Ecological safety early warning and evaluation system based on three-generation space coupling and multi-source model fusion

Abstract

The invention discloses an ecological safety early warning evaluation system based on three-generation space coupling and multi-source model fusion. The system comprises an index framework construction module, a weight determination module, an ecological safety level evaluation module, a three-generation space coupling evaluation module and an ecological safety early warning evaluation module. The invention provides an ecological safety early warning evaluation system, which masters the possibility of occurrence of ecological environment problems and spatial and temporal distribution characteristics, and provides a scientific basis for management of governments and environmental protection departments in the region. According to the method, indexes are selected according to historical spatial data and various statistical yearbook, an ecological safety evaluation system is constructed based on a three-generation space theory, three-generation coupling coordination is calculated according to different land utilization functions, and finally an ecological safety early warning area is determined according to the space-time relationship between the two.

Description

Technical Field

[0001] This invention relates to the field of ecological security assessment technology, specifically to an ecological security early warning and assessment system that combines three-life space coupling with multi-source model fusion. Background Technology

[0002] With rapid economic and social development and the increasing intensity of human activities, ecosystems are facing pressure. Therefore, constructing a scientific and systematic ecological security assessment technology system and conducting timely ecological security early warnings are of great significance for achieving coordinated progress in ecological protection and high-quality development.

[0003] Traditional ecological security assessment methods often rely on single data sources and specific models or indicator systems, making it difficult to comprehensively reflect the multidimensional state and dynamic changes of ecosystems. Furthermore, existing assessment methods have limitations in determining indicator weights, ensuring result reliability, and identifying regional spatial heterogeneity, leading to insufficient scientific rigor and stability of the assessment results. In addition, ecological security is often closely related to land use configuration, especially the degree of coupling and coordination among the three types of spaces—production, living, and ecological—which directly affects the overall effectiveness of the regional ecosystem. How to organically combine ecological security assessment with the coupling analysis of these three spaces to achieve comprehensive fusion of multi-source data, integrated assessment of multiple models, and spatiotemporal dynamic identification and early warning of results is a key issue that urgently needs to be addressed in current ecological assessment technology.

[0004] Therefore, there is an urgent need to construct an ecological security early warning and assessment system based on the coupling of the three-life space and the fusion of multi-source models. This system should comprehensively utilize multi-source data such as natural, meteorological, remote sensing, and socio-economic data, integrate subjective and objective weighting methods with advanced assessment models, systematically assess the regional ecological security level, quantify the coordination degree of the three-life space, identify potential ecological risk areas, and achieve ecological security early warning and precise spatial identification. This will provide a scientific basis and technical support for ecological protection and regional spatial optimization. Summary of the Invention

[0005] The purpose of this invention is to provide an ecological security early warning and assessment system that combines three-life space coupling and multi-source model fusion to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an ecological security early warning and assessment system based on the coupling of three-life space and the fusion of multi-source models, comprising an indicator framework construction module, a weight determination module, an ecological security level assessment module, a three-life space coupling assessment module, and an ecological security early warning and assessment module;

[0007] The indicator framework construction module integrates multi-source data such as natural, meteorological, remote sensing and socio-economic data. Based on the "Production-Living-Ecology" PLES spatial concept and the "Stress-State-Response" PSR model, it constructs a comprehensive ecological security evaluation indicator framework of "PLES-PSR" from four dimensions: natural environment, socio-economic, resource utilization and ecological function.

[0008] The weight determination module uses Bayesian BWM and CRITIC methods to determine subjective and objective weights respectively, and improves the stability and scientific nature of the index weights through a combined weighting strategy based on distance function CWDF.

[0009] The ecological security level assessment module improves the traditional TOPSIS method and combines it with grey relational analysis (GRA) to calculate the regional ecological security level, thereby realizing the comprehensive assessment of ecological security and identification of dynamic spatiotemporal distribution.

[0010] The three-life space coupling assessment module scores the dominant functions of the three-life space based on land use data, calculates the coupling coordination degree of the three-life space using an improved coupling coordination degree model, and identifies the coordination differences and evolution trends between regions.

[0011] The ecological security early warning and assessment module divides early warning zones based on the ecological security level and the coordination results of the three-life coupling, supporting ecological protection and spatial control decisions.

[0012] Preferably, the indicator framework construction module specifically selects the following indicators:

[0013] Ecological space: stress indicators include average annual precipitation, average annual temperature, average annual wind speed, and fertilizer application rate; status indicators include wetland soil salinity index, wetland density index, landscape diversity index, landscape fragmentation index, and biodiversity index; response indicators include hydrological regulation index, wetland degradation index, desertification area ratio, and conversion rate of non-ecological land to ecological land.

[0014] Production space: Pressure indicators include per capita GDP, the proportion of primary and secondary industry output, the number of large livestock, and the total output value of agriculture, forestry, animal husbandry and fishery; Status indicators include land reclamation rate, per capita grain output, per capita construction land area, and net primary productivity; Response indicators include comprehensive utilization rate of general industrial solid waste, the proportion of tertiary industry output, and the scientific and cultural value of wetlands.

[0015] Living space: Stress indicators include natural population growth rate, population density, and night light index; status indicators include per capita daily domestic water consumption, per capita park green space area, and number of ordinary middle schools; response indicators include green coverage rate of built-up areas and proportion of education expenditure.

[0016] Preferably, in the weight determination module, Bayesian BWM determines subjective weights: determining the best and worst indicators, constructing a comparison matrix, aggregating expert opinions through a probability model, and calculating the cumulative weight and the weight of each decision-maker;

[0017] CRITIC determines objective weights by normalizing the raw data, calculating the coefficient of variation and conflict, obtaining the information content of the indicators, and then determining the normalized weights.

[0018] Distance-based weighted combination: Construct a distance function to calculate the degree of difference between subjective and objective weights, and determine the combined weights through linear weighting.

[0019] Preferably, the ecological security level assessment module establishes an index standardization matrix, determines positive and negative ideal solutions, calculates the distance between the evaluation object and the positive and negative ideal solutions, determines the grey relational coefficient, calculates the relative proximity of the grey correlation coefficient, determines the ecological security level based on the relative proximity, and uses the natural breakpoint method for classification.

[0020] Preferably, the three-life space coupling assessment module scores the dominant functions of the three-life spaces based on land use data, calculates the coupling coordination degree of the three-life spaces using an improved coupling coordination degree model, and identifies the coordination differences and evolution trends between regions; it scores the dominant functions of cultivated land, construction land, forest land, grassland, water area, unused land or other types of land use.

[0021] The coupling degree and the comprehensive evaluation index of the three-life space are calculated to obtain the coupling coordination degree, and the spatiotemporal variation characteristics of the three-life coupling coordination are analyzed.

[0022] Preferably, the ecological security early warning assessment module divides ecological security into five levels: unsafe zone I, relatively unsafe zone II, moderately safe zone III, relatively safe zone IV, and safe zone V; and divides the coupling and coordination of the three-life space into five levels: severely unbalanced zone I, moderately unbalanced zone II, basically coordinated zone III, moderately coordinated zone IV, and well coordinated zone V. ArcGIS is used to overlay and analyze the two levels to establish early warning zones.

[0023] The ecological security early warning and assessment method based on the coupling of three-source space and the fusion of multi-source models includes the following steps:

[0024] (1) The indicator framework is constructed based on multi-source data of "nature-meteorology-remote sensing-socioeconomic data", integrating the spatial concept of "production-life-ecology" and the "stress-state-response" model PSR, selecting multi-dimensional indicators of natural environment, socio-economic, resource utilization and ecological function to form a scientific ecological security evaluation indicator framework.

[0025] (2) Weight determination: Bayesian optimal-worst method (BWM), CRITIC objective weighting method and CWDF combined weighting strategy are adopted to calculate the weights of multiple indicators and improve the stability and scientific nature of the indicator weights.

[0026] (3) Ecological security level assessment: The improved TOPSIS method and grey relational analysis model are introduced to calculate the regional ecological security level, so as to realize the comprehensive assessment of ecological security and the identification of dynamic spatiotemporal distribution.

[0027] (4) Coupling assessment of the three-life space: Based on land use data, the three-life space is quantified, and the coupling coordination degree of the three-life space is assessed using an improved coupling coordination degree model.

[0028] (5) Ecological security early warning assessment: Based on the assessment results of ecological security and the three-life coupling and coordination, the two are analyzed in a coordinated manner to identify potential areas of ecological function imbalance and areas with high incidence of spatial conflict, and ecological security early warning zoning is carried out.

[0029] Preferably, in step (1), the "Pressure-State-Response" model PSR is widely used to assess the causal relationship between "nature-society-economy"; where "pressure" originates from the overexploitation of resources, "state" reflects changes in the structure and function of the ecosystem, and "response" refers to the regulation and restoration measures taken by the government and society; combined with the spatial functional characteristics of the "production-life-ecology" PLES in the region, PSR indicator systems corresponding to the three types of spaces are constructed respectively, and integrated to form a comprehensive evaluation framework of "PLES-PSR" to systematically assess the level of ecological security; in step (2), the subjective weights of the indicators are determined by Bayesian BWM, and the objective weights of the indicators are determined by CRITIC. In order to combine the advantages of the two methods and minimize the difference between the weights and distribution coefficients obtained from the subjective and objective methods, a combined weighting method based on the distance function CWDF is used to comprehensively assign weights to the indicators;

[0030] The calculation method is as follows:

[0031] Determining Subjective Weights: Bayesian Bayesian Weighting (BWM) is a recently emerging method for determining subjective weights. It reduces the number of comparisons between indicators, thus simplifying the calculation process. Furthermore, it employs probabilistic methods to integrate the different weighting opinions of multiple evaluators, minimizing the single subjectivity and information loss in the decision-making process, resulting in more accurate evaluation results. The calculation formula is as follows:

[0032] Step 1: Determine the optimal indicators from the evaluation indicator system and worst indicators

[0033] Step 2: Construct comparison criteria; use numbers 1-9 to determine the preference for the best indicator relative to all other indicators, where 1 represents equal importance and 9 represents extreme importance. The larger the number, the greater the importance. This results in a comparison matrix:

[0034]

[0035] Step 3: Determine the degree of preference for all other indicators relative to the worst criterion, and obtain the comparison matrix:

[0036]

[0037] Step 4: Calculation of Cumulative Weights: The Bayesian BWM method given below, from a probabilistic perspective, shows that expert opinions cluster together; based on the following probabilistic model, the cumulative weights are obtained. and the weight w of each decision-maker k k = 1, ..., K;

[0038]

[0039] γ~gamma(0.1,0.1), w agg ~Dir(1), where Dir is a Dirichlet distribution and gamma(0.1,0.1) is a gamma distribution with a shape parameter of 0.1;

[0040] Determining objective weights: CRITIC is a weighting method based on objective data. It not only considers the amount of information carried by each indicator, but also the duplicate information caused by the correlation between indicators. Therefore, CRITIC can more comprehensively evaluate the importance and differences of indicators, and is more objective and accurate in weighting.

[0041] Step 1: Normalize the original data matrix. In the ecological security assessment based on the indicator method, the assessment indicators such as GDP and natural population growth rate have inconsistent dimensions and need to be standardized. The standardized calculation formulas for positive and negative indicators are as follows:

[0042]

[0043] Step 2: Calculate the coefficient of variation:

[0044]

[0045] In the formula: x j s is the mean of the j-th indicator; j Let j be the standard deviation of the j-th indicator;

[0046] Step 3: Calculate conflict rates; conflict rates are calculated using R.j This means that the stronger the correlation between the j-th evaluation index and other indicators, the more similar information it reflects, and the lower the conflict rate (R). j The smaller; R j The calculation formula is:

[0047]

[0048] r ij The correlation coefficient between indicator i and indicator j

[0049]

[0050] Step 4: The information content of the j-th indicator is

[0051]

[0052] Step 5: The normalized weight of the j-th indicator is

[0053]

[0054] Distance-based weighted combination: To simultaneously consider the advantages of both weighting methods, it is necessary to ensure that the differences between the weights and distribution coefficients of the subjective and objective methods are the same. Therefore, this study determines the degree of difference by calculating the distance between the two types of weights, and then determines the combined weights by linear weighting.

[0055] Construct the distance function relationship:

[0056]

[0057] Where X is the weight value calculated by the Bayesian BWM method, and Y is the weight value calculated by the CRITIC method.

[0058] W is the combined weight value, which is a linear weighted sum of the two methods; the expression for the combined weight value is:

[0059] W = aX + bY

[0060] a and b are two proportions with different weights, and the simplified expression is as follows:

[0061]

[0062] Preferably, in step (3), the traditional TOPSIS method of first weighting and then calculating the Euclidean distance amplifies the influence of the weights. Therefore, it is changed to calculate the Euclidean distance and then weight it simultaneously. At the same time, in order to overcome the shortcomings of TOPSIS in reflecting nonlinear utility changes, grey relational analysis is introduced. The combination of the two improves the stability and adaptability of the comprehensive evaluation results. Finally, the ecological security is classified based on the natural discontinuity method. The calculation steps are as follows:

[0063] Step 1: Establish a standardized matrix of indicators;

[0064] Step 2: Determine the positive and negative ideal solutions

[0065]

[0066] Among them, the ideal solution r + and negative ideal solution r - It consists of the maximum and minimum values ​​of each indicator;

[0067] Step 3: Calculate the distance from the evaluation object to the positive and negative ideal solutions.

[0068]

[0069] and These are the distances from the j-th evaluation object to the positive and negative ideal solutions, respectively, w j Let be the weight of the j-th indicator;

[0070] Step 4: Determine the grey relational coefficient m + and m -

[0071]

[0072] and Let be the grey relation of the j-th evaluation object in the positive ideal solution and the negative ideal solution, respectively. The difference between the two minimum values. The difference between the two maximum values ​​is ξ, which is the discriminant coefficient, ξ∈[0,1]. It is used to improve the variability of the correlation coefficient and is set to 0.5.

[0073] Step 5: Calculate the relative closeness of the grey correlation coefficient.

[0074]

[0075] Where β = δ = 0.5; T ranges from 0 to 1, and the larger T is, the closer the ecological security is to the optimal level; in this study, T is used to represent ecological security, thereby determining the level of ecological security.

[0076] Preferably, in step (4), based on land use data, the dominant functions of the three-life space are assigned scores, and the coupling coordination degree value of the three-life space is calculated using an improved coupling coordination degree model to analyze the spatiotemporal variation characteristics of the three-life coupling coordination; the coupling coordination degree of the three-life space is quantified based on the improved coupling coordination degree model, and the calculation steps are as follows:

[0077] Production, living, and ecological spaces are mutually reinforcing and mutually restrictive; the coupling coordination model can reflect the coordinated development of these three spaces from disorder to order.

[0078]

[0079] C represents the coupling degree, E p E l E e These are the dominant functional scores of production, living, and ecological spaces; T is the comprehensive evaluation index of the three spaces; α, β, and γ are the weights of the three scores. In previous calculations, these three were generally considered to be equally important; however, to more accurately assess the coordination relationship among them, systems with larger fluctuations should be assigned higher weights. Therefore, the standard deviation method is used; the larger the standard deviation of the index, the higher the internal dispersion of the system, and therefore it should be assigned a higher weight. Thus, the contribution coefficients of each system are:

[0080]

[0081] The D value represents the degree of coupling coordination. The higher the D value, the higher the level of development and coordination of the three-dimensional space in western Jilin, and vice versa.

[0082] Compared with the prior art, the beneficial effects of the present invention are:

[0083] This invention provides an ecological security early warning and evaluation system to understand the likelihood and spatiotemporal distribution characteristics of ecological and environmental problems, providing a scientific basis for local governments and environmental protection departments. Based on historical spatial data and various statistical yearbooks, this invention selects indicators and constructs an ecological security evaluation system according to the "three-life" spatial theory. It calculates the coupling and coordination of the three aspects (ecological, environmental, and ecological aspects) based on different land use functions, and finally determines the ecological security early warning area based on the spatiotemporal relationship between the two. Attached Figure Description

[0084] Figure 1 This is an application diagram of the ecological security assessment case based on the integration of the three-life space concept of the present invention and multi-model fusion;

[0085] Figure 2 This is a case study of the evaluation of the coupling coordination degree of the three-dimensional space based on the improved coupling coordination degree model of the present invention.

[0086] Figure 3 This is the ecological security early warning zoning map of the present invention;

[0087] Figure 4 This is a flowchart illustrating the relationship between the ecological security early warning and assessment technology based on the coupling of three-life space and the fusion of multi-source models of the present invention. Detailed Implementation

[0088] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0089] Please see Figure 1-4 This invention provides an ecological security early warning and assessment system based on the coupling of three-life space and the fusion of multi-source models, including an indicator framework construction module, a weight determination module, an ecological security level assessment module, a three-life space coupling assessment module, and an ecological security early warning assessment module.

[0090] The indicator framework construction module integrates multi-source data such as natural, meteorological, remote sensing and socio-economic data. Based on the "Production-Living-Ecology" PLES spatial concept and the "Stress-State-Response" PSR model, it constructs a comprehensive ecological security evaluation indicator framework of "PLES-PSR" from four dimensions: natural environment, socio-economic, resource utilization and ecological function.

[0091] The weight determination module uses Bayesian BWM and CRITIC methods to determine subjective and objective weights respectively, and improves the stability and scientific nature of the indicator weights through a combined weighting strategy based on distance function CWDF.

[0092] The ecological security level assessment module improves upon the traditional TOPSIS method and combines it with grey relational analysis (GRA) to calculate the regional ecological security level, thereby achieving a comprehensive assessment of ecological security and identification of its dynamic spatiotemporal distribution.

[0093] The three-life space coupling assessment module scores the dominant functions of the three-life space based on land use data, calculates the coupling coordination degree of the three-life space using an improved coupling coordination degree model, and identifies the coordination differences and evolution trends between regions.

[0094] The ecological security early warning and assessment module divides early warning zones based on the ecological security level and the results of the three-life coupling coordination, supporting decision-making on ecological protection and spatial control.

[0095] The indicator framework construction module specifically selects the following indicators:

[0096] Ecological space: stress indicators include average annual precipitation, average annual temperature, average annual wind speed, and fertilizer application rate; status indicators include wetland soil salinity index, wetland density index, landscape diversity index, landscape fragmentation index, and biodiversity index; response indicators include hydrological regulation index, wetland degradation index, desertification area ratio, and conversion rate of non-ecological land to ecological land.

[0097] Production space: Pressure indicators include per capita GDP, the proportion of primary and secondary industry output, the number of large livestock, and the total output value of agriculture, forestry, animal husbandry and fishery; Status indicators include land reclamation rate, per capita grain output, per capita construction land area, and net primary productivity; Response indicators include comprehensive utilization rate of general industrial solid waste, the proportion of tertiary industry output, and the scientific and cultural value of wetlands.

[0098] Living space: Stress indicators include natural population growth rate, population density, and night light index; status indicators include per capita daily domestic water consumption, per capita park green space area, and number of ordinary middle schools; response indicators include green coverage rate of built-up areas and proportion of education expenditure.

[0099] In the weight determination module, Bayesian BWM determines subjective weights: it identifies the best and worst indicators, constructs a comparison matrix, aggregates expert opinions through a probability model, and calculates the cumulative weight and the weight of each decision-maker.

[0100] CRITIC determines objective weights by normalizing the raw data, calculating the coefficient of variation and conflict, obtaining the information content of the indicators, and then determining the normalized weights.

[0101] Distance-based weighted combination: Construct a distance function to calculate the degree of difference between subjective and objective weights, and determine the combined weights through linear weighting.

[0102] The ecological security level assessment module establishes a standardized matrix of indicators to determine positive and negative ideal solutions; calculates the distance between the evaluation object and the positive and negative ideal solutions; determines the grey relational coefficient; calculates the relative proximity of the grey correlation coefficient; determines the ecological security level based on the relative proximity; and uses the natural breakpoint method for classification.

[0103] The three-life space coupling assessment module scores the dominant functions of the three-life spaces based on land use data, calculates the coupling coordination degree of the three-life spaces using an improved coupling coordination degree model, and identifies the coordination differences and evolution trends between regions; it scores the dominant functions of cultivated land, construction land, forest land, grassland, water area, unused land or other types of land use.

[0104] The coupling degree and the comprehensive evaluation index of the three-life space are calculated to obtain the coupling coordination degree, and the spatiotemporal variation characteristics of the three-life coupling coordination are analyzed.

[0105] The ecological security early warning assessment module divides ecological security into five levels: unsafe zone I, relatively unsafe zone II, moderately safe zone III, relatively safe zone IV, and safe zone V; and divides the coupling and coordination of the three-life space into five levels: severely unbalanced zone I, moderately unbalanced zone II, basically coordinated zone III, moderately coordinated zone IV, and well coordinated zone V. ArcGIS is used to overlay and analyze the two levels to establish early warning zones.

[0106] The ecological security early warning and assessment method based on the coupling of three-source space and the fusion of multi-source models includes the following steps:

[0107] (1) The indicator framework is constructed based on multi-source data of "nature-meteorology-remote sensing-socioeconomic data", integrating the spatial concept of "production-life-ecology" and the "stress-state-response" model PSR, selecting multi-dimensional indicators of natural environment, socio-economic, resource utilization and ecological function to form a scientific ecological security evaluation indicator framework.

[0108] (2) Weight determination: Bayesian optimal-worst method (BWM), CRITIC objective weighting method and CWDF combined weighting strategy are adopted to calculate the weights of multiple indicators and improve the stability and scientific nature of the indicator weights.

[0109] (3) Ecological security level assessment: The improved TOPSIS method and grey relational analysis model are introduced to calculate the regional ecological security level, so as to realize the comprehensive assessment of ecological security and the identification of dynamic spatiotemporal distribution.

[0110] (4) Coupling assessment of the three-life space: Based on land use data, the three-life space is quantified, and the coupling coordination degree of the three-life space is assessed using an improved coupling coordination degree model.

[0111] (5) Ecological security early warning assessment: Based on the assessment results of ecological security and the three-life coupling and coordination, the two are analyzed in a coordinated manner to identify potential areas of ecological function imbalance and areas with high incidence of spatial conflict, and ecological security early warning zoning is carried out.

[0112] In step (1), the "Stress-State-Response" model PSR is widely used to assess the causal relationship between "nature-society-economy"; where "stress" stems from the overexploitation of resources, "state" reflects changes in the structure and function of the ecosystem, and "response" refers to the regulation and restoration measures taken by the government and society; combined with the spatial functional characteristics of the "production-living-ecology" PLES in the region, PSR indicator systems corresponding to the three types of spaces are constructed respectively, and integrated to form the "PLES-PSR" comprehensive evaluation framework, which is used to systematically assess the level of ecological security;

[0113] The evaluation indicators were selected as follows:

[0114]

[0115]

[0116] In step (2), the subjective weights of the indicators are determined by Bayesian BWM and the objective weights of the indicators are determined by CRITIC. In order to combine the advantages of the two methods and minimize the difference between the weights and distribution coefficients derived from the subjective and objective methods, a combined weighting method based on distance function CWDF is adopted to comprehensively assign weights to the indicators.

[0117] The calculation method is as follows:

[0118] Determining Subjective Weights: Bayesian Bayesian Weighting (BWM) is a recently emerging method for determining subjective weights. It reduces the number of comparisons between indicators, thus simplifying the calculation process. Furthermore, it employs probabilistic methods to integrate the different weighting opinions of multiple evaluators, minimizing the single subjectivity and information loss in the decision-making process, resulting in more accurate evaluation results. The calculation formula is as follows:

[0119] Step 1: Determine the optimal indicators from the evaluation indicator system and worst indicators

[0120] Step 2: Construct comparison criteria; use numbers 1-9 to determine the preference for the best indicator relative to all other indicators, where 1 represents equal importance and 9 represents extreme importance. The larger the number, the greater the importance. This results in a comparison matrix:

[0121]

[0122] Step 3: Determine the degree of preference for all other indicators relative to the worst criterion, and obtain the comparison matrix:

[0123]

[0124] Step 4: Calculation of Cumulative Weights: The Bayesian BWM method given below, from a probabilistic perspective, shows that expert opinions cluster together; based on the following probabilistic model, the cumulative weights are obtained. and the weight w of each decision-maker k k = 1, ..., K;

[0125]

[0126] γ~gamma(0.1,0.1), w agg ~Dir(1), where Dir is a Dirichlet distribution and gamma(0.1,0.1) is a gamma distribution with a shape parameter of 0.1;

[0127] Determining objective weights: CRITIC is a weighting method based on objective data. It not only considers the amount of information carried by each indicator, but also the duplicate information caused by the correlation between indicators. Therefore, CRITIC can more comprehensively evaluate the importance and differences of indicators, and is more objective and accurate in weighting.

[0128] Step 1: Normalize the original data matrix. In the ecological security assessment based on the indicator method, the assessment indicators such as GDP and natural population growth rate have inconsistent dimensions and need to be standardized. The standardized calculation formulas for positive and negative indicators are as follows:

[0129]

[0130] Step 2: Calculate the coefficient of variation:

[0131]

[0132] In the formula: x j s is the mean of the j-th indicator; j Let j be the standard deviation of the j-th indicator;

[0133] Step 3: Calculate conflict rates; conflict rates are calculated using R. j This means that the stronger the correlation between the j-th evaluation index and other indicators, the more similar information it reflects, and the lower the conflict rate (R). j The smaller; R j The calculation formula is:

[0134]

[0135] r ij The correlation coefficient between indicator i and indicator j

[0136]

[0137] Step 4: The information content of the j-th indicator is

[0138]

[0139] Step 5: The normalized weight of the j-th indicator is

[0140]

[0141] Distance-based weighted combination: To simultaneously consider the advantages of both weighting methods, it is necessary to ensure that the differences between the weights and distribution coefficients of the subjective and objective methods are the same. Therefore, this study determines the degree of difference by calculating the distance between the two types of weights, and then determines the combined weights by linear weighting.

[0142] Construct the distance function relationship:

[0143]

[0144] Where X is the weight value calculated by the Bayesian BWM method, and Y is the weight value calculated by the CRITIC method.

[0145] W is the combined weight value, which is a linear weighted sum of the two methods; the expression for the combined weight value is:

[0146] W = aX + bY

[0147] a and b are two proportions with different weights, and the simplified expression is as follows:

[0148]

[0149] In step (3), the traditional TOPSIS method, which first weights the components and then calculates the Euclidean distance, amplifies the influence of the weights. Therefore, it is changed to calculate the Euclidean distance and then weight them simultaneously. At the same time, to overcome the shortcomings of TOPSIS in reflecting nonlinear utility changes, grey relational analysis is introduced. The combination of the two improves the stability and adaptability of the comprehensive evaluation results. Finally, ecological security is classified based on the natural breakpoint method. The calculation steps are as follows:

[0150] Step 1: Establish a standardized matrix of indicators;

[0151] Step 2: Determine the positive and negative ideal solutions

[0152]

[0153] Among them, the ideal solution r + and negative ideal solution r - It consists of the maximum and minimum values ​​of each indicator;

[0154] Step 3: Calculate the distance from the evaluation object to the positive and negative ideal solutions.

[0155]

[0156] and These are the distances from the j-th evaluation object to the positive and negative ideal solutions, respectively, w j Let be the weight of the j-th indicator;

[0157] Step 4: Determine the grey relational coefficient m + and m -

[0158]

[0159] and Let be the grey relation of the j-th evaluation object in the positive ideal solution and the negative ideal solution, respectively. The difference between the two minimum values. The difference between the two maximum values ​​is ξ, which is the discriminant coefficient, ξ∈[0,1]. It is used to improve the variability of the correlation coefficient and is set to 0.5.

[0160] Step 5: Calculate the relative closeness of the grey correlation coefficient.

[0161]

[0162] Where β = δ = 0.5; T ranges from 0 to 1, and the larger T is, the closer the ecological security is to the optimal level; in this study, T is used to represent ecological security, thereby determining the level of ecological security.

[0163] In step (4), based on land use data, the dominant functions of the three spaces are assigned scores, and the coupling coordination degree value of the three spaces is calculated using the improved coupling coordination degree model to analyze the spatiotemporal variation characteristics of the coupling coordination of the three spaces.

[0164] Based on land use data, the dominant functions of the three spaces (life, ecology, and environment) are scored, and the scoring criteria are shown in the table below:

[0165]

[0166]

[0167] The coupling coordination degree of the three-dimensional space is quantified based on the improved coupling coordination degree model. The calculation steps are as follows:

[0168] Production, living, and ecological spaces are mutually reinforcing and mutually restrictive; the coupling coordination model can reflect the coordinated development of these three spaces from disorder to order.

[0169]

[0170] C represents the coupling degree, E p E l E e These are the dominant functional scores of production, living, and ecological spaces; T is the comprehensive evaluation index of the three spaces; α, β, and β are the weights of the three scores. In previous calculations, these three were generally considered to be equally important; however, to more accurately assess the coordination relationship among them, systems with larger fluctuations should be assigned higher weights. Therefore, the standard deviation method is used; the larger the standard deviation of the index, the higher the internal dispersion of the system, and therefore it should be assigned a higher weight. Thus, the contribution coefficients of each system are:

[0171]

[0172] The D value represents the degree of coupling coordination. The higher the D value, the higher the level of development and coordination of the three-dimensional space in western Jilin, and vice versa.

[0173] Example:

[0174] Construction and Field Application of Ecological Security Assessment Model in Western Jilin Province

[0175] Step 1: Select multiple evaluation indicators from three perspectives—production, living conditions, and ecology—based on the "stress-state-response" model.

[0176] From an ecological space perspective, the following factors were selected at the stress level: average annual precipitation, average annual temperature, average annual wind speed, and fertilizer application rate; at the status level: wetland soil salinity index, wetland density index, landscape diversity index, landscape fragmentation index, and biodiversity index; at the response level: hydrological regulation index, wetland degradation index, desertification area ratio, and conversion rate of non-ecological land to ecological land. From a production space perspective, the following factors were selected at the stress level: per capita GDP, the proportion of primary and secondary industry output, the number of large livestock, and total output value of agriculture, forestry, animal husbandry, and fishery; at the status level: land reclamation rate, per capita grain output, per capita construction land area, and net primary productivity; at the response level: comprehensive utilization rate of general industrial solid waste, the proportion of tertiary industry output, and the scientific and cultural value of wetlands. From a living space perspective, the following factors were selected at the stress level: natural population growth rate, population density, and nighttime light intensity index; at the status level: per capita daily domestic water consumption, per capita park green space area, and the number of ordinary middle schools; at the response level: green coverage rate of built-up areas and the proportion of education expenditure.

[0177] Step 2: Standardize the indicators and calculate their weights using Bayesian BWM, CRITIC, and CWDF.

[0178] Step 3: Construct an ecological security evaluation model based on standardized data with determined weights and indicators. Calculate the ecological security evaluation value by combining the improved distance TOPSIS model with the GRA model. Analyze the spatial changes of ecological security using ArcGIS with the natural discontinuity method.

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[0180] Where β = δ = 0.5; T ranges from 0 to 1, with a larger T indicating that ecological security is closer to the optimal level. In this study, T is used to represent ecological security, thereby determining the level of ecological security.

[0181] Step 4: Assign scores to the dominant functions of the three-life space, calculate the coupling coordination degree value of the three-life space using the improved coupling coordination degree model, and classify the coupling coordination degree of the three-life space into levels using the natural breakpoint method.

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[0183] C represents the coupling degree, E p E l E e These represent the dominant functional scores of production, living, and ecological spaces; T is the comprehensive evaluation index of the three spaces; α, β, and γ are the weights of the three scores, which are generally considered equally important in previous calculations. However, to more accurately assess the coordination relationship among them, systems with larger fluctuations should be assigned higher weights. Therefore, the standard deviation method is used; the larger the standard deviation of the index, the higher the internal dispersion of the system, and therefore, it should be assigned a higher weight. Thus, the contribution coefficients of each system are:

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[0185] Step 5: Based on the calculation results of the coupling coordination degree of ecological security and the three-life space, the natural breakpoint method is used to classify the two into levels respectively. Ecological security is divided into five levels: unsafe zone (Ⅰ), relatively unsafe zone (Ⅱ), moderately safe zone (Ⅲ), relatively safe zone (Ⅳ), and safe zone (Ⅴ). The coupling coordination of the three-life space is divided into five levels: severely unbalanced zone (Ⅰ), moderately unbalanced zone (Ⅱ), basically coordinated zone (Ⅲ), moderately coordinated zone (Ⅳ), and well coordinated zone (Ⅴ). The ecological security early warning zone is divided according to the classification method in the table below.

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[0188] According to the assessment, from 2000 to 2020, the area of ​​the ecological security warning zone in western Jilin Province gradually decreased, and the ecological environment improved.

[0189] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An ecological security early warning and assessment system based on three-dimensional spatial coupling and multi-source model fusion, characterized in that: It includes an indicator framework construction module, a weight determination module, an ecological security level assessment module, a three-life space coupling assessment module, and an ecological security early warning assessment module; The indicator framework construction module integrates multi-source data such as natural, meteorological, remote sensing and socio-economic data. Based on the "Production-Living-Ecology" PLES spatial concept and the "Stress-State-Response" PSR model, it constructs a comprehensive ecological security evaluation indicator framework from four dimensions: natural environment, socio-economic, resource utilization and ecological function. The weight determination module uses Bayesian BWM and CRITIC methods to determine subjective and objective weights respectively, and improves the stability and scientific nature of the index weights through a combined weighting strategy based on distance function CWDF. The ecological security level assessment module improves the traditional TOPSIS method and combines it with grey relational analysis (GRA) to calculate the regional ecological security level, thereby realizing the comprehensive assessment of ecological security and identification of dynamic spatiotemporal distribution. The three-life space coupling assessment module scores the dominant functions of the three-life space based on land use data, calculates the coupling coordination degree of the three-life space using an improved coupling coordination degree model, and identifies the coordination differences and evolution trends between regions. The ecological security early warning and assessment module divides early warning zones based on the ecological security level and the coordination results of the three-life coupling, supporting ecological protection and spatial control decisions.

2. The ecological security early warning and assessment system based on the coupling of three-life spaces and fusion of multi-source models according to claim 1, characterized in that: The indicator framework construction module specifically selects the following indicators: Ecological space: stress indicators include average annual precipitation, average annual temperature, average annual wind speed, and fertilizer application rate; status indicators include wetland soil salinity index, wetland density index, landscape diversity index, landscape fragmentation index, and biodiversity index; response indicators include hydrological regulation index, wetland degradation index, desertification area ratio, and conversion rate of non-ecological land to ecological land. Production space: Pressure indicators include per capita GDP, the proportion of primary and secondary industry output, the number of large livestock, and the total output value of agriculture, forestry, animal husbandry and fishery; Status indicators include land reclamation rate, per capita grain output, per capita construction land area, and net primary productivity; Response indicators include comprehensive utilization rate of general industrial solid waste, the proportion of tertiary industry output, and the scientific and cultural value of wetlands. Living space: Stress indicators include natural population growth rate, population density, and night light index; status indicators include per capita daily domestic water consumption, per capita park green space area, and number of ordinary middle schools; response indicators include green coverage rate of built-up areas and proportion of education expenditure.

3. The ecological security early warning and assessment system based on the coupling of three-life space and fusion of multi-source models according to claim 1, characterized in that: In the weight determination module, Bayesian BWM determines subjective weights: it determines the best and worst indicators, constructs a comparison matrix, aggregates expert opinions through a probability model, and calculates the cumulative weight and the weight of each decision-maker. CRITIC determines objective weights by normalizing the raw data, calculating the coefficient of variation and conflict, obtaining the information content of the indicators, and then determining the normalized weights. Distance-based weighted combination: Construct a distance function to calculate the degree of difference between subjective and objective weights, and determine the combined weights through linear weighting.

4. The ecological security early warning and assessment system based on the coupling of three-life space and fusion of multi-source models according to claim 1, characterized in that: The ecological security level assessment module establishes an index standardization matrix and determines positive and negative ideal solutions. Calculate the distance from the evaluation object to the positive and negative ideal solutions; determine the grey relational coefficient, calculate the relative proximity of the grey correlation coefficient, determine the ecological security level based on the relative proximity, and classify the levels using the natural breakpoint method.

5. The ecological security early warning and assessment system based on the coupling of three-life space and fusion of multi-source models according to claim 1, characterized in that: The three-life space coupling assessment module scores the dominant functions of the three-life space based on land use data, calculates the coupling coordination degree of the three-life space using an improved coupling coordination degree model, and identifies the coordination differences and evolution trends between regions. Scoring the dominant functions of cultivated land, construction land, forest land, grassland, water area, unused land, or other types of land use; The coupling degree and the comprehensive evaluation index of the three-life space are calculated to obtain the coupling coordination degree, and the spatiotemporal variation characteristics of the three-life coupling coordination are analyzed.

6. The ecological security early warning and assessment system based on the coupling of three-life space and fusion of multi-source models according to claim 1, characterized in that: The ecological security early warning and assessment module divides ecological security into five levels: unsafe zone I, relatively unsafe zone II, moderately safe zone III, relatively safe zone IV, and safe zone V; and divides the coupling and coordination of the three-life space into five levels: severely unbalanced zone I, moderately unbalanced zone II, basically coordinated zone III, moderately coordinated zone IV, and well coordinated zone V. ArcGIS is used to overlay and analyze the two levels to establish early warning zones.

7. An ecological security early warning and assessment method based on the coupling of three-life space and the fusion of multi-source models, characterized by: Includes the following steps: (1) The indicator framework is constructed based on multi-source data of "nature-meteorology-remote sensing-socioeconomic data", integrates the spatial concept of "production-life-ecology" and the "stress-state-response" model PSR, selects multi-dimensional indicators of natural environment, socio-economic, resource utilization and ecological function, and forms a scientific ecological security evaluation indicator framework. (2) Weight determination: Bayesian optimal-worst method (BWM), CRITIC objective weighting method and CWDF combined weighting strategy are adopted to calculate the weights of multiple indicators and improve the stability and scientific nature of the indicator weights. (3) Ecological security level assessment: The improved TOPSIS method and grey relational analysis model are introduced to calculate the regional ecological security level, so as to realize the comprehensive assessment of ecological security and the identification of dynamic spatiotemporal distribution. (4) Coupling assessment of the three-life space: Based on land use data, the three-life space is quantified, and the coupling coordination degree of the three-life space is assessed using an improved coupling coordination degree model. (5) Ecological security early warning assessment: Based on the assessment results of ecological security and the three-life coupling and coordination, the two are analyzed in a coordinated manner to identify potential areas of ecological function imbalance and areas with high incidence of spatial conflict, and ecological security early warning zoning is carried out.

8. The ecological security early warning and assessment method based on the coupling of three-life space and fusion of multi-source models according to claim 7, characterized in that: In step (1), the "Stress-State-Response" model PSR is widely used to assess the causal relationship between "nature-society-economy"; where "stress" originates from the overexploitation of resources, "state" reflects changes in the structure and function of the ecosystem, and "response" refers to the regulation and restoration measures taken by the government and society; combined with the spatial functional characteristics of the "production-life-ecology" PLES in the region, PSR indicator systems corresponding to the three types of spaces are constructed respectively, and integrated to form the "PLES-PSR" comprehensive evaluation framework, which is used to systematically assess the level of ecological security; in step (2), the subjective weights of the indicators are determined by Bayesian BWM, and the objective weights of the indicators are determined by CRITIC. In order to combine the advantages of the two methods and minimize the difference between the weights and distribution coefficients obtained from the subjective and objective methods, a combined weighting method based on the distance function CWDF is used to comprehensively assign weights to the indicators; The calculation method is as follows: Determining Subjective Weights: Bayesian Bayesian Weighting (BWM) is a recently emerging method for determining subjective weights. It reduces the number of comparisons between indicators, thus simplifying the calculation process. Furthermore, it employs probabilistic methods to integrate the different weighting opinions of multiple evaluators, minimizing the single subjectivity and information loss in the decision-making process, resulting in more accurate evaluation results. The calculation formula is as follows: Step 1: Determine the optimal indicators from the evaluation indicator system and worst indicators Step 2: Construct comparison criteria; use numbers 1-9 to determine the preference for the best indicator relative to all other indicators, where 1 represents equal importance and 9 represents extreme importance. The larger the number, the greater the importance. This results in a comparison matrix: Step 3: Determine the degree of preference for all other indicators relative to the worst criterion, and obtain the comparison matrix: Step 4: Calculation of Cumulative Weights: The Bayesian BWM method given below, from a probabilistic perspective, shows that expert opinions cluster together; based on the following probabilistic model, the cumulative weights are obtained. and the weight w of each decision-maker k k = 1, ..., K; γ~gamma(0.1,0.1), w agg ~Dir(1), where Dir is a Dirichlet distribution and gamma(0.1,0.1) is a gamma distribution with a shape parameter of 0.1; Determining objective weights: CRITIC is a weight allocation method based on objective data. It not only considers the amount of information carried by each indicator, but also the duplicate information caused by the correlation between indicators. Therefore, CRITIC is able to more comprehensively assess the importance and differences of indicators, and is more objective and accurate in weight allocation; Step 1: Normalize the original data matrix. In the ecological security assessment based on the indicator method, the assessment indicators such as GDP and natural population growth rate have inconsistent dimensions and need to be standardized. The standardized calculation formulas for positive and negative indicators are as follows: Step 2: Calculate the coefficient of variation: In the formula: x j s is the mean of the j-th indicator; j Let j be the standard deviation of the j-th indicator; Step 3: Calculate conflict rates; conflict rates are calculated using R. j This means that the stronger the correlation between the j-th evaluation index and other indicators, the more similar information it reflects, and the lower the conflict rate (R). j The smaller; R j The calculation formula is: r ij The correlation coefficient between indicator i and indicator j Step 4: The information content of the j-th indicator is Step 5: The normalized weight of the j-th indicator is Distance-based weighted combination: To simultaneously consider the advantages of both weighting methods, it is necessary to ensure that the differences between the weights and distribution coefficients of the subjective and objective methods are the same. Therefore, this study determines the degree of difference by calculating the distance between the two types of weights, and then determines the combined weights by linear weighting. Construct the distance function relationship: Where X is the weight value calculated by the Bayesian BWM method, and Y is the weight value calculated by the CRITIC method. W is the combined weight value, which is a linear weighted sum of the two methods; the expression for the combined weight value is: W = aX + bY a and b are two proportions with different weights, and the simplified expression is as follows:

9. The ecological security early warning and assessment method based on the coupling of three-life space and fusion of multi-source models according to claim 7, characterized in that: In step (3), the traditional TOPSIS method, which first weights the components and then calculates the Euclidean distance, amplifies the influence of the weights. Therefore, it is changed to calculate the Euclidean distance and then weight them simultaneously. At the same time, to overcome the shortcomings of TOPSIS in reflecting nonlinear utility changes, grey relational analysis is introduced. The combination of the two improves the stability and adaptability of the comprehensive evaluation results. Finally, ecological security is classified based on the natural discontinuity method. The calculation steps are as follows: Step 1: Establish a standardized matrix of indicators; Step 2: Determine the positive and negative ideal solutions Among them, the ideal solution r + and negative ideal solution r - It consists of the maximum and minimum values ​​of each indicator; Step 3: Calculate the distance from the evaluation object to the positive and negative ideal solutions. and These are the distances from the j-th evaluation object to the positive and negative ideal solutions, respectively, w j Let be the weight of the j-th indicator; Step 4: Determine the grey relational coefficient m + and m - and Let be the grey relation of the j-th evaluation object in the positive ideal solution and the negative ideal solution, respectively. The difference between the two minimum values. The difference between the two maximum values ​​is ξ, which is the discriminant coefficient, ξ∈[0,1]. It is used to improve the variability of the correlation coefficient and is set to 0.

5. Step 5: Calculate the relative closeness of the grey correlation coefficient. Where β = δ = 0.5; T ranges from 0 to 1, and the larger T is, the closer the ecological security is to the optimal level; in this study, T is used to represent ecological security, thereby determining the level of ecological security.

10. The ecological security early warning and assessment method based on the coupling of three-life space and fusion of multi-source models according to claim 7, characterized in that: In step (4), based on land use data, the dominant functions of the three spaces are assigned scores, and the coupling coordination degree value of the three spaces is calculated using the improved coupling coordination degree model to analyze the spatiotemporal variation characteristics of the coupling coordination of the three spaces. The coupling coordination degree of the three-dimensional space is quantified based on the improved coupling coordination degree model. The calculation steps are as follows: Production, living, and ecological spaces are mutually reinforcing and mutually restrictive; the coupling coordination model can reflect the coordinated development of these three spaces from disorder to order. C represents the coupling degree, E p E l E e These are the dominant functional scores of production, living, and ecological spaces; T is the comprehensive evaluation index of the three spaces; α, β, and γ are the weights of the three scores. In previous calculations, these three were generally considered to be equally important; however, to more accurately assess the coordination relationship among them, systems with larger fluctuations should be assigned higher weights. Therefore, the standard deviation method is used; the larger the standard deviation of the index, the higher the internal dispersion of the system, and therefore it should be assigned a higher weight. Thus, the contribution coefficients of each system are: The D value represents the degree of coupling coordination. The higher the D value, the higher the level of development and coordination of the three-dimensional space in western Jilin, and vice versa.

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