density data, carbon emission data, water resource consumption data and water quality standard data; The step of performing urbanization level assessment and coastal wetland ecological function supply and demand assessment based on the impact data to obtain a comprehensive urbanization level index and a coastal wetland comprehensive ecological function supply and demand ratio for each grid unit includes the following steps: Obtaining a comprehensive urbanization index for each grid cell by performing cumulative averaging processing on the population density data, regional GDP data, and building land data of the same grid cell; Calculating carbon sequestration supply and demand data based on the carbon density data, carbon emission data, and population density data of the same grid unit to obtain carbon sequestration supply and demand data for each grid unit; Calculating water resource supply and demand data based on the meteorological data, water resource consumption data, and population density data of each grid unit to obtain water resource supply and demand data for each grid unit; Calculating water quality purification supply and demand data based on the water quality standard data of each grid unit to obtain water quality purification supply and demand data of each grid unit; Based on the carbon sequestration function supply and demand data, water resource supply and demand data, water purification supply and demand data of several grid units and a preset coastal wetland comprehensive ecological function supply and demand ratio calculation algorithm, the coastal wetland comprehensive ecological function supply and demand ratio of each grid unit is obtained.

3. The method for identifying the threshold value of the impact of urbanization on the supply and demand of coastal wetland ecological functions according to claim 2 is characterized in that: The neighboring grid cell dataset and the spatial position weight matrix of each grid cell are input into a preset initial spatial weighted threshold recognition model for model fitting to obtain a target spatial weighted threshold recognition model corresponding to each grid cell, including the steps of: Extracting comprehensive urbanization level indexes of several neighboring grid cells from a dataset of neighboring grid cells of the grid cell and using them as candidate spatial threshold parameters of the initial spatial weighted threshold recognition model; fitting the initial spatial weighted threshold recognition model based on the dataset of neighboring grid cells of the grid cell and the candidate spatial threshold parameters to obtain several candidate spatial weighted threshold recognition models corresponding to the several grid cells; According to the candidate spatial threshold parameters and candidate regression coefficients of the candidate spatial weighted threshold recognition model, a target spatial weighted threshold recognition model is determined from a plurality of the candidate spatial weighted threshold recognition models to obtain the target spatial weighted threshold recognition model corresponding to each of the grid cells.

4. The method for identifying the threshold value of the impact of urbanization on the supply and demand of coastal wetland ecological functions according to claim 3 is characterized in that: The method comprises the following steps: determining a target space weighted threshold recognition model from a plurality of candidate space weighted threshold recognition models based on the candidate space threshold parameters and the candidate regression coefficients of the candidate space weighted threshold recognition models, and obtaining the target space weighted threshold recognition model corresponding to each grid unit. Obtaining predicted coastal wetland ecological function supply-demand ratios of several neighboring grid cells output by several candidate spatial weighted threshold recognition models corresponding to the grid cell based on the comprehensive urbanization level index and spatial position coordinate data in the neighboring grid cell data set; Constructing a local spatial position weight matrix of the grid cell based on the spatial position coordinate data of a plurality of neighboring grid cells in the grid cell and the neighboring grid cell data set of the grid cell, wherein the local spatial position weight matrix includes local spatial position weight parameters between the grid cell and the plurality of neighboring grid cells; performing weighted residual sum of squares calculation based on the spatial position weight matrix of the grid cell, the coastal wetland ecological function supply and demand ratios of several neighboring grid cells in the neighboring grid cell data set of the grid cell, and the predicted coastal wetland ecological function supply and demand ratios of several neighboring grid cells output by several candidate spatial weighted threshold recognition models corresponding to the grid cell, to obtain the weighted residual sum of squares corresponding to the several candidate spatial weighted threshold recognition models corresponding to the grid cell; According to the weighted residual square sums corresponding to the several candidate spatial weighted threshold recognition models corresponding to the grid units, a target spatial weighted threshold recognition model is determined from the several candidate spatial weighted threshold recognition models to obtain the target spatial weighted threshold recognition model corresponding to each of the grid units.

5. The method for identifying thresholds for the impact of urbanization on the supply and demand of coastal wetland ecological functions according to claim 4 is characterized by: The urbanization impact analysis results include a spatial clustering map of urbanization impact thresholds, wherein the spatial clustering map of urbanization impact thresholds is used to indicate areas in the coastal area with different degrees of sensitivity to urbanization's key impact on the supply and demand of coastal wetland ecological functions; The method of performing an impact analysis based on the spatial threshold parameters and regression coefficients of the target spatial weighted threshold recognition model corresponding to each grid unit to obtain an urbanization impact analysis result comprises the following steps: Constructing a global spatial position weight matrix of the grid cells based on the spatial position coordinate data of the grid cells; performing cluster analysis based on the spatial threshold parameters in the target spatial weighted threshold recognition model corresponding to each grid cell and the global spatial position weight matrix to obtain spatial threshold clustering indices of the grid cells; According to the spatial threshold clustering index of the plurality of grid cells and a preset clustering index threshold, the plurality of grid cells are divided into high-value clustering units and low-value clustering units, and a spatial clustering map of the urbanization impact threshold of the coastal area is constructed.

6. The method for identifying thresholds for 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, wherein the spatial classification map of urbanization impact patterns is used to indicate the spatial heterogeneity of the impact pattern of urbanization on the ecological function of coastal wetlands in the coastal area; the regression coefficient includes a pre-threshold regression coefficient and a post-threshold regression coefficient; The method of performing an impact analysis based on the spatial threshold parameters and regression coefficients of the target spatial weighted threshold recognition model corresponding to each grid unit to obtain an impact analysis result comprises the steps of: Performing threshold type judgment according to the regression coefficient of the target space weighted threshold recognition model corresponding to each grid unit to obtain threshold type judgment results corresponding to a plurality of grid units, wherein the threshold type judgment results include a sudden change threshold type judgment result and a gradual change threshold type judgment result; According to the threshold type judgment results corresponding to the plurality of grid cells, the pre-threshold regression coefficient and the post-threshold regression coefficient of the target space weighted threshold recognition model corresponding to the plurality of grid cells, the impact pattern judgment results corresponding to the plurality of grid cells are obtained, and a spatial classification map of the urbanization impact pattern of the coastal area is constructed, wherein the impact pattern judgment results include positive conversion type impact results, negative conversion type impact results, enhanced progressive type impact results and weakened progressive type impact results.

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

8. A device for identifying the threshold value of the impact of urbanization on the supply and demand of coastal wetland ecological functions, characterized by: include: An impact data acquisition module, configured to obtain a coastal area and impact data of the coastal area, wherein the coastal area includes a plurality of grid units, and the impact data includes impact data of the plurality of grid units; A data evaluation module is used to evaluate the urbanization level and the supply and demand of coastal wetland ecological functions based on the impact data, and obtain a comprehensive urbanization level index and a comprehensive coastal wetland ecological function supply and demand ratio for each grid unit; A dataset construction module is used to obtain a number of neighboring grid cells corresponding to each grid cell, taking each grid cell as the center, and construct a neighboring grid cell dataset of each grid cell, wherein the neighboring grid cell dataset includes spatial position coordinate data of the neighboring grid cells, a comprehensive urbanization level index, and a supply-demand ratio of the comprehensive ecological function of coastal wetlands; A model fitting module is used to input the neighbor grid cell dataset and spatial position weight matrix of each grid cell into a preset initial spatial weighted threshold recognition model for model fitting, so as to obtain a target spatial weighted threshold recognition model corresponding to each grid cell; wherein the initial spatial weighted threshold recognition model is a piecewise linear regression model constructed by combining a comprehensive index of urbanization level and spatial position coordinate data as independent variables and the supply and demand ratio of coastal wetland ecological functions as a dependent variable, with spatial threshold parameters and regression coefficients; the spatial threshold parameters are used to indicate changes in the direction of the impact of urbanization on the supply and demand relationship of coastal wetland ecological functions; and the regression coefficients are used to indicate the intensity and direction of the impact of changes in urbanization level on the supply and 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 target spatial weighted threshold recognition model corresponding to each grid unit to obtain urbanization impact analysis results.

9. A computer device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method 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 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 value of the supply and demand of the ecological functions of coastal wetlands due to urbanization as described in any one of claims 1 to 7.

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