Method for measuring and calculating collaborative safety index of water-grain-ecological compound system in arid region

Abstract

The invention provides a method for measuring and calculating a collaborative safety index of a water-grain-ecological composite system in an arid region, and relates to the technical field of collaborative management of green agricultural resources in the arid region. The method aims at solving the technical problem that in the prior art, a system for evaluating the water-grain-ecology multi-element co-evolution relation with an arid region as a research object is lacked. The method comprises the following steps: constructing a state parameter index system of water resource, grain and ecological safety in the arid region, and measuring and calculating through an improved G1-CRITIC subjective and objective weighting method to obtain a subsystem safety index reflecting a state parameter of a composite system; and inputting the subsystem safety index into a pre-established Hakkine model, analyzing the co-evolution process of the water-grain-ecological composite system, and measuring and calculating to obtain a water-grain-ecological collaborative safety index reflecting the collaborative safety state of the water-grain-ecological composite system. According to the method, from the perspective of the compound system, the dynamic relationship among water-grain-ecology is revealed under the background of agricultural green development in the arid region, and the technical blank in the aspect of research on the evolution mechanism of the compound system in the arid region is effectively filled. According to the method, the water competition of the arid region can be relieved, the soil salinization and underground water mineralization process can be slowed down, a high-level cooperation mechanism of mutual promotion and dynamic adjustment among water, grain and ecology is formed, so that the green and sustainable development of agriculture in the arid region is promoted, and the method has important theoretical value and practical application prospects.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of collaborative management of green agricultural resources in arid areas, and in particular to a method for calculating the collaborative safety index of a water-grain-ecological complex system in arid areas. Background Art

[0002] In recent years, rapid global economic development and sustained population growth have driven a significant increase in demand for natural resources and ecological services. This has made maintaining ecological balance and meeting the needs of socioeconomic development within limited resource constraints a major global challenge. Water shortages are particularly prominent, particularly under the influence of climate change, which has exacerbated droughts in many regions and put regional water stress near or beyond their ecological carrying capacity. Agriculture, as the world's largest water-consuming sector, plays a key role in ensuring food security. However, food production in arid regions primarily relies on irrigation. The high reliance of irrigated agriculture on freshwater resources has exacerbated the conflict between food production and other water-using sectors, further contributing to ecological and environmental problems such as soil salinization and groundwater mineralization.

[0003] To address the water resource pressures and ecological degradation associated with food production in arid regions, in-depth research is needed to address the challenges of synergistic development among regional water resource utilization, food production, and ecological protection. This will promote the coordinated management of water resources, food, and ecosystems and enhance the coordinated security of the water-food-ecological complex in arid regions. Current research on the water-food-ecological complex in arid regions has not fully considered the diversity and complexity of regional ecological environments. Furthermore, there is a lack of technical methods for analyzing the coevolutionary relationships of this complex in the context of sustainable development and for calculating its security index. Therefore, in-depth analysis of the coevolutionary mechanisms of the water-food-ecological complex in arid regions and the measurement of its synergistic security index are of great significance for alleviating water, food, and ecological conflicts and improving the coordinated security of the water-food-ecological complex in arid regions. Summary of the Invention

[0004] An embodiment of the present invention provides a method for calculating the collaborative safety index of the water-food-ecological complex system in arid areas to at least partially solve the above problems.

[0005] A first aspect of an embodiment of the present invention provides a method for calculating a coordinated safety index of a water-food-ecological complex system in an arid area, the method comprising: Step 1: Determine the target scope of the assessment based on the assessment needs and assessment objectives; Step 2: Obtain land use data, climate and environmental data, DEM data, and socioeconomic data within the target area to construct a parameter indicator system for the coordinated security status of water-food-ecosystems in arid areas; Step 3: Input the parameter indicator system of the coordinated security status of water, food and ecosystem in arid areas into the pre-built improved G1-CRITIC combined weighting model, and obtain the water resources security index, food security index and ecological security index based on the indicator weights to reflect the parameters of the water, food and ecosystem security status; Step 4: Input two of each of the water security index, food security index, and ecological security index into the pre-built Haken model. 、 、 、 The four parameter values ​​are tested to see whether they satisfy the adiabatic approximation assumption of the Haken model, and the order parameters that dominate the co-evolution of the water-food-ecosystem are identified; Step five: Based on the inverse integral of the order parameter evolution equation, the potential function is obtained. The potential function is solved to obtain the stable point of the co-evolution of the composite system. The distance between any point on the potential function and the stable point is measured by the distance function to reflect the state value of the point. The state value of each point is processed by the extreme value method to obtain the evolutionary co-evolutionary security index of the water-food-ecological composite system in arid areas.

[0006] Optionally, in step 2, the water resources security status parameter indicator system includes: water resources endowment, water resources development level and water resources utilization efficiency, which presents the supply, development and utilization of water resources; water resources endowment includes per capita water resources and annual average precipitation; water resources development level includes water resources development and utilization rate, surface water resources development and utilization rate, and groundwater resources development and utilization rate; water resources utilization efficiency includes water consumption per 10,000 yuan of GDP, water consumption per 10,000 yuan of industrial added value, per mu irrigation water consumption, and per capita domestic water consumption; The food security status parameter indicator system includes: food production, consumption and stability, reflecting the basic characteristics and changing trends of food security. Food production security includes the proportion of grain planting area, per capita arable land area, mechanical power per unit area, and grain output per unit area; food consumption security includes per capita grain output, per capita disposable income, and natural population growth rate; food stability includes the intensity of fertilizer application, pesticide application intensity, agricultural film application intensity, and grain output fluctuation rate; The parameter indicator system for ecological security status includes: ecological risk, ecological health and ecological services. Ecological risks include drought risk, human activity risk, salinization risk and landscape pattern risk; ecological health includes ecological vitality, ecological organization and ecological resilience; ecological services include water production services, food supply services and habitat quality.

[0007] Optionally, obtain land use data, climate and environmental data, DEM data, and socioeconomic data within the target area to construct a parameter indicator system for the coordinated security status of water-food-ecosystems in arid areas, including: Based on the landscape characteristics within the target area, the target area is divided into six landscape types: cultivated land, forest land, grassland, water area, construction land and unused land, according to the method of distinguishing differences and summarizing similarities. Drought risk is measured based on the Standardized Precipitation Evapotranspiration Index (SEPTI) on a 12-month scale; Measuring human activity risk based on the human activity intensity index; A comprehensive salinity index was synthesized based on the mean of the three salinity indices to measure salinization risk; Measuring landscape pattern risk based on landscape disturbance index and landscape vulnerability index; a measure of ecological vitality based on net primary productivity; Relevant indicators based on landscape heterogeneity and landscape connectivity are used to characterize ecological organizational capacity; Based on measuring ecological resilience through resistance and resilience; Calculate water yield services based on the difference between regional precipitation and evapotranspiration, using the water yield module in the InVEST model; use The grain production in the study area was rasterized and assigned a value to obtain the grain supply service; The HQ module in the InVEST model was used to assess habitat quality.

[0008] Optionally, in step three, the subjective weight is determined based on the improved G1 method, the objective weight is determined based on the CRITIC method, and the subjective and objective weights are combined using a combined weighting method based on a Nash equilibrium point; Among them, the improved G1 method is a subjective weight determination method that uses experts to rank the importance of indicators and use the coefficient of variation of indicators to determine the importance ratio of adjacent indicators. Its calculation steps include: set up It is ranked in order of importance The evaluation indicators are ranked according to the professional knowledge and judgment of the experts to obtain a unique importance ranking: , is the number of indicators.

[0009] Calculate the indicator weights based on the coefficient of variation method: ; ; ; Where, For the ( ) of the objects ( ) indicator values; 、 、 Respectively represent The standard deviation, mean, and weight of each indicator based on the coefficient of variation method; The indicator weights obtained by the coefficient of variation method Determine adjacent indicators 、 The importance ratio between : ; Calculate the indicator with the lowest importance ranking Weight : ; Get the last indicator weight On the basis of the importance of the indicators, the weight of the previous indicator is calculated in turn by the weight of the latter indicator, and finally the weight of the previous indicator is calculated. Weight : ; The subjective weight obtained by the improved G1 method is recorded as ; The CRITIC method reflects the contribution of each indicator to the overall indicator by quantifying the differences and conflicts between indicators. The calculation steps include: Select evaluation object evaluation indicators, establish The indicator data matrix, Represents the jth indicator of the i-th evaluation object and standardizes the matrix; Calculate the indicator variability using standard deviation measure; Calculate the conflict of indicators, indicators The conflict size with other indicators is : ; in, Indicates evaluation indicators and indicators The correlation coefficient between Calculation indicators Amount of information : ; Calculation indicators Weight : ; The objective weight obtained by the CRITIC method is denoted as ; The combined weighting method based on Nash equilibrium point uses the principles of game theory to determine the comprehensive weight through linear combination coefficients. The calculation steps are as follows: Use 、 Indicates the subjective and objective weights of evaluation indicators and The importance of the combination weight is calculated using a linear equation , the formula is as follows: ; Finding linear coefficients 、 The Nash equilibrium point makes the subjective and objective weights and and combination weight The deviation is the smallest, and the objective function is: ; Solve for the optimal combination coefficients. According to the properties of the matrix differential equation, we can obtain the conditions for the optimal first-order derivative: ; According to the above formula, the optimal combination coefficient is calculated 、 , and normalize it to get the optimal linear combination: .

[0010] Optionally: In step 4, the Haken model identifies the fast and slow variables in the system and uses adiabatic elimination to eliminate the fast variables, thereby determining the slow variable that dominates the system evolution, i.e., the order parameter; The water-grain-ecological complex system consists of three subsystems: water resources, grain, and ecology. Based on the water-grain-ecological synergistic security status parameter indicator system, the improved G1-CRITIC combined weighting model is used to calculate the three subsystem security indices, namely the subsystem status parameters. The Haken model assumes that during the evolution of two interrelated subsystems, if one subsystem controls the other, the state parameter of the controlling subsystem is expressed as The state parameters of the controlled subsystem are expressed as The evolution equation of the system can be expressed as: ; ; Discretize the model to improve the adaptability of the Haken model to discrete socioeconomic data: ; ; in, 、 represent the damping coefficients of the two subsystems respectively; 、 They represent the interaction strength between the two subsystems; Indicates time; when hour, and , the system reaches a stable solution, satisfying the "adiabatic approximation assumption"; at this time, the subsystem Relative to subsystem is a fast variable that decays rapidly. The slow variables that dominate the evolution of the system, namely the order parameters; The Haken model is used to conduct pairwise analysis on the state parameters of the three subsystems of water resources, food, and ecology, and the system order parameters are identified by the adiabatic elimination method; when there are multiple order parameters in the water-food-ecosystem, the summed order parameters are assumed to be slow variables, and an evolution equation is constructed to test whether the adiabatic approximation assumption is met.

[0011] Optionally, in step 5, the collaborative safety index is calculated by integrating the inverse of the evolution equation of the composite system to obtain a potential function, solving the stable point of the potential function, and then using a distance function to measure the distance between any point on the potential function and the stable point to reflect the state of the point, and performing extreme value processing on the state value of each point to obtain the collaborative safety index; Among them, the order parameter is dominant in the entire system, and the evolution equation of the order parameter is also the evolution equation of the system: ; Integrate the inverse of the evolution equation of the composite system to obtain the potential function: ; when Determine the stable point of the potential function when , the equation has a unique solution, namely , then the stable state of any deviation from the stable point is determined by the straight-line distance between the point and the stable point; if , then the equation has three solutions: , , ,at this time It is not a stable point. Any point in the system will eventually return to and At these two stable points; since the water-food-ecosystem state parameters water resources, food, and ecological security index are all greater than 0, only the potential function graph is considered. Then, the stable point of the water-food-ecosystem co-evolution is [ , ], the distance between any point and the stable point for: ; The larger the value, the farther the state point of the system is from the stable point, and the lower the synergy index value; conversely, the higher the synergy index; In order to more comprehensively and intuitively reflect the synergistic evolution trend of the composite system, the extreme value method is used to The value is normalized; at the same time, Before normalizing the values, The maximum and minimum values ​​of the value are increased and decreased by 10% respectively to avoid the boundary problem of 0 and 1, and finally the evolutionary synergistic security index of the water-food-ecological complex system in arid areas is obtained: ; in, It represents the synergistic security index of the evolution of the water-food-ecological complex system in arid areas; Respectively The maximum and minimum values.

[0012] A second aspect of an embodiment of the present invention provides a device for calculating a coordinated safety index of a water-food-ecological complex system in an arid area, the device comprising: A target scope determination module is used to determine the target scope of the assessment based on the assessment needs and assessment objectives; The data acquisition module is used to obtain land use data, climate and environmental data, DEM data, and socioeconomic data within the target range, and to build a parameter indicator system for the coordinated security status of water-food-ecosystems in arid areas; A state parameter determination module is used to input the water-food-ecosystem coordinated security state parameter indicator system in arid areas into a pre-built improved G1-CRITIC combined weighting model, and obtain a water resource security index, a food security index, and an ecological security index based on the indicator weights to reflect the water-food-ecosystem security state parameters; The order parameter determination module is used to input two of the water security index, food security index and ecological security index into the pre-built Haken model, and the output of the Haken model is used to determine the order parameter. 、 、 、 The four parameter values ​​are tested to see whether they satisfy the adiabatic approximation assumption of the Haken model, and the order parameters that dominate the co-evolution of the water-food-ecosystem are identified; The safety index determination module is used to obtain the potential function based on the inverse integral of the order parameter evolution equation, solve the potential function to obtain the stable point of the co-evolution of the composite system, and use the distance function to measure the distance between any point on the potential function and the stable point to reflect the state value of the point. The state value of each point is processed by the extreme value method to obtain the evolutionary co-evolutionary safety index of the water-food-ecological composite system in arid areas.

[0013] A third aspect of an embodiment of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and runnable on the processor. When executed, the processor implements the method for calculating the collaborative safety index of the water-food-ecological complex system in arid areas as described in the first aspect of the present invention.

[0014] A fourth aspect of an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the method for calculating the collaborative safety index of the water-food-ecological complex system in arid areas as described in the first aspect of the present invention is implemented.

[0015] The fifth aspect of an embodiment of the present invention provides a computer program product, including a computer program / instruction, which is used by a processor to implement the steps in the method for calculating the collaborative security index of the water-food-ecological complex system in arid areas as described in the first aspect of the present invention.

[0016] The present invention aims to address the existing technical problem of a lack of a systematic assessment of the co-evolutionary relationship between water, food, and ecology, focusing on arid regions. The method comprises: constructing a state parameter indicator system for water resources, food, and ecological security in arid regions; and calculating a subsystem security index reflecting the state parameters of the complex system using an improved G1-CRITIC subjective and objective weighting method. The subsystem security index is then input into a pre-established Haken model to analyze the co-evolutionary process of the water-food-ecology complex system and calculate a water-food-ecology synergistic security index reflecting the synergistic security status of the water-food-ecology complex system. This method, from a complex system perspective, reveals the dynamic relationship between water, food, and ecology in the context of green agricultural development in arid regions, effectively filling a technical gap in the study of the evolutionary mechanisms of complex systems in arid regions. The present invention helps alleviate water competition in arid regions, slow soil salinization and groundwater mineralization, and establish a high-level synergistic mechanism for the mutual promotion and dynamic regulation of water, food, and ecology, thereby promoting the green and sustainable development of agriculture in arid regions. The method has significant theoretical value and practical application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 This is a flowchart of the steps of the method for calculating the collaborative safety index of the water-food-ecological complex system in arid areas provided by an embodiment of the present invention; Figure 2 This is a principle flow chart of the method for calculating the collaborative safety index of the water-food-ecological complex system in arid areas provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] The embodiment of the present invention provides a method for calculating the collaborative safety index of the water-food-ecological complex system in arid areas. Specifically, Figure 1 As shown, it shows a flowchart of the steps of the method for calculating the synergistic safety index of the water-food-ecological complex system in arid areas provided by an embodiment of the present invention, and the method includes the following steps: Step 1: Determine the target scope of the assessment based on the assessment needs and assessment objectives; Step 2: Obtain land use data, climate and environmental data, DEM data, and socioeconomic data within the target area to construct a parameter indicator system for the coordinated security status of water-food-ecosystems in arid areas; Step 3: Input the parameter indicator system of the coordinated security status of water, food and ecosystem in arid areas into the pre-built improved G1-CRITIC combined weighting model, and obtain the water resources security index, food security index and ecological security index based on the indicator weights to reflect the parameters of the water, food and ecosystem security status; Step 4: Input two of each of the water security index, food security index, and ecological security index into the pre-built Haken model. 、 、 、 The four parameter values ​​are tested to see whether they satisfy the adiabatic approximation assumption of the Haken model, and the order parameters that dominate the co-evolution of the water-food-ecosystem are identified; Step five: Based on the inverse integral of the order parameter evolution equation, the potential function is obtained. The potential function is solved to obtain the stable point of the co-evolution of the composite system. The distance between any point on the potential function and the stable point is measured by the distance function to reflect the state value of the point. The state value of each point is processed by the extreme value method to obtain the evolutionary co-evolutionary security index of the water-food-ecological composite system in arid areas.

[0021] In the embodiment of the present invention, in step 1, the target range may be divided according to the river basin range or the administrative area range.

[0022] Specifically, the target range is arid or semi-arid areas. Based on DEM (digital elevation model) data, the basin range is delineated using Arc GIS, or the range is determined by administrative boundaries at the provincial, municipal or county levels.

[0023] In the embodiment of the present invention, in step 2, the water resources security status parameter indicator system includes: water resources endowment, water resources development level and water resources utilization efficiency, which presents the supply, development and utilization of water resources; water resources endowment includes per capita water resources and annual average precipitation; water resources development level includes water resources development and utilization rate, surface water resources development and utilization rate, and groundwater resources development and utilization rate; water resources utilization efficiency includes water consumption per 10,000 yuan of GDP, water consumption per 10,000 yuan of industrial added value, per mu irrigation water consumption, and per capita domestic water consumption; The food security status parameter indicator system includes: food production, consumption and stability, reflecting the basic characteristics and changing trends of food security. Food production security includes the proportion of grain planting area, per capita arable land area, mechanical power per unit area, and grain output per unit area; food consumption security includes per capita grain output, per capita disposable income, and natural population growth rate; food stability includes the intensity of fertilizer application, pesticide application intensity, agricultural film application intensity, and grain output fluctuation rate; The parameter indicator system for ecological security status includes: ecological risk, ecological health and ecological services. Ecological risks include drought risk, human activity risk, salinization risk and landscape pattern risk; ecological health includes ecological vitality, ecological organization and ecological resilience; ecological services include water production services, food supply services and habitat quality.

[0024] In an embodiment of the present invention, a state parameter indicator system can be constructed based on step two. Specifically, the construction of the state parameter indicator system takes the security of the water-food-ecological subsystem as the target layer.

[0025] The water resources security criteria layer is gradually expanded according to "resource endowment-development level-utilization efficiency", comprehensively presenting the supply, development and utilization of water resources.

[0026] The food security criteria layer includes three aspects: food production, consumption and stability, reflecting the basic characteristics and changing trends of food security.

[0027] The ecological security criteria layer is based on the "risk avoidance-health maintenance-service gain" framework, including three aspects: ecological risk, ecological health and ecological services.

[0028] Figure 2 The principle flow chart of the method for calculating the collaborative safety index of the water-food-ecological complex system in arid areas provided by the embodiment of the present invention is shown as follows: Figure 2 As shown, in the embodiment of the present invention, the invention aims to obtain a coordinated security index for the evolution of the water-grain-ecological complex system in arid areas. This security index needs to be based on water resource security state parameters, grain security state parameters, and ecological security state parameters. Furthermore, the water resource security state parameters, grain security state parameters, and ecological security state parameters need to be determined by a water resource security state parameter index system, a grain security state parameter index system, and an ecological security state parameter index system.

[0029] In the embodiment of the present invention, land use data, climate and environmental data, DEM data, and socioeconomic data within the target range are obtained to construct a parameter indicator system for the coordinated security status of water-food-ecosystems in arid areas, including: S1, based on the landscape characteristics within the target area, the target area is divided into six landscape types: cultivated land, forest land, grassland, water area, construction land and unused land according to the method of distinguishing differences and summarizing similarities; S2, Standardized precipitation evapotranspiration index based on the 12-month scale ( Standardized Precipitation Evapotranspiration Index, SPEI ) Measuring drought risk; The calculation steps of the standardized precipitation evapotranspiration index are as follows: Calculate the difference between monthly precipitation and potential evapotranspiration : (1) In formula (1), 、 Respectively monthly precipitation and potential evapotranspiration; Through the three-parameter Log-Logistic probability distribution Fitting is performed, and the cumulative function is as follows: (2) In formula (2), 、 、 are scale parameter, shape parameter and starting parameter respectively, which are obtained by fitting through the linear moment method; Normalize the cumulative probability density: (3) (4) In formula (4), SPEI represents the standardized precipitation evapotranspiration index, W is the probability weighted matrix, if P≤0.5, then , ;like ,but , ; is a fixed parameter used to convert the cumulative probability into a standard normal quantile, 、 、 、 、 、 .

[0030] S3, based on the human activity intensity index ( Human Activity Intensity, HAI ) Measuring risks from human activities; The calculation steps are as follows: (5) (6) In formula (5) and formula (6), Indicates the intensity of human activities; is the equivalent area of ​​construction land; S is the total area of ​​the region; 、 Respectively represent The area and construction land equivalent conversion coefficients of various land use types are as follows: the construction land equivalent conversion coefficients of cultivated land, forest land, grassland, water area, construction land and unused land are 0.2, 0, 0.067, 0, 1 and 0 respectively.

[0031] S4, a comprehensive salinity index based on the mean of the three salinity indices to measure salinization risk; The calculation steps are as follows: (7) (8) (9) (10) In formulas (7)-(10), represents the comprehensive salt index; 、 、 Represents three salt indices respectively; They represent the red band reflectance, near-infrared band reflectance, and green band reflectance of remote sensing data respectively.

[0032] S5, measuring landscape pattern risk based on landscape disturbance index and landscape vulnerability index; The calculation steps are as follows: (11) In formula (11), ERI represents the landscape pattern risk index; 、 represent the area and total area of ​​the i-th landscape type in the k-th evaluation unit, respectively; N is the number of landscape types in the evaluation unit; represents the landscape disturbance index, represents the landscape vulnerability index; (12) (13) (14) (15) In formula (12)-(15), 、 、 represent landscape fragmentation, separation, and fractal dimension index respectively; is the number of patches of landscape type i; is the total area of ​​landscape type i; A is the total area; is the proportion of quadrats where patch i appears; 、 are the number ratio and area ratio of patch i respectively; for , unused land, water area, cultivated land, grassland, forest land and construction land were assigned values ​​of 6, 5, 4, 3, 2 and 1 respectively, and then all vulnerability values ​​were normalized to eliminate the dimensional differences between different landscape types.

[0033] S6, a measure of ecological vitality based on net primary productivity; The calculation steps are as follows: (16) In formula (16), Represents a grid cell exist Total amount of organic matter accumulated during the month; Represents a grid cell exist photosynthetically active radiation absorbed during the month; Refers to the grid cell exist The actual light energy utilization rate of the month.

[0034] (17) In formula (17), is a grid cell exist Total solar radiation for the month; is a grid cell exist The proportion of photosynthetically active radiation absorbed by vegetation in a month.

[0035] (18) In formula (18), Respectively stated that Low temperature and high temperature stress factors that have an impact; represents water stress factor; It refers to the maximum light energy utilization rate of vegetation under ideal conditions.

[0036] S7, characterizing ecological organization based on indicators related to landscape heterogeneity and landscape connectivity; The calculation steps are as follows: (19) In formula (19), represents the area-weighted average patch fractal dimension; Indicates rich and diverse flavors. 、 represent the overall landscape connectivity and landscape sprawl indices, respectively; 、 represent the landscape fragmentation index of woodland and grassland respectively; 、 represent the aggregation index of forest land and grassland respectively.

[0037] S8, based on measuring ecological resilience through resistance and resilience; The calculation steps are as follows: (20) In formula (20), represents ecosystem resilience; Is the land use type area ratio; is the number of land use types. The resistances of cultivated land, forest land, grassland, water area, construction land, and unused land are 0.5, 1, 0.7, 0.8, 0.3, and 0.2, respectively, and the resiliences are 0.4, 0.5, 0.8, 0.7, 0.2, and 0.1, respectively.

[0038] S9, calculates water production service based on the difference between regional precipitation and evapotranspiration, using the water production module in the InVEST model; The calculation steps are as follows: (twenty one) In formula (21), Represents a grid cell Water production; Represents a grid cell Actual evapotranspiration; It means unit of annual precipitation.

[0039] S10, use The grain production in the study area was rasterized and assigned a value to obtain the grain supply service; The calculation steps are as follows: (twenty two) In formula (22), Represents a grid cell food supply services; Represents a grid cell of value; express district The sum of the values; express The total grain output of the region.

[0040] S11, Habitat quality was assessed using the HQ module in the InVEST model.

[0041] The calculation steps are as follows: (twenty three) In formula (23), Is the land use type In grid cells The habitat quality value on Land use type In grid cells the level of stress on is the half-saturation parameter; is habitat suitability; is the normalization constant.

[0042] In the embodiment of the present invention, in step three, the indicator weights are determined by determining the subjective weights through the improved G1 method, determining the objective weights through the CRITIC method, and combining the subjective and objective weights through a combined weighting method based on the Nash equilibrium point.

[0043] like Figure 2As shown, in the embodiment of the present invention, it is necessary to determine the water resources security index, food security index and ecological security index based on the water resources security status parameter indicator system, food security status parameter indicator system and ecological security status parameter indicator system based on the subjective-objective combined weighting method (combined with the improved G1 subjective weighting method and the CRITIC objective weighting method) to reflect the security status parameters of the three subsystems (water resources, food, and ecology).

[0044] Among them, the improved G1 method is a subjective weight determination method that uses experts to rank the importance of indicators and use the coefficient of variation of indicators to determine the importance ratio of adjacent indicators. Its calculation steps include: S311, set It is ranked in order of importance The evaluation indicators are ranked according to the professional knowledge and judgment of the experts to obtain a unique importance ranking: , is the number of indicators.

[0045] S312, calculate the indicator weight based on the coefficient of variation method: (twenty four) (25) (26) In formulas (24)-(26), For the ( ) of the objects ( ) indicator values; 、 、 Respectively represent The standard deviation, mean, and weight of each indicator based on the coefficient of variation method.

[0046] S313, indicator weights obtained by the coefficient of variation method Determine adjacent indicators 、 The importance ratio between : (27) S314, since the process of determining weights by G1 method is a reverse process, it is necessary to first calculate the index with the lowest importance. Weight : (28) S315, after obtaining the last indicator weight On the basis of the importance of the indicators, the weight of the previous indicator is calculated in turn by the weight of the latter indicator, and finally the weight of the previous indicator is calculated. Weight .

[0047] (29) The subjective weight obtained by the improved G1 method is recorded as .

[0048] The CRITIC method accurately reflects the contribution of each indicator to the overall indicator by quantifying the differences and conflicts between indicators. The calculation steps include: S321, select evaluation object evaluation indicators, establish The indicator data matrix, Represents the jth indicator of the i-th evaluation object, and the matrix is ​​standardized.

[0049] S322, calculate the indicator variability and measure it using the standard deviation, the same as formula (24) and (25).

[0050] S323, calculation index conflict, index The conflict size with other indicators is : (30) In formula (30), Indicates evaluation indicators and indicators The correlation coefficient between .

[0051] S324, calculation index Amount of information : (31) S325, calculation index Weight : (32) The objective weight obtained by the CRITIC method is denoted as .

[0052] Among them, the combined weighting method based on the Nash equilibrium point uses the principles of game theory to determine the comprehensive weight through the linear combination coefficient. The calculation steps are as follows: S331, respectively 、 Indicates the subjective and objective weights of evaluation indicators and The importance of the combination weight is calculated using a linear equation , the formula is as follows: (33) S332, Finding Linear Coefficients 、 The Nash equilibrium point makes the subjective and objective weights and and combination weight The deviation is the smallest, and the objective function is: (34) S333, solve the optimal combination coefficient. According to the properties of the matrix differential equation, the conditions for the optimal first-order derivative can be obtained: (35) S334, calculate the optimal combination coefficient according to the above formula 、 , and normalize it to get the optimal linear combination: (36) In an embodiment of the present invention, in step four, the order parameter is determined by identifying the Haken model, which identifies the fast and slow variables in the system and uses the "adiabatic elimination method" to eliminate the fast variables, thereby determining the slow variables that dominate the evolution of the system, that is, the order parameter.

[0053] like Figure 2 As shown, in an embodiment of the present invention, the order parameter is identified using the adiabatic elimination method based on the three subsystem security indices (water resource security index, food security index and ecological security index).

[0054] The water-food-ecological complex system is composed of three subsystems: water resources, food, and ecology. According to the water-food-ecological coordinated security status parameter indicator system, the improved G1-CRITIC combined weighting model is used to calculate the three subsystem security indices, namely the subsystem status parameters.

[0055] The Haken model assumes that during the evolution of two interrelated subsystems, if one subsystem controls the other, the state parameter of the controlling subsystem is expressed as The state parameters of the controlled subsystem are expressed as The evolution equation of the system can be expressed as: (37) (38) Discretize the model to improve the adaptability of the Haken model to discrete socioeconomic data: (39) (40) In formulas (37)-(40), 、 represent the damping coefficients of the two subsystems respectively; 、 They represent the interaction strength between the two subsystems; Indicates time. hour, and , the system reaches a stable solution, satisfying the "adiabatic approximation assumption". At this time, the subsystem Relative to subsystem is a fast variable that decays rapidly. The slow variable that dominates the evolution of the system is the order parameter.

[0056] The Haken model was used to analyze the state parameters of the three subsystems, namely water resources, food, and ecology, pairwise. The system order parameters were identified using adiabatic elimination. If the water-food-ecosystem had multiple order parameters, the summed order parameters were assumed to be slow variables, and an evolution equation was constructed to test whether the adiabatic approximation assumption was met.

[0057] In an embodiment of the present invention, in step five, the collaborative safety index is calculated by integrating the inverse of the evolution equation of the composite system to obtain a potential function, and solving the stable point of the potential function. The distance function is then used to calculate the distance between any point on the potential function and the stable point to reflect the state of the point, and the state value of each point is extremeized to obtain the collaborative safety index.

[0058] like Figure 2 As shown, in an embodiment of the present invention, after identifying the order parameter, the stable point of the potential function is further solved, the distance between any point on the potential function and the stable point is calculated, and the extreme processing is performed. The result obtained corresponds to the evolutionary synergistic security index of the water-food-ecological complex system in arid areas.

[0059] Among them, the order parameter is dominant in the entire system, and the evolution equation of the order parameter is also the evolution equation of the system: (41) Integrate the inverse of the evolution equation of the composite system to obtain the potential function: (42) when Determine the stable point of the potential function when , the equation has a unique solution, namely , then the stable state of any deviation from the stable point is determined by the straight-line distance between the point and the stable point; if , then the equation has three solutions: , , ,at this time It is not a stable point. Any point in the system will eventually return to and Since the water-food-ecosystem state parameters water resources, food, and ecological security index are all greater than 0, only the potential function graph is considered. Then, the stable point of the water-food-ecosystem co-evolution is [ , ], the distance between any point and the stable point for: (43) The larger the value, the farther the state point of the system is from the stable point, and the lower the synergy index value; conversely, the higher the synergy index. In order to more comprehensively and intuitively reflect the synergistic evolution trend of the composite system, the extreme value method is used to At the same time, Before normalizing the values, The maximum and minimum values ​​of the value are increased and decreased by 10% respectively to avoid the boundary problem of 0 and 1, and finally the evolutionary synergistic security index of the water-food-ecological complex system in arid areas is obtained: (44) Where, It represents the synergistic security index of the evolution of the water-food-ecological complex system in arid areas; Respectively The maximum and minimum values.

[0060] Based on the same inventive concept, an embodiment of the present invention further provides a device for calculating the collaborative safety index of a water-food-ecological complex system in an arid area, the device comprising: A target scope determination module is used to determine the target scope of the assessment based on the assessment needs and assessment objectives; The data acquisition module is used to obtain land use data, climate and environmental data, DEM data, and socioeconomic data within the target range, and to build a parameter indicator system for the coordinated security status of water-food-ecosystems in arid areas; A state parameter determination module is used to input the water-food-ecosystem coordinated security state parameter indicator system in arid areas into a pre-built improved G1-CRITIC combined weighting model, and obtain a water resource security index, a food security index, and an ecological security index based on the indicator weights to reflect the water-food-ecosystem security state parameters; The order parameter determination module is used to input two of the water security index, food security index and ecological security index into the pre-built Haken model, and the output of the Haken model is used to determine the order parameter. 、 、 、 The four parameter values ​​are tested to see whether they satisfy the adiabatic approximation assumption of the Haken model, and the order parameters that dominate the co-evolution of the water-food-ecosystem are identified; The safety index determination module is used to obtain the potential function based on the inverse integral of the order parameter evolution equation, solve the potential function to obtain the stable point of the co-evolution of the composite system, and use the distance function to measure the distance between any point on the potential function and the stable point to reflect the state value of the point. The state value of each point is processed by the extreme value method to obtain the evolutionary co-evolutionary safety index of the water-food-ecological composite system in arid areas.

[0061] Optionally, the water resources security status parameter indicator system includes: water resources endowment, water resources development level and water resources utilization efficiency, which presents the supply, development and utilization of water resources; water resources endowment includes per capita water resources and average annual precipitation; water resources development level includes water resources development and utilization rate, surface water resources development and utilization rate, and groundwater resources development and utilization rate; water resources utilization efficiency includes water consumption per 10,000 yuan of GDP, water consumption per 10,000 yuan of industrial added value, per mu irrigation water consumption, and per capita domestic water consumption; The food security status parameter indicator system includes: food production, consumption and stability, reflecting the basic characteristics and changing trends of food security. Food production security includes the proportion of grain planting area, per capita arable land area, mechanical power per unit area, and grain output per unit area; food consumption security includes per capita grain output, per capita disposable income, and natural population growth rate; food stability includes the intensity of fertilizer application, pesticide application intensity, agricultural film application intensity, and grain output fluctuation rate; The parameter indicator system for ecological security status includes: ecological risk, ecological health and ecological services. Ecological risks include drought risk, human activity risk, salinization risk and landscape pattern risk; ecological health includes ecological vitality, ecological organization and ecological resilience; ecological services include water production services, food supply services and habitat quality.

[0062] Optionally, the data acquisition module is used to: Based on the landscape characteristics within the target area, the target area is divided into six landscape types: cultivated land, forest land, grassland, water area, construction land and unused land, according to the method of distinguishing differences and summarizing similarities. Drought risk is measured based on the Standardized Precipitation Evapotranspiration Index (SEPTI) on a 12-month scale; Measuring human activity risk based on the human activity intensity index; A comprehensive salinity index was synthesized based on the mean of the three salinity indices to measure salinization risk; Measuring landscape pattern risk based on landscape disturbance index and landscape vulnerability index; a measure of ecological vitality based on net primary productivity; Relevant indicators based on landscape heterogeneity and landscape connectivity are used to characterize ecological organizational capacity; Based on measuring ecological resilience through resistance and resilience; Calculate water yield services based on the difference between regional precipitation and evapotranspiration, using the water yield module in the InVEST model; use The grain production in the study area was rasterized and assigned a value to obtain the grain supply service; The HQ module in the InVEST model was used to assess habitat quality.

[0063] Optionally, the subjective weight is determined based on the improved G1 method, the objective weight is determined based on the CRITIC method, and the subjective and objective weights are combined using a combined weighting method based on a Nash equilibrium point; Among them, the improved G1 method is a subjective weight determination method that uses experts to rank the importance of indicators and use the coefficient of variation of indicators to determine the importance ratio of adjacent indicators. Its calculation steps include: set up It is ranked in order of importance The evaluation indicators are ranked according to the professional knowledge and judgment of the experts to obtain a unique importance ranking: , is the number of indicators.

[0064] Calculate the indicator weights based on the coefficient of variation method: ; ; ; Where, For the ( ) of the objects ( ) indicator values; 、 、 Respectively represent The standard deviation, mean, and weight of each indicator based on the coefficient of variation method; The indicator weights obtained by the coefficient of variation method Determine adjacent indicators 、 The importance ratio between : ; Calculate the indicator with the lowest importance ranking Weight : ; Get the last indicator weight On the basis of the importance of the indicators, the weight of the previous indicator is calculated in turn by the weight of the latter indicator, and finally the weight of the previous indicator is calculated. Weight : ; The subjective weight obtained by the improved G1 method is recorded as ; The CRITIC method reflects the contribution of each indicator to the overall indicator by quantifying the differences and conflicts between indicators. The calculation steps include: Select evaluation object evaluation indicators, establish The indicator data matrix, Represents the jth indicator of the i-th evaluation object and standardizes the matrix; Calculate the indicator variability using standard deviation measure; Calculate the conflict of indicators, indicators The conflict size with other indicators is : ; in, Indicates evaluation indicators and indicators The correlation coefficient between Calculation indicators Amount of information : ; Calculation indicators Weight : ; The objective weight obtained by the CRITIC method is denoted as ; The combined weighting method based on Nash equilibrium point uses the principles of game theory to determine the comprehensive weight through linear combination coefficients. The calculation steps are as follows: Use 、 Indicates the subjective and objective weights of evaluation indicators and The importance of the combination weight is calculated using a linear equation , the formula is as follows: ; Finding linear coefficients 、 The Nash equilibrium point makes the subjective and objective weights and and combination weight The deviation is the smallest, and the objective function is: ; Solve for the optimal combination coefficients. According to the properties of the matrix differential equation, we can obtain the conditions for the optimal first-order derivative: ; According to the above formula, the optimal combination coefficient is calculated 、 , and normalize it to get the optimal linear combination: .

[0065] Optionally: The Haken model identifies the fast and slow variables in the system and uses adiabatic elimination to eliminate the fast variables, thereby determining the slow variable that dominates the evolution of the system, i.e., the order parameter; The water-grain-ecological complex system consists of three subsystems: water resources, grain, and ecology. Based on the water-grain-ecological synergistic security status parameter indicator system, the improved G1-CRITIC combined weighting model is used to calculate the three subsystem security indices, namely the subsystem status parameters. The Haken model assumes that during the evolution of two interrelated subsystems, if one subsystem controls the other, the state parameter of the controlling subsystem is expressed as The state parameters of the controlled subsystem are expressed as The evolution equation of the system can be expressed as: ; ; Discretize the model to improve the adaptability of the Haken model to discrete socioeconomic data: ; ; in, 、 represent the damping coefficients of the two subsystems respectively; 、 They represent the interaction strength between the two subsystems; Indicates time; when hour, and , the system reaches a stable solution, satisfying the "adiabatic approximation assumption"; at this time, the subsystem Relative to subsystem is a fast variable that decays rapidly. The slow variables that dominate the evolution of the system, namely the order parameters; The Haken model is used to conduct pairwise analysis on the state parameters of the three subsystems of water resources, food, and ecology, and the system order parameters are identified by the adiabatic elimination method; when there are multiple order parameters in the water-food-ecosystem, the summed order parameters are assumed to be slow variables, and an evolution equation is constructed to test whether the adiabatic approximation assumption is met.

[0066] Optionally, the collaborative safety index is calculated by integrating the inverse of the evolution equation of the composite system to obtain a potential function, solving the stable point of the potential function, and then using a distance function to measure the distance between any point on the potential function and the stable point to reflect the state of the point, and performing extreme value processing on the state value of each point to obtain the collaborative safety index; Among them, the order parameter is dominant in the entire system, and the evolution equation of the order parameter is also the evolution equation of the system: ; Integrate the inverse of the evolution equation of the composite system to obtain the potential function: ; when Determine the stable point of the potential function when , the equation has a unique solution, namely , then the stable state of any deviation from the stable point is determined by the straight-line distance between the point and the stable point; if , then the equation has three solutions: , , ,at this time It is not a stable point. Any point in the system will eventually return to and At these two stable points; since the water-food-ecosystem state parameters water resources, food, and ecological security index are all greater than 0, only the potential function graph is considered. Then, the stable point of the water-food-ecosystem co-evolution is [ , ], the distance between any point and the stable point for: ; The larger the value, the farther the state point of the system is from the stable point, and the lower the synergy index value; conversely, the higher the synergy index; In order to more comprehensively and intuitively reflect the synergistic evolution trend of the composite system, the extreme value method is used to The value is normalized; at the same time, Before normalizing the values, The maximum and minimum values ​​of the value are increased and decreased by 10% respectively to avoid the boundary problem of 0 and 1, and finally the evolutionary synergistic security index of the water-food-ecological complex system in arid areas is obtained: ; in, It represents the synergistic security index of the evolution of the water-food-ecological complex system in arid areas; Respectively The maximum and minimum values.

[0067] Based on the same inventive concept, an embodiment of the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor. When executed, the processor implements the steps in the method for calculating the collaborative safety index of the arid area water-food-ecological complex system as described in any of the above embodiments.

[0068] Based on the same inventive concept, an embodiment of the present invention also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, it implements the steps in the method for calculating the collaborative safety index of the arid area water-food-ecological complex system described in any of the above embodiments.

[0069] Based on the same inventive concept, an embodiment of the present invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps in the method for calculating the collaborative security index of the arid area water-food-ecological complex system described in any of the above embodiments.

[0070] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0071] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, apparatuses, or computer program products. Thus, embodiments of the present invention may take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0072] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (apparatuses), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as the combination of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable terminal device to produce a machine, so that the instructions executed by the processor of the computer or other programmable terminal device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0073] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable terminal device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0074] These computer program instructions can also be loaded onto a computer or other programmable terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0075] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they become aware of the basic creative concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0076] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or terminal device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or terminal device that includes the element.

[0077] The above is a detailed introduction to the method for calculating the synergistic security index of the water-food-ecological complex system in arid areas provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. The method for calculating the synergistic security index of the water-food-ecological complex system in arid areas is characterized by: The method comprises: Step 1: Determine the target scope of the assessment based on the assessment needs and assessment objectives; Step 2: Obtain land use data, climate and environmental data, DEM data, and socioeconomic data within the target area to construct a parameter indicator system for the coordinated security status of water-food-ecosystems in arid areas; Step 3: Input the parameter indicator system of the coordinated security status of water, food and ecosystem in arid areas into the pre-built improved G1-CRITIC combined weighting model, and obtain the water resources security index, food security index and ecological security index based on the indicator weights to reflect the parameters of the water, food and ecosystem security status; Step 4: Input two of each of the water security index, food security index, and ecological security index into the pre-built Haken model. 、 、 、 The four parameter values ​​are tested to see whether they satisfy the adiabatic approximation assumption of the Haken model, and the order parameters that dominate the co-evolution of the water-food-ecosystem are identified; Step five: Based on the inverse integral of the order parameter evolution equation, the potential function is obtained. The potential function is solved to obtain the stable point of the co-evolution of the composite system. The distance between any point on the potential function and the stable point is measured by the distance function to reflect the state value of the point. The state value of each point is processed by the extreme value method to obtain the evolutionary co-evolutionary security index of the water-food-ecological composite system in arid areas.

2. The method for calculating the synergistic security index of the water-food-ecological complex system in arid areas according to claim 1 is characterized in that: In step 2, the water resources security status parameter indicator system includes: water resources endowment, water resources development level and water resources utilization efficiency, which presents the supply, development and utilization of water resources; water resources endowment includes per capita water resources and average annual precipitation; water resources development level includes water resources development and utilization rate, surface water resources development and utilization rate, and groundwater resources development and utilization rate; water resources utilization efficiency includes water consumption per 10,000 yuan of GDP, water consumption per 10,000 yuan of industrial added value, per mu irrigation water consumption, and per capita domestic water consumption; The food security status parameter indicator system includes: food production, consumption and stability, reflecting the basic characteristics and changing trends of food security. Food production security includes the proportion of grain planting area, per capita arable land area, mechanical power per unit area, and grain output per unit area; food consumption security includes per capita grain output, per capita disposable income, and natural population growth rate; food stability includes the intensity of fertilizer application, pesticide application intensity, agricultural film application intensity, and grain output fluctuation rate; The parameter indicator system for ecological security status includes: ecological risk, ecological health and ecological services. Ecological risks include drought risk, human activity risk, salinization risk and landscape pattern risk; ecological health includes ecological vitality, ecological organization and ecological resilience; ecological services include water production services, food supply services and habitat quality.

3. The method for calculating the synergistic security index of the water-food-ecological complex system in arid areas according to claim 2 is characterized in that: Obtain land use data, climate and environmental data, DEM data, and socioeconomic data within the target area, and construct a parameter indicator system for the coordinated security status of water-food-ecosystems in arid areas, including: Based on the landscape characteristics within the target area, the target area is divided into six landscape types: cultivated land, forest land, grassland, water area, construction land and unused land, according to the method of distinguishing differences and summarizing similarities. Drought risk is measured based on the Standardized Precipitation Evapotranspiration Index (SEPTI) on a 12-month scale; Measuring human activity risk based on the human activity intensity index; A comprehensive salinity index was synthesized based on the mean of the three salinity indices to measure salinization risk; Measuring landscape pattern risk based on landscape disturbance index and landscape vulnerability index; a measure of ecological vitality based on net primary productivity; Relevant indicators based on landscape heterogeneity and landscape connectivity are used to characterize ecological organizational capacity; Based on measuring ecological resilience through resistance and resilience; Calculate water yield services based on the difference between regional precipitation and evapotranspiration, using the water yield module in the InVEST model; use The grain production in the study area was rasterized and assigned a value to obtain the grain supply service; The HQ module in the InVEST model was used to assess habitat quality.

4. The method for calculating the synergistic safety index of the water-food-ecological complex system in arid areas according to claim 1 is characterized in that: In step three, the subjective weight is determined based on the improved G1 method, the objective weight is determined based on the CRITIC method, and the subjective and objective weights are combined using the combined weighting method based on the Nash equilibrium point; Among them, the improved G1 method is a subjective weight determination method that uses experts to rank the importance of indicators and use the coefficient of variation of indicators to determine the importance ratio of adjacent indicators. Its calculation steps include: set up It is ranked in order of importance The evaluation indicators are ranked according to the professional knowledge and judgment of the experts to obtain a unique importance ranking: , is the number of indicators; Calculate the indicator weights based on the coefficient of variation method: ； ； ； in, For the ( ) of the objects ( ) indicator values; 、 、 Respectively represent The standard deviation, mean, and weight of each indicator based on the coefficient of variation method; The indicator weights obtained by the coefficient of variation method Determine adjacent indicators 、 The importance ratio between : ； Calculate the indicator with the lowest importance ranking Weight : ； Get the last indicator weight On the basis of the importance of the indicators, the weight of the previous indicator is calculated in turn by the weight of the latter indicator, and finally the weight of the previous indicator is calculated. Weight : ； The subjective weight obtained by the improved G1 method is recorded as ; The CRITIC method reflects the contribution of each indicator to the overall indicator by quantifying the differences and conflicts between indicators. The calculation steps include: Select evaluation object evaluation indicators, establish The indicator data matrix, Indicates the The first evaluation object indicators and standardize the matrix; Calculate the indicator variability using standard deviation measure; Calculate the conflict of indicators, indicators The conflict size with other indicators is : ； in, Indicates evaluation indicators and indicators The correlation coefficient between Calculation indicators Amount of information : ； Calculation indicators Weight : ； The objective weight obtained by the CRITIC method is denoted as ; The combined weighting method based on Nash equilibrium point uses the principles of game theory to determine the comprehensive weight through linear combination coefficients. The calculation steps are as follows: Use 、 Indicates the subjective and objective weights of evaluation indicators and The importance of the combination weight is calculated using a linear equation , the formula is as follows: ； Finding linear coefficients 、 The Nash equilibrium point makes the subjective and objective weights and and combination weight The deviation is the smallest, and the objective function is: ； Solve for the optimal combination coefficients. According to the properties of the matrix differential equation, we can obtain the conditions for the optimal first-order derivative: ； According to the above formula, the optimal combination coefficient is calculated 、 , and normalize it to get the optimal linear combination: 。 5. The method for calculating the synergistic safety index of the water-food-ecological complex system in arid areas according to claim 1 is characterized by: In step 4, the Haken model identifies the fast and slow variables in the system and uses adiabatic elimination to eliminate the fast variables, thereby determining the slow variable that dominates the system evolution, namely the order parameter. The water-grain-ecological complex system consists of three subsystems: water resources, grain, and ecology. Based on the water-grain-ecological synergistic security status parameter indicator system, the improved G1-CRITIC combined weighting model is used to calculate the three subsystem security indices, namely the subsystem status parameters. The Haken model assumes that during the evolution of two interrelated subsystems, if one subsystem controls the other, the state parameter of the controlling subsystem is expressed as The state parameters of the controlled subsystem are expressed as The evolution equation of the system can be expressed as: ； ； Discretize the model to improve the adaptability of the Haken model to discrete socioeconomic data: ； ； in, 、 represent the damping coefficients of the two subsystems respectively; 、 They represent the interaction strength between the two subsystems; Indicates time; when hour, and , the system reaches a stable solution, satisfying the "adiabatic approximation assumption"; at this time, the subsystem Relative to subsystem is a fast variable that decays rapidly. The slow variables that dominate the evolution of the system, namely the order parameters; The Haken model is used to conduct pairwise analysis on the state parameters of the three subsystems of water resources, food, and ecology, and the system order parameters are identified by the adiabatic elimination method; when there are multiple order parameters in the water-food-ecosystem, the summed order parameters are assumed to be slow variables, and an evolution equation is constructed to test whether the adiabatic approximation assumption is met.

6. The method for calculating the synergistic security index of the water-food-ecological complex system in arid areas according to claim 1 is characterized in that: In step 5, the collaborative safety index is calculated by integrating the inverse of the evolution equation of the composite system to obtain the potential function, solving the stable point of the potential function, and then using the distance function to measure the distance between any point on the potential function and the stable point to reflect the state of the point, and then performing extreme value processing on the state value of each point to obtain the collaborative safety index; Among them, the order parameter is dominant in the entire system, and the evolution equation of the order parameter is also the evolution equation of the system: ； Integrate the inverse of the evolution equation of the composite system to obtain the potential function: ； when Determine the stable point of the potential function when , the equation has a unique solution, namely , then the stable state of any deviation from the stable point is determined by the straight-line distance between the point and the stable point; if , then the equation has three solutions: , , ,at this time It is not a stable point. Any point in the system will eventually return to and At these two stable points; since the water-food-ecosystem state parameters water resources, food, and ecological security index are all greater than 0, only the potential function graph is considered. Then, the stable point of the water-food-ecosystem co-evolution is [ , ], the distance between any point and the stable point for: ； The larger the value, the farther the state point of the system is from the stable point, and the lower the synergy index value; conversely, the higher the synergy index; In order to more comprehensively and intuitively reflect the synergistic evolution trend of the composite system, the extreme value method is used to The value is normalized; at the same time, Before normalizing the values, The maximum and minimum values ​​of the value are increased and decreased by 10% respectively to avoid the boundary problem of 0 and 1, and finally the evolutionary synergistic security index of the water-food-ecological complex system in arid areas is obtained: ； in, It represents the synergistic security index of the evolution of the water-food-ecological complex system in arid areas; Respectively The maximum and minimum values.

7. A device for calculating the synergistic safety index of a water-grain-ecological complex system in an arid area, characterized by: The device comprises: A target scope determination module is used to determine the target scope of the assessment based on the assessment needs and assessment objectives; The data acquisition module is used to obtain land use data, climate and environmental data, DEM data, and socioeconomic data within the target range, and to build a parameter indicator system for the coordinated security status of water-food-ecosystems in arid areas; A state parameter determination module is used to input the water-food-ecosystem coordinated security state parameter indicator system in arid areas into a pre-built improved G1-CRITIC combined weighting model, and obtain a water resource security index, a food security index, and an ecological security index based on the indicator weights to reflect the water-food-ecosystem security state parameters; The order parameter determination module is used to input two of the water security index, food security index and ecological security index into the pre-built Haken model, and the output of the Haken model is used to determine the order parameter. 、 、 、 The four parameter values ​​are tested to see whether they satisfy the adiabatic approximation assumption of the Haken model, and the order parameters that dominate the co-evolution of the water-food-ecosystem are identified; The safety index determination module is used to obtain the potential function based on the inverse integral of the order parameter evolution equation, solve the potential function to obtain the stable point of the co-evolution of the composite system, and use the distance function to measure the distance between any point on the potential function and the stable point to reflect the state value of the point. The state value of each point is processed by the extreme value method to obtain the evolutionary co-evolutionary safety index of the water-food-ecological composite system in arid areas.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, it implements the method for calculating the collaborative safety index of the arid area water-food-ecological complex system as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements the method for calculating the collaborative safety index of the water-food-ecological complex system in arid areas as described in any one of claims 1 to 6.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the steps in the method for calculating the collaborative safety index of the water-food-ecological complex system in arid areas described in any one of claims 1 to 6 are implemented.

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