A Comprehensive Evaluation Method for National Parks Based on Dynamic Process Background

Abstract

This invention discloses a method for evaluating the comprehensive effectiveness of national parks based on dynamic process background data, comprising the following steps: S1: acquiring multi-source data for five years before and after the establishment of the national park; S2: establishing an evaluation index system for the comprehensive effectiveness of the national park; S3: acquiring the above evaluation index data based on methods such as model simulation, spatial analysis, and statistical analysis; S4: determining the weights of the evaluation indicators based on the entropy method and the analytic hierarchy process; S5: establishing an evaluation model for the comprehensive effectiveness of the national park based on the weight values ​​of each evaluation indicator obtained in step S4; S6: evaluating the comprehensive effectiveness of the national park based on the comparison of average conditions before and after the project and the comparison of change trends. This invention proposes an evaluation method for the construction effectiveness of the national nature reserve system represented by national parks, and simultaneously realizes the spatial expression of statistical data at the kilometer scale from the county-level scale, which can be used to quantitatively evaluate the ecological and environmental benefits, socio-economic benefits, and comprehensive effectiveness of national park construction.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of park construction, and particularly relates to a national park comprehensive effect evaluation method based on a dynamic process background. BACKGROUND

[0002] A national park refers to a specific land or marine area approved and managed by the state, with clear boundaries, and mainly for the purpose of protecting large-area natural ecosystems with national representation, so as to realize scientific protection and rational utilization of natural resources.

[0003] It is of great significance to adjust the policy direction, more scientifically carry out the corresponding management work, and provide reference experience for the sustainable development of other national parks. However, there is no complete evaluation method or evaluation model for the construction effect of national parks in the prior art, and the construction effect of national parks cannot be quickly evaluated scientifically and accurately. SUMMARY

[0004] In view of the above problems, the present application provides a national park comprehensive effect evaluation method based on a dynamic process background, which unifies the social and economic data of the administrative unit scale and the ecological environment data of the grid pixel scale by using the method of space mathematics and statistics, effectively solves the spatial mismatch problem of ecological environment data and social and economic data encountered in most studies, and measures the development level of an area by integrating the two, realizes the fusion calculation of different categories and different scales of data, has great reference significance for related evaluation research of comprehensive evaluation research, and finally establishes a model which can be directly used for quickly evaluating the comprehensive benefits of the construction of national parks.

[0005] In order to achieve the above purpose, the technical scheme adopted by the present application is as follows:

[0006] A national park comprehensive effect evaluation method based on a dynamic process background, characterized in that it comprises the following steps:

[0007] S1: obtaining multi-source data of 5 years before and after the construction of a national park;

[0008] S2: establishing a national park comprehensive effect evaluation index system;

[0009] S3: determining the evaluation index data in step S2;

[0010] S4: determining the weight of the evaluation index based on the entropy value method and the analytic hierarchy process;

[0011] S5: establishing an evaluation model of the comprehensive effect of the national park according to the weight values of the evaluation indexes obtained in step S4;

[0012] S6: Evaluate the overall effectiveness of national parks based on the comparison of average conditions before and after the project and the comparison of changing trends.

[0013] Furthermore, the national park comprehensive effectiveness evaluation index system described in step S2 includes a target layer, a criterion layer, and an indicator layer;

[0014] The target layer includes ecological and environmental indicators and socio-economic indicators;

[0015] The criteria layer corresponding to the ecological and environmental indicators includes ecosystem quality, ecosystem function, and landscape pattern index.

[0016] The criteria layer corresponding to the socio-economic indicators includes social benefits and economic benefits;

[0017] The indicator layer corresponding to the ecosystem quality includes vegetation cover and net primary productivity; the indicator layer corresponding to the ecosystem function includes water conservation capacity, soil retention capacity, and windbreak and sand fixation capacity; the indicator layer corresponding to the landscape pattern index includes the Shannon diversity index.

[0018] The indicators corresponding to the social benefits include population density, medical service capacity, and density of employees in the tertiary sector; the indicators corresponding to the economic benefits include GDP density, residents' savings deposit balance, and livestock density.

[0019] Furthermore, the calculation method for the evaluation indicators corresponding to the ecological and environmental indicators in step S3 includes,

[0020] Vegetation cover is calculated using a pixel-based binary model, and the calculation formula is as follows:

[0021]

[0022] In the formula, F vc NDVI is the normalized difference between vegetation cover and NDVI, which is an indicator of plant growth status and spatial distribution density of vegetation. soil NDVI value for pixels with no vegetation cover; NDVI veg The NDVI value for pixels with pure vegetation;

[0023] Net primary productivity is calculated using the EC-LUE model, and the formula is as follows:

[0024] NPP = ε max ×Min(f(T)×f(W))×FPAR×PAR

[0025] FPAR = 1.24 × NDVI - 0.168

[0026] In the formula, NPP is net primary productivity, and ε maxis the potential light energy utilization rate; f(T) and f(W) are the light energy utilization rates under environmental stresses of temperature and canopy moisture conditions, respectively; PAR is the incident photosynthetically active radiation; FPAR is the proportion of photosynthetically active radiation absorbed by the plant canopy;

[0027] Water conservation capacity is calculated using the precipitation storage method, and the calculation formula is as follows:

[0028] S = 10M × Q × E

[0029] Q = Q0 × K

[0030] E = E0 - E g

[0031] E g = -0.3187 × F vc +0.3640

[0032] In the formula, S represents the increase in water retention capacity of the terrestrial ecosystem compared to bare land; M represents the ecosystem area; Q represents the annual runoff; Q0 represents the average annual precipitation; E represents the efficiency coefficient of the ecosystem in reducing runoff compared to bare land; K represents the proportion of runoff to total precipitation, with a value of 0.68; E0 represents the bare land runoff rate under runoff conditions, with a value of 0.36403; E g The ecosystem rainfall-runoff rate under runoff-producing conditions;

[0033] Soil retention capacity is calculated using a modified general soil loss equation, and the formula is as follows:

[0034] TB = TB q -TB r

[0035] TB q =K q ×M

[0036] TB r =K r ×M

[0037] T = J × D × P × H × F × W

[0038] In the formula, TB represents the ecosystem soil retention capacity at the computational unit; TB q This represents the potential soil erosion volume in a unit without vegetation protection; TB r K represents the amount of soil erosion under the actual land cover condition on the calculation unit. q K represents the potential soil erosion modulus under unvegetated conditions in the computational unit. rdenoted as Soil erosion modulus under actual cover conditions of the calculation unit; T is soil erosion modulus flux; J is precipitation erosivity factor; D is soil erodibility factor; P is slope length factor; H is slope factor; F is cover and management factor, with a value range of 0 to 1; W is soil and water conservation measures factor, with a value range of 0 to 1.

[0039] The amount of windbreak and sand fixation is calculated using a modified soil erosion equation. The calculation formula is as follows:

[0040] DG = DG q -DG r

[0041] DG q =WL q ×A

[0042] DG r =WL r ×A

[0043]

[0044]

[0045] N max =109.8(WF×BF×SCF×C×FG)

[0046] L=150.71(WF×BF×SCF×C×FG) -0.3711

[0047] In the formula, DG represents the amount of windbreak and sand fixation in the ecosystem of the calculation unit; DG q To calculate the potential soil wind erosion in a unit without vegetation protection; DG r To calculate the amount of soil wind erosion under the actual cover condition on the calculation unit; WL q WL is the potential soil wind erosion modulus under unprotected vegetation conditions in the calculation unit. r WL is the soil wind erosion modulus under the actual land cover condition in the calculation unit; A is the area of ​​the calculation unit; WL is the wind erosion modulus; X is the plot length; N x N represents the sand flux at location x; max 1. Maximum sand transport capacity by wind; L. Length of key plot; WF. Meteorological factor; BF. Soil erodibility component; SCF. Soil crust factor; C. Soil roughness factor; FG. Vegetation factor.

[0048] The Shannon diversity index was calculated using the landscape pattern software Fragstats. The formula is as follows:

[0049]

[0050] In the formula, SHDI is the Shannon Diversity Index, pi The percentage occupied by land use / cover type i.

[0051] Furthermore, the calculation method for the evaluation indicators corresponding to the socio-economic indicators in step S3 includes,

[0052] The methods for calculating population density and GDP density are as follows:

[0053] POP i+1 =POP i ×k i+1

[0054] GDP i+1 =GDP i ×k i+1

[0055] In the formula, POP i Let k be the population density in year i. i+1 For the population density growth rate in year i+1, GDP j Let k be the GDP density in year j. j+1 This represents the GDP density growth rate in the first year of production.

[0056] The calculation method for medical service capacity is as follows:

[0057]

[0058]

[0059] In the formula, NBMI ij NBMI represents the number of hospital beds in the i-th row and j-th column of the grid. cou MSTA represents the statistical value of the number of hospital beds in the county-level administrative region where the grid cell is located. ij For the theoretical medical service capabilities of this grid cell, MSTA cou The total theoretical capacity of medical services for the county-level administrative unit where the grid cell is located, MSC ij For the medical service capacity of the grid in row i and column j, POP ij This represents the population of the grid cell.

[0060] The method for calculating the density of employees in the tertiary sector is as follows:

[0061]

[0062] In the formula, PETI ij For the tertiary sector employment density of the grid in row i and column j, PETI cou This is the statistical value of the tertiary industry employment density of the county-level administrative region where the grid cell is located; POP couThis represents the total population of the county-level administrative unit where the grid cell is located;

[0063] The method for calculating the balance of residents' savings deposits is as follows:

[0064]

[0065] In the formula, SDBR ij It represents the balance of residents' savings deposits in the i-th row and j-th column of the grid, SDBR cou This refers to the statistical value of residents' savings deposits in the county-level administrative region where the grid cell is located; GDP ij GDP for this raster cell; GDP cou This represents the total GDP of the county-level administrative unit where the grid cell is located;

[0066] The method for calculating livestock density is as follows:

[0067]

[0068] In the formula, LD ij LD represents the number of livestock in the i-th row and j-th column grid; cou This is the livestock population statistic for the county-level administrative region where the raster cell is located; LAI ij This represents the livestock activity intensity of the grid cell; LAI cou This represents the total livestock activity intensity of the county-level administrative unit where the grid cell is located.

[0069] Furthermore, the specific operation of step S4 includes the following steps:

[0070] S401: Calculate the weights of each evaluation indicator using the entropy method;

[0071] S402: Use the analytic hierarchy process (AHP) to calculate the weights of each evaluation indicator;

[0072] S403: Take the average of the evaluation index weights calculated by the entropy method and the evaluation index weights calculated by the analytic hierarchy process as the final weight of the evaluation index.

[0073] Furthermore, the specific operation of step S401 includes the following steps:

[0074] S4011: Perform range standardization on the raw values ​​of each evaluation indicator;

[0075] Among them, for positive indicators,

[0076] For negative indicators,

[0077] S4012: Calculate the weight of the j-th indicator value for the i-th sample.

[0078]

[0079] S4013: Calculate the entropy value of the j-th index.

[0080] S4014: Calculate the weight of the j-th indicator.

[0081] In the formula, X′ ij X is the standardized value of the j-th indicator for the i-th sample; ij X is the original value of the j-th indicator for the i-th sample; max X is the maximum value of the j-th indicator in the i-th sample; min Let be the minimum value of the j-th indicator for the i-th sample; n is the number of samples.

[0082] Furthermore, the specific operation of step S402 includes the following steps:

[0083] S4021: Construct a judgment matrix based on the indicator system: By using expert scoring, compare indicators pairwise within the same level to construct the judgment matrix for that level.

[0084]

[0085] In the formula, u xy Indicate u x Relative to u y Importance values, x = 1, 2, ..., t, y = 1, 2, ..., t; t represents the number of indicators at this level;

[0086] S4022: Calculate the corresponding maximum eigenvalue λ based on the judgment matrix U. max The eigenvector W is then normalized to obtain the weight vector, i.e., UW = λ. max ×W, where UW is the weight vector;

[0087] S4023: Perform a consistency check on the weight vector obtained in step S4022.

[0088] Furthermore, the specific operation of step S4023 includes the following steps:

[0089] Step 1: First, calculate the consistency index.

[0090] Step 2: Determine the average random consistency index (RI) based on the number of factors;

[0091] Step 3: Calculate the consistency ratio

[0092] Furthermore, the specific operation of step S5 includes the following steps:

[0093] S501: By multiplying the weight of each indicator by the standardized value of its range, and summing the results, the corresponding ecological environment indicator evaluation results and socio-economic indicator evaluation results are obtained.

[0094]

[0095]

[0096] In the formula, Eco represents the evaluation result of ecological and environmental indicators, Soc represents the evaluation result of socio-economic indicators, and Ew represents the evaluation result of socio-economic indicators. i For the weights of various indicators corresponding to the ecological environment, Sw j EI represents the weights of various indicators corresponding to socioeconomic conditions. i SI represents the standardized results of various indicators corresponding to the ecological environment. j The results are standardized versions of the various indicators corresponding to the socio-economic situation.

[0097] S502: Using the evaluation results from step S501, establish a comprehensive evaluation model for the overall benefits of social park construction, where Com = Cw e ×Eco+Cw s ×Soc, where Com is the comprehensive benefit value of construction, and Cw is the value of the comprehensive benefit of construction. e As the weight of ecological and environmental indicators, Cw s Weighting of socioeconomic indicators.

[0098] The beneficial effects of this invention are:

[0099] 1. This invention constructs a dynamic process baseline model for evaluating the effectiveness of national park construction. By calculating the average values ​​of the ecological environment index, socio-economic index, and comprehensive benefit value for the five years before and after national park construction, and using national park construction as a time threshold, the comprehensive effectiveness after national park construction is compared and analyzed. In addition, to eliminate the fluctuations of climate factors, this invention also adopts a trend comparison method to calculate and compare the trends of the ecological environment index, socio-economic index, and comprehensive benefit value for the five years before and after national park construction, thereby analyzing the effectiveness of national park construction. Furthermore, by combining the model simulation variable control method, the method of comparing changes inside and outside the project area, and the trend comparison method, the contribution rate of national park construction and climate factors to ecological effectiveness is identified, thereby further verifying the effectiveness of national park construction and innovatively developing a method for evaluating the effectiveness of national park construction.

[0100] 2. This invention employs spatial mathematical and statistical methods to unify socio-economic data at the administrative unit scale with ecological and environmental data at the raster pixel scale, effectively solving the spatial mismatch problem between ecological and environmental data and socio-economic data encountered in most studies. Furthermore, it integrates the two to measure the development level of a region, realizing the fusion calculation of data of different categories and scales. This invention has significant reference value for related evaluation studies in comprehensive evaluation research.

[0101] 3. In this invention, the combination of entropy method and analytic hierarchy process is used to determine the weight of each evaluation index, which makes the calculation results of the evaluation index more accurate and thus improves the accuracy of the evaluation results.

[0102] 4. The selection of evaluation indicators in this invention is based on a deep understanding of the underlying purpose of the construction of the Sanjiangyuan National Park. It follows the principles of scientificity, relevance, accessibility, and quantifiability, and integrates indicators from both ecological environment and socio-economic aspects. It fully aligns with the ecological and social baseline conditions and construction goals of the Sanjiangyuan National Park. The resulting evaluation model can be directly used to quickly evaluate the comprehensive effectiveness of the national park's construction. Attached Figure Description

[0103] Figure 1 This is a technical roadmap for the comprehensive effectiveness evaluation method of national parks in this invention.

[0104] Figure 2 This is a spatial distribution map of the comprehensive benefit index of national parks before and after 5 years and a schematic diagram of the dynamic process background construction in this invention. Detailed Implementation

[0105] To enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0106] Taking the Sanjiangyuan National Park as an example, a comprehensive evaluation method for national parks based on dynamic process baselines is presented in the appendix. Figure 1 As shown, it includes the following steps:

[0107] S1: Obtain multi-source data for five years before and after the construction of the national park. The multi-source data includes multi-source data related to ground monitoring, field sampling, remote sensing, and statistics.

[0108] Specifically, data can be obtained from multiple sources within the national park area, including meteorological stations, remote sensing satellite data, relevant data websites, field measurement data, and statistical yearbooks.

[0109] Further, step S2: Establish a comprehensive evaluation index system for the effectiveness of national parks;

[0110] The following principles should be followed in the further screening of indicators: (1) Scientificity: Refer to the research of experts and scholars in relevant fields and combine with field research to scientifically screen indicators; (2) Targetedness: This invention takes the Sanjiangyuan National Park as a typical case area and selects indicators according to local conditions based on regional characteristics and resource and environmental endowments; (3) Accessibility: The data acquisition methods required in the indicator system should be relatively mature; (4) Quantifiability: Each indicator should be able to obtain quantitative results through scientific calculations to ensure the practical significance of the evaluation system. Based on the above principles, this invention finally selected 12 indicators in two major categories and three levels involving ecological environment and socio-economic aspects to construct a comprehensive evaluation indicator system for the construction effectiveness of national parks, as shown in Table 1 below. Among the ecological and environmental indicators, the Shannon Diversity Index (SHDI) is a negative indicator because a higher SHDI indicates greater landscape fragmentation, which is a major cause of biodiversity loss. A lower SHDI indicates greater ecological and environmental benefits. The other indicators, considering their respective ecological significance, are all positive indicators, meaning a higher SHDI indicates greater ecological benefits. Among the socio-economic indicators, based on the specific requirements for core conservation areas, traditional use areas, and ecological conservation and restoration areas, and considering the socio-economic significance of the indicators themselves, population density and livestock density are negative indicators, while the rest are positive indicators.

[0111] Table 1 Evaluation Index System for the Implementation Effect of the Sanjiangyuan National Park System Pilot Program

[0112]

[0113] Further, step S3: Determine the calculation method for all evaluation indicators in the comprehensive effectiveness evaluation index system of national parks;

[0114] Specifically, ecological and environmental indicators include:

[0115] (1) Vegetation coverage

[0116] Vegetation cover is calculated using a pixel-based binary model, assuming that each pixel's NDVI value consists of two parts: vegetation and soil. The calculation formula is as follows:

[0117]

[0118] In the formula, F vc NDVI is the normalized difference between vegetation cover and NDVI, which is an indicator of plant growth status and spatial distribution density of vegetation. soil The NDVI value for pixels with no vegetation cover is theoretically close to 0; NDVI vegThe NDVI value for a pure vegetation pixel is theoretically close to 1; however, in reality, due to the influence of surface environmental factors (such as temperature, surface humidity, and atmosphere), the vegetation species composition of pure vegetation pixels varies, resulting in a certain range of values. Based on the cumulative frequency value of NDVI, with a 5% confidence interval, the 5% NDVI value is set as the NDVI value. soil 95% of the NDVI value is NDVI veg .

[0119] (2) Net primary productivity

[0120] In this invention, net primary productivity (NPP) is selected to characterize the carbon sequestration capacity of vegetation in the Sanjiangyuan National Park. NPP is calculated using the EC-LUE model, a light energy utilization model developed based on eddy covariance carbon flux station data. This model follows Liebig's minimum factor law in ecology, meaning that the environmental factor ultimately limiting net primary productivity is the one that exerts the strongest stress on it. The formula is:

[0121] NPP = ε max ×Min(f(T)×f(W))×FPAR×PAR

[0122] FPAR = 1.24 × NDVI - 0.168

[0123] In the formula, NPP is net primary productivity, and ε max Potential light energy utilization (%), f(T) and f(W) are the light energy utilization (%) under environmental stresses of temperature and canopy moisture conditions, respectively, and PAR (Photosynthetically Active Radiation) is the incident photosynthetically active radiation (W / m²). 2 FPAR (Fraction of PAR absorbed by the vegetation canopy) is the proportion of photosynthetically active radiation absorbed by the plant canopy. The product of PAR and FPAR is the photosynthetically active radiation absorbed by the plant canopy (W / m²). 2 );

[0124] (3) Water conservation capacity

[0125] This invention uses the precipitation storage method to calculate water conservation capacity. This method measures the water conservation capacity based on the hydrological regulation effect of the ecosystem. The formula is as follows:

[0126] S = 10M × Q × E

[0127] Q = Q0 × K

[0128] E = E0 - Eg

[0129] E g = -0.3187 × F vc +0.3640

[0130] In the formula, S represents the increase in water retention capacity of the terrestrial ecosystem compared to bare land; M represents the ecosystem area; Q represents the annual runoff; Q0 represents the average annual precipitation; E represents the efficiency coefficient of the ecosystem in reducing runoff compared to bare land; K represents the proportion of runoff to total precipitation, with a value of 0.68; E0 represents the bare land runoff rate under runoff conditions, with a value of 0.36403; E g The ecosystem rainfall-runoff rate under runoff-producing conditions;

[0131] (4) Soil retention

[0132] Soil retention capacity is calculated using a modified general soil loss equation, and the formula is as follows:

[0133] TB = TB q -TB r

[0134] TB q =K q ×M

[0135] TB r =K r ×M

[0136] In the formula, TB represents the ecosystem soil retention capacity (t) at the computational unit; TB q The potential soil erosion (t) in a unit cell without vegetation protection is calculated; TB r K represents the amount of soil erosion (t) under the actual land cover condition on the calculation unit. q The potential soil erosion modulus (t / hm²) is calculated for a unit without vegetation cover. 2 ); K r To calculate the soil erosion modulus (t / hm) under the actual land cover condition of the unit. 2 );

[0137] The soil erosion modulus was calculated using a modified general soil loss equation.

[0138] T = J × D × P × H × F × W

[0139] In the formula, T is the soil erosion modulus flux (t / hm). 2 J represents the precipitation erosivity factor (MJ·mm·hm). -2 ·h -1 ·a); D is the soil erodibility factor (t·hm). 2 ·h·hm-2 ·MJ·mm); P is the slope length factor; H is the slope gradient factor; F is the cover and management factor, with a value range of 0 to 1; W is the soil and water conservation measures factor, with a value range of 0 to 1;

[0140] (5) Windbreak and sand fixation volume

[0141] The amount of windbreak and sand fixation is calculated using a modified soil erosion equation. The calculation formula is as follows:

[0142] DG = DG q -DG r

[0143] DG q =WL q ×A

[0144] DG r =WL r ×A

[0145] In the formula, DG represents the amount of windbreak and sand fixation in the ecosystem at the calculation unit (t); DG q The potential soil wind erosion (t) is calculated for a unit without vegetation protection; DG r To calculate the amount of soil wind erosion (t) under the actual cover condition on the calculation unit; WL q The potential soil wind erosion modulus (t / hm) is calculated for a unit without vegetation protection. 2 WL r The soil wind erosion modulus (t / hm) under the actual land cover condition on the calculation unit. 2 A represents the area of ​​the calculation unit (hm²). 2 );

[0146] The wind erosion modulus is calculated using the modified soil wind erosion equation RWEQ, and the formula is as follows:

[0147]

[0148]

[0149] N max =109.8(WF×BF×SCF×C×FG)

[0150] L=150.71(WF×BF×SCF×C×FG) -0 . 3711

[0151] In the formula, WL is the wind erosion modulus (kg / m²). 2 X is the length of the plot (m), taken as 100m; N x The sand flux at location x (kg / m³); N max1. Maximum wind-driven sand transport capacity (kg / m); 2. Length of key plot (m); 3. Meteorological factor (kg / m); 4. Soil erodibility component (kg / m); 5. Soil crusting factor (SCF); 6. Soil roughness factor (C); 7. Vegetation factor (FG).

[0152] (6) Shannon Diversity Index

[0153] Shannon diversity index (SHDI) is a landscape pattern index based on information theory. It is highly sensitive to the heterogeneity of the same landscape at different times and among different landscapes. In this case, SHDI is an important indicator. The calculation formula is as follows:

[0154]

[0155] In the formula, SHDI is the Shannon Diversity Index, p i The percentage (%) occupied by land use / cover type i.

[0156] Socioeconomic indicators include:

[0157] (1) Population density and GDP density

[0158] Since the obtained population density and GDP density data are from 2010 and 2015, this study calculates the population density and GDP density for 2011-2020 based on the annual population density growth rate and annual GDP growth rate of the four counties related to the Sanjiangyuan National Park obtained from the statistical yearbook. The formula is as follows:

[0159] POP i+1 =POP i ×k i+1

[0160] GDP i+1 =GDP i ×k i+1

[0161] In the formula, POP i Population density (people / km) in year i 2 ), k i+1 The population density growth rate (%) in year i+1, and GDP j GDP density (ten thousand yuan / km²) in year j 2 ), k j+1 The GDP density growth rate (%) in the first year of production;

[0162] (2) Medical service capacity

[0163] In this invention, the number of available medical beds per resident in a region is used to assess its medical service capacity. First, all medical institutions within the four counties related to the Sanjiangyuan National Park are obtained through online data collection (the influence of medical institutions in other counties is not considered). Second, information about each hospital is retrieved to obtain its establishment date and corresponding hospital level according to the "Hospital Classification Management Standards". Then, based on the "National Medical and Health Service System Planning Outline", the medical coverage capacity of different hospital levels within designated buffer zones (50km, 100km, 150km, 200km, 500km) is defined, as shown in Table 2 below, yielding the theoretical medical service capacity of each region in the Sanjiangyuan National Park. Using the theoretical medical service capacity of different regions, the number of medical institution beds in the four counties from 2011 to 2020 can be rasterized using ArcGIS, and then divided by the population density of the Sanjiangyuan National Park to obtain the actual spatial distribution data of medical service capacity. The calculation formula is as follows:

[0164]

[0165]

[0166] In the formula, NBMI ij This represents the number of medical and health institution beds in the i-th row and j-th column of the grid, with a grid size of 1km × 1km; NBMI cou This represents the statistical value (number of beds) of medical and health institutions in the county-level administrative region where the grid cell is located. (MSTA) ij For the theoretical medical service capacity (%) of this grid cell, MSTA con The total theoretical medical service capacity (%) of the county-level administrative unit where the grid cell is located, MSC ij For the healthcare service capacity (people / person) of the grid in row i and column j, POP ij This represents the population (in people) of this grid cell.

[0167] Table 2 Coverage Capacity of Hospitals at Different Levels

[0168]

[0169] (3) Density of employees in the tertiary sector

[0170] Existing research indicates that population density has a significant impact on the density of employees in the tertiary sector. Therefore, based on the tertiary sector employee density data obtained from four counties related to the Sanjiangyuan National Park from 2011 to 2020, and combined with the corresponding population density distribution of the Sanjiangyuan National Park, this invention derives the spatial distribution data of the tertiary sector employee density in the Sanjiangyuan National Park over the ten-year period. The formula is as follows:

[0171]

[0172] In the formula, PETI ij The tertiary sector employment density (in people) is represented by the grid cell in the i-th row and j-th column, with a grid size of 1km × 1km; PETI cou This is the statistical value (in people) of the tertiary industry employment density of the county-level administrative region where the grid cell is located; POP cou This represents the total population (in people) of the county-level administrative unit where the grid cell is located.

[0173] (4) Balance of residents' savings deposits

[0174] Existing research indicates a strong positive correlation between GDP and residents' savings deposit balances. Therefore, based on the residents' savings deposit balances of four counties related to the Sanjiangyuan National Park from 2011 to 2020, and combined with the spatial distribution data of GDP in the Sanjiangyuan National Park for the corresponding years, this invention derives the spatial distribution of residents' savings deposit balances in the Sanjiangyuan National Park over the ten-year period. The formula is as follows:

[0175]

[0176] In the formula, SDBR ij This represents the balance (in ten thousand yuan) of residents' savings deposits in the i-th row and j-th column of the grid, with a grid size of 1km × 1km; SDBR cou This is the statistical value (in ten thousand yuan) of residents' savings deposits in the county-level administrative region where the grid cell is located; GDP ij This represents the GDP (in ten thousand yuan) for this grid cell; GDP cou This represents the total GDP (in ten thousand yuan) of the county-level administrative unit where the grid cell is located;

[0177] (5) Livestock density

[0178] Animal husbandry plays a vital role in the lives and production of residents in the Sanjiangyuan National Park area. For a long time, animal husbandry in the Sanjiangyuan region has directly exerted significant pressure on the grassland ecosystem. Since the distribution of livestock numbers is strongly correlated with altitude, distance from settlements, distance from water sources, and grassland conditions, this invention selects distance from settlements, altitude, NPP (Natural Per Scale), and distance from water sources as influencing factors on the spatial distribution of livestock numbers. First, the four factors are tailored based on the distribution of grassland ecological types in the core conservation area, ecological conservation and restoration area, and traditional use area of ​​the Sanjiangyuan National Park, and then normalized and dimensionless. Specifically, since productive livestock activities are completely prohibited in the core conservation area, and strict grassland-livestock balance, seasonal grazing rest, and rotational grazing are implemented in the ecological conservation and restoration area and the traditional use area, but because a certain scale of herding households already existed in the core conservation area before the establishment of the national park, grazing still exists to varying degrees in all three areas. Therefore, based on field surveys and expert consultations, different grazing probabilities are assigned to the core conservation area, ecological conservation and restoration area, and traditional use area. Simultaneously, a hierarchical model was constructed, and the weights of the four influencing factors were obtained through expert scoring. By multiplying the normalized results of different factors by their corresponding weights and the grazing probabilities of different areas, livestock activity intensity scores for different grids were obtained. Combined with livestock numbers in the four counties from 2011 to 2020, the final distribution of livestock numbers in the Sanjiangyuan National Park could be obtained. The calculation formula is as follows:

[0179]

[0180] In the formula, LD ij It is the number of livestock (SHU) in the i-th row and j-th column of the grid, and the grid size is 1km × 1km; LD cou This is the livestock population statistic (SHU) for the county-level administrative region where the raster cell is located; LAI ij This represents the livestock activity intensity of the grid cell; LAI cou This represents the total livestock activity intensity of the county-level administrative unit where the grid cell is located.

[0181] Further, step S4: Determine the weights of each evaluation index based on the entropy method and the analytic hierarchy process;

[0182] Specifically, in this invention, the determination of the weights of the evaluation index system adopts a combination of the entropy method and the analytic hierarchy process (AHP). That is, the weights of the indicators are calculated using both the entropy method and AHP, and the average of the two is taken as the final weight. First, the entropy method is an objective weighting method. It measures the weight of an indicator by calculating its information entropy. The higher the information entropy, the less information the system carries, and the smaller the corresponding indicator weight. The advantage of this method is its objectivity, eliminating the influence of subjective bias compared to subjective weighting methods. However, its disadvantages include a lack of horizontal comparison between indicators, and the weights are entirely dependent on the data sample, requiring extremely high accuracy. Second, the analytic hierarchy process (AHP) is a method combining qualitative and quantitative approaches. It constructs a multi-level analytical structure model of the various influencing factors of a complex system, analyzes and calculates between each level to obtain the weights. The advantages of this method are its simple and clear calculation, its focus on the essence of the problem, and its lower data requirements. However, its disadvantage is its strong subjectivity. Therefore, this invention employs a combined approach, which not only compensates for the shortcomings of each evaluation method, allowing them to complement each other, but also enables a comparison of the weight calculation results from the two methods, making the final weight calculation more scientific and reasonable. The calculation steps of the two methods are as follows:

[0183] (1) Entropy method

[0184] S4011: Perform range standardization on the raw values ​​of each evaluation indicator;

[0185] For positive indicators:

[0186]

[0187] For negative indicators:

[0188]

[0189] S4012: Calculate the weight of the j-th indicator value for the i-th sample.

[0190]

[0191] S4013: Calculate the entropy value of the j-th index.

[0192] S4014: Calculate the weight of the j-th indicator.

[0193] In the formula, X′ ij X is the standardized value of the j-th indicator for the i-th sample; ij X is the original value of the j-th indicator for the i-th sample; maxX is the maximum value of the j-th indicator in the i-th sample; min Let be the minimum value of the j-th indicator for the i-th sample; n is the number of samples.

[0194] (2) Analytic Hierarchy Process

[0195] S4021: Construct a judgment matrix based on the indicator system: By using expert scoring, compare indicators pairwise within the same level to construct the judgment matrix for that level.

[0196]

[0197] In the formula, u xy Indicate u x Relative to u y Importance values, x = 1, 2, ..., t, y = 1, 2, ..., t; t represents the number of indicators at this level;

[0198] S4022: Calculate the corresponding maximum eigenvalue λ based on the judgment matrix U. max The eigenvector W is then normalized to obtain the weight vector, i.e., UW = λ. max ×W, where UW is the weight vector;

[0199] S4023: Perform a consistency check on the weight vector obtained in step S4022.

[0200] Step 1: First, calculate the consistency index.

[0201] Step 2: Based on the number of factors, find and confirm the corresponding average random consistency index RI from Table 3;

[0202] Step 3: Calculate the consistency ratio

[0203] Table 3. RI values ​​corresponding to different numbers of factors.

[0204]

[0205] When the calculated judgment matrix U has λ max When CI = n, CI = 0, or CR < 0.1, the consistency of the judgment matrix U is considered acceptable; otherwise, the judgment matrix U needs to be adjusted appropriately to ensure its consistency meets the requirements.

[0206] After calculating the weights of each indicator in the evaluation index system using the entropy method and the analytic hierarchy process, the average of the weights obtained by the two methods is taken to obtain the final weights of each indicator in the evaluation index system, as shown in Table 4 below.

[0207] Table 4. Weight values ​​of each indicator in the evaluation index system

[0208]

[0209] Further, step S5: Based on the weight values ​​of each evaluation indicator, establish an evaluation model for the comprehensive effectiveness of the national park.

[0210] Specifically, S501: By multiplying the weight of each indicator by the standardized value of its range, and summing the results, the corresponding ecological and environmental indicator evaluation results and socio-economic indicator evaluation results are obtained.

[0211]

[0212]

[0213] In the formula, Eco represents the evaluation result of ecological and environmental indicators, Soc represents the evaluation result of socio-economic indicators, and Ew represents the evaluation result of socio-economic indicators. i For the weights of various indicators corresponding to the ecological environment, Sw j EI represents the weights of various indicators corresponding to socioeconomic conditions. i SI represents the standardized results of various indicators corresponding to the ecological environment. j The results are standardized versions of the various indicators corresponding to the socio-economic situation.

[0214] S502: Using the evaluation results from step S501, establish a comprehensive evaluation model for the overall benefits of social park construction, where Com = Cw e ×Eco+Cw s ×Soc, where Com is the comprehensive benefit value of construction, and Cw is the value of the comprehensive benefit of construction. e As the weight of ecological and environmental indicators, Cw s Weighting of socioeconomic indicators.

[0215] Further, step S6: Evaluate the overall effectiveness of the national park based on the comparison of average conditions before and after the project and the comparison of changing trends.

[0216] Specifically, using the comprehensive evaluation model of the overall benefits of national park construction obtained in step S5, the comprehensive benefits of the national park in the five years before and after its construction are calculated, and the results are attached. Figure 2 As shown in the attached figure, the trends of the comprehensive benefit indicators before and after the establishment of the national park in the five years prior to its completion are as follows. Figure 2 As shown in (c) in the figure, from Figure 2 The data clearly shows that the comprehensive benefit index of national parks increased significantly five years after their completion.

[0217] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating the overall effectiveness of a national park based on the dynamic process background, characterized in that, The method comprises the following steps, S1: obtaining multi-source data of 5 years before and after the construction of the national park; S2: establishing a comprehensive effect evaluation index system of the national park; S3: determining the evaluation index data in step S2; S4: determining the weight of the evaluation index based on the entropy value method and the analytic hierarchy process; S5: establishing an evaluation model of the comprehensive effect of the national park according to the weight values of the evaluation indexes obtained in step S4; S6: evaluating the comprehensive effect of the national park based on the average condition comparison method and the change trend comparison method before and after the project; The comprehensive effect evaluation index system of the national park in step S2 comprises a target layer, a criterion layer and an index layer; The target layer comprises ecological environment indexes and social economic indexes; The criterion layer corresponding to the ecological environment indexes comprises ecological system quality, ecological system function and landscape pattern index; The criterion layer corresponding to the social economic indexes comprises social benefit and economic benefit; The index layer corresponding to the ecological system quality comprises vegetation coverage and net primary productivity; the index layer corresponding to the ecological system function comprises water conservation capacity, soil conservation capacity and wind prevention and sand fixation capacity; the index layer corresponding to the landscape pattern index comprises Shannon diversity index; The index layer corresponding to the social benefit comprises population density, medical service capacity and third industry employee density; the index layer corresponding to the economic benefit comprises GDP density, resident savings balance and livestock density; The specific operation of step S4 comprises the following steps, S401: calculating the weight of each evaluation index by using the entropy value method; S402: calculating the weight of each evaluation index by using the analytic hierarchy process; S403: taking the average value of the evaluation index weight calculated by the entropy value method and the evaluation index weight calculated by the analytic hierarchy process as the final weight of the evaluation index; The specific operation of step S5 comprises the following steps, S501: multiplying the weight corresponding to each index by the standardized value of the index range, and adding to obtain the evaluation results of the ecological environment indexes and the social economic indexes, that is ； ； In the formula, is the evaluation result of the ecological environment index, is the evaluation result of the social economy index, is the weight of each index corresponding to the ecological environment, Sw j is the weight of each index corresponding to the social economy, is the result of each index corresponding to the ecological environment after standardization, is the result of each index corresponding to the social economy after standardization; S502: using the evaluation result in step S501, a comprehensive evaluation model of the comprehensive benefit of the social park construction is established, , wherein, is the construction comprehensive benefit value, is the ecological environment index weight, is the social economic index weight.

2. The method for evaluating the comprehensive performance of a national park based on a dynamic process background according to claim 1, characterized in that: The calculation method of the index layer evaluation index corresponding to the ecological environment indexes in step S3 comprises, The vegetation coverage is calculated by using the pixel bisection model, and the calculation formula is: ； where F vc is the vegetation coverage; is the normalized difference vegetation index, which is an indicator of the plant growth status and the spatial distribution density of vegetation; is the NDVI value of the full non-vegetation coverage pixel; is the NDVI value of the pure vegetation pixel; The net primary productivity is calculated by using the EC-LUE model, and the calculation formula is: ； ； wherein is the net primary productivity, is the potential light use efficiency; and are the light use efficiencies under stress of temperature and canopy water regime in the environment, respectively; PAR is the incident photosynthetically active radiation; FPAR is the fraction of photosynthetically active radiation absorbed by the plant canopy. The water conservation capacity is calculated by using the precipitation storage method, and the calculation formula is: ； ； ； ； In the formula, S is the increase of water conservation of the land ecological system compared with bare land; M is the area of the ecological system; Q is the annual runoff rainfall; Q0 is the annual average rainfall; E is the benefit coefficient of runoff reduction of the ecological system compared with bare land; K is the proportion of runoff rainfall to rainfall, and is 0.68; E0 is the rainfall runoff rate of bare land under the condition of runoff rainfall, and is 0.36403; E g is the rainfall runoff rate of the ecological system under the condition of runoff rainfall. The soil conservation capacity is calculated by using the modified universal soil loss equation, and the calculation formula is: ； ； ； ； where TB is the ecosystem soil retention on the calculation unit; TB q is the potential soil erosion without vegetation cover on the calculation unit; TB r is the soil erosion with the actual vegetation cover on the calculation unit; K q is the potential soil erosion modulus without vegetation cover on the calculation unit; K r is the soil erosion modulus with the actual vegetation cover on the calculation unit; T is the soil erosion modulus flux; J is the precipitation erosivity factor; D is the soil erodibility factor; P is the slope length factor; H is the slope factor; F is the coverage and management factor, and the value range is 0-1; W is the water and soil conservation measure factor, and the value range is 0-1; The wind prevention and sand fixation capacity is calculated by using the modified soil erosion equation, and the calculation formula is: ； ； ； ； ； ； ； In the formula, DG is the amount of wind and sand prevention and fixation of the ecosystem on the calculation unit; DG q is the potential soil erosion amount without vegetation protection on the calculation unit; DG r is the soil erosion amount under the actual vegetation state on the calculation unit; WL q is the potential soil erosion modulus without vegetation protection on the calculation unit; WL r is the soil erosion modulus under the actual vegetation state on the calculation unit; A is the area of the calculation unit; WL is the wind erosion modulus; X is the length of the land block; N x is the sand flux at the land block x; N max is the maximum sand carrying capacity of the wind; L is the critical land block length; WF is the meteorological factor; BF is the soil erodible component; SCF is the soil crust factor; C is the soil roughness factor; FG is the vegetation factor; The Shannon diversity index is calculated by using the landscape pattern software Fragstats, and the calculation formula is: ； where SHDI is the Shannon diversity index, is the fraction of the land use / cover type i occupied.

3. The method of claim 2, wherein: The calculation method of the index layer evaluation index corresponding to the social economic indexes in step S3 comprises, The calculation method of the population density and the GDP density is: ； ； wherein is the population density in the i-th year, is the population density growth rate in the i+1-th year, is the GDP density in the j-th year, is the GDP density growth rate in the j+1-th year; The calculation method of the medical service capacity is: ； ； wherein, is the number of medical and health institutions beds of the grid cell in the i-th row and j-th column, is the number of medical and health institutions beds of the county-level administrative unit in which the grid cell is located, is the theoretical medical service capacity of the grid cell, is the total theoretical medical service capacity of the county-level administrative unit in which the grid cell is located, is the medical service capacity of the grid cell in the i-th row and j-th column, is the population of the grid cell; The calculation method of the third industry employee density is: ； wherein, is the third industry employee density of the i-th row and j-th column grid, is the third industry employee density statistical value of the county-level administrative unit in which the grid unit is located; is the total population of the county-level administrative unit in which the grid unit is located; The calculation method of the resident savings balance is: ； wherein, is the resident savings account balance of the i-th row and j-th column grid, is the resident savings account balance statistical value of the county-level administrative unit in which the grid unit is located; is the GDP of the grid unit; is the total GDP of the county-level administrative unit in which the grid unit is located; The calculation method of the livestock density is: ； wherein is the number of livestock in the i-th row and j-th column grid; is the number of livestock in the county administrative unit in which the grid cell is located; is the livestock activity intensity of the grid cell; is the total livestock activity intensity of the county administrative unit in which the grid cell is located.

4. The method of claim 3, wherein, The specific operation of step S401 includes the following steps, S4011: range standardization processing is performed on the original numerical values of each evaluation index; wherein, for the positive indicators, ; For negative indicators, ; S4012: the proportion of the jth index value of the ith sample is calculated, ； S4013: calculate the entropy value of the jth index, ; S4014: calculate the weight of the jth index, ; In the formula, is the normalized value of the jth index of the ith sample; is the original value of the jth index of the ith sample; is the maximum value of the jth index of the ith sample; is the minimum value of the jth index of the ith sample; and n is the number of samples.

5. The method of claim 4, wherein, The specific operation of step S402 includes the following steps, S4021: constructing a judgment matrix according to the index system: through the method of expert scoring, the indexes in the same level are compared two by two to construct the judgment matrix of the level: ； wherein denotes u x the importance value of u y x = 1,2,..., t, y = 1,2,..., t; t denotes the number of the hierarchical indexes S4022: According to the judgment matrix U, the corresponding maximum eigenvalue is calculated With eigenvector W, then the eigenvector is normalized to obtain the weight vector, that is , UW is the weight vector; S4023: consistency detection is performed on the weight vector obtained in step S4022.

6. The method of claim 5, wherein, The specific operation of step S4023 includes the following steps, Step 1 : First, the consistency index is calculated ; Step 2: confirming the average random consistency index RI according to the number of factors; Step 3: Calculate the proportion of consistency .

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