A regional drought resilience simulation analysis method and device

Abstract

The present invention discloses a regional drought resilience simulation analysis method and device, which relates to the technical field of regional drought resilience analysis, in order to solve the problem of low regional drought resilience prediction accuracy of static analysis models in the prior art. The method comprises: obtaining observation data of a target drought resilience system corresponding to a target area; constructing a regional drought resilience system dynamics model corresponding to the target drought resilience system based on preset system boundaries and observation data; constructing an initial drought resilience assessment index system based on a causal relationship loop diagram and the relationship between stock and flow; based on a preset simulation scenario, using a regional drought resilience system dynamics model to perform scenario simulation and output corresponding dynamic change results; adjusting the initial drought resilience assessment index system based on the dynamic change results to obtain a target drought resilience assessment index system. The present invention is used to improve the prediction accuracy of regional drought resilience.

Description

Technical Field

[0001] The present invention belongs to the technical field of drought resilience analysis, and in particular to a method and device for simulating and analyzing regional drought resilience. Background Art

[0002] Water supply security is facing increasingly severe challenges. Rapid urbanization, economic development and population growth have led to a surge in urban water demand, which has exacerbated the potential negative impact of drought. The urban system is complex, with internal elements interrelated and mutually influential. Changes in any element may trigger a chain reaction, ultimately affecting the entire urban system.

[0003] As an external environmental pressure, drought can have a significant impact on the internal elements of the urban system. The process and ability of the urban system to cope with and adapt to drought reflect its drought resilience. Regional drought resilience refers to the ability of the urban system to resist, absorb and adapt to drought risks by taking engineering and non-engineering measures to maintain its basic characteristics, structure and key functions when encountering drought pressure. Due to the complex interactions between social and natural systems, resource and environmental issues are often viewed as complex systemic challenges. Enhancing regional resilience is essential to cope with changes and shocks and achieve sustainable development.

[0004] Existing drought research has developed from drought identification, spatiotemporal distribution and detection of driving factors to drought risk monitoring, vulnerability assessment and risk management, and the assessment of drought resilience has also received increasing attention. Researchers have proposed a variety of drought resilience simulation analysis frameworks, such as a framework based on "socio-economic-environmental-institutional", and methods to quantify urban flood occurrence, drought severity and related resilience. However, most of these methods are limited to static assessments of the current status or history of drought resilience, lack quantitative analysis of the feedback relationship between internal elements of the regional drought resilience system, and are difficult to effectively predict the drought resilience system with uncertainty. Existing technologies, such as time series analysis, multivariate regression analysis, artificial neural networks, gray clustering analysis, GIS spatial analysis and information diffusion methods, all use static assessment methods. Although they have been applied to related studies, it is still difficult to effectively predict the drought resilience system with uncertainty. Summary of the invention

[0005] The purpose of the present invention is to provide a regional drought resilience simulation analysis method and device for improving the prediction accuracy of drought resilience in a target area.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a regional drought resilience simulation analysis method, comprising:

[0008] Obtaining observation data of a target drought resilience system corresponding to a target area; the observation data at least includes socioeconomic data, precipitation data, and water use data;

[0009] Based on the preset system boundary and the observed data, a regional drought resilience system dynamics model corresponding to the target drought resilience system is constructed; the regional drought resilience system dynamics model at least includes a causal relationship loop diagram and a control equation; the causal relationship loop diagram is used to describe the interaction between multiple resilience elements in the regional drought resilience system; the control equation is used to describe the relationship between the stock and flow in the regional drought resilience system; the multiple resilience elements are identified based on the observed data; the stock and the flow are identified based on the causal relationship diagram;

[0010] Based on the causal relationship loop diagram and the relationship between the stock and flow, an initial drought resilience assessment index system is constructed;

[0011] Based on the preset simulation scenario, the regional drought resilience system dynamics model is used to perform scenario simulation and output corresponding dynamic change results;

[0012] Adjusting the initial drought resilience assessment index system based on the dynamic change result to obtain a target drought resilience assessment index system;

[0013] Calculate the resilience index of the target drought resilience assessment index system.

[0014] Optionally, constructing a regional drought resilience system dynamics model corresponding to the target drought resilience system based on a preset system boundary and the observation data includes:

[0015] Determining the preset system boundary as the system boundary of the target drought resilience system;

[0016] Structural division of the target drought resilience system is performed to obtain multiple subsystems; the subsystems include a water resources subsystem, a population subsystem, an economic subsystem, a science and technology subsystem, and a social subsystem;

[0017] Analyzing and identifying the observation data to obtain the plurality of resilience factors;

[0018] Based on the multiple resilience factors, the causal relationship diagram is constructed by using the Liang-Kleeman information flow analysis method;

[0019] Based on the cause-effect relationship diagram, system components of each subsystem are identified, wherein the system components include the stock and the flow; the flow is an influencing factor of the stock;

[0020] constructing a control equation between the inventory and the flow;

[0021] Based on the cause-effect relationship diagram and the control equation, an initial regional drought resilience system dynamics model is constructed;

[0022] The initial regional drought resilience system dynamics model is set up and verified to obtain the regional drought resilience system dynamics model.

[0023] Optionally, the causal relationship diagram is constructed based on the multiple resilience factors using the Liang-Kleeman information flow analysis method, including:

[0024] Analyzing and obtaining a plurality of variables from the plurality of resilience factors;

[0025] Construct a vector autoregression model of the first variable and the second variable in the multiple variables:

[0026]

[0027] Wherein, the first variable and the second variable are any two variables among a plurality of variables; Y(t) represents the value of the first variable Y at time t; X(t) represents the value of the second variable X at time t; α and α' are constant terms; βᵢ, γⱼ, β'ᵢ, γ'ⱼ are preset model coefficients; ε(t) and ε'(t) are error terms;

[0028] Based on the vector autoregression model, using the Liang-Klieman information flow formula, a first information flow of the first variable to the second variable is calculated, and a second information flow of the second variable to the first variable is calculated;

[0029] comparing the sizes of the first information flow and the second information flow;

[0030] If the first information flow is larger than the second information flow by a first preset threshold, a first causal relationship is marked; the first causal relationship indicates that the first variable has a causal influence on the second variable, and the causal influence direction is from the first variable to the second variable;

[0031] If the second information flow is greater than the first information flow by a second preset threshold, a second causal relationship is marked; the second causal relationship indicates that the second variable has a causal influence on the first variable, and the causal influence direction is from the second variable to the first variable;

[0032] If the difference between the first information flow and the second information flow is less than a preset bidirectional causal determination threshold, and both the first information flow and the second information flow are greater than a third preset threshold, then a bidirectional causal relationship is marked; the bidirectional causal relationship indicates that the first variable and the second variable are mutually causal;

[0033] If the difference between the first information flow and the second information flow is greater than or equal to the bidirectional causal determination threshold, no causal relationship is marked;

[0034] If both the first information flow and the second information flow are smaller than the third preset threshold, no causal relationship is marked; if both the first information flow and the second information flow are smaller than the third preset threshold, no causal relationship is marked; wherein the third preset threshold is smaller than the preset bidirectional causal determination threshold, the preset bidirectional causal determination threshold is smaller than or equal to the first preset threshold, and the preset bidirectional causal determination threshold is smaller than or equal to the second preset threshold;

[0035] When all variables are analyzed, the causal relationship diagram is obtained.

[0036] Optionally, the stock includes at least a gross domestic product associated with the economic subsystem; the multiple flows include at least a GDP growth rate affecting the gross domestic product;

[0037] The constructing of a control equation between the inventory and the flow rate includes:

[0038] Based on the gross domestic product and the GDP growth rate, the socioeconomic control equation is constructed:

[0039]

[0040] in, is the gross domestic product, is the GDP growth rate, is the initial value of GDP.

[0041] Optionally, the stock further includes community sensitivity associated with the social subsystem; the multiple flows further include sensitive indicators and weighting factors affecting the community sensitivity; the weighting factors are used to quantify the relative importance of water resources and the gross domestic product to the community sensitivity;

[0042] The constructing of a control equation between the inventory and the flow rate includes:

[0043] Based on the community sensitivity, the sensitivity index and the weighting factor, a community sensitivity control equation is constructed:

[0044] ;

[0045] in, for community sensitivity; It is a simplified sensitivity index, which reflects the impact of the macro environment on community sensitivity; is the weighting factor; for water resources;

[0046] is the critical community sensitivity value.

[0047] Optionally, the stock further includes a technology level associated with the technology subsystem; the multiple flows further include a technology level growth rate, a technology investment proportion, and an output index of technology investment that affect the technology level; and constructing a control equation between the stock and the flow includes:

[0048] Based on the technology level, the technology level growth rate, the technology investment proportion and the output index of technology investment, the technology level control equation is constructed:

[0049] ;

[0050] in,

[0051] ;

[0052] ;

[0053] Indicates the level of science and technology; Indicates the growth rate of scientific and technological level; Indicates the growth coefficient of the level of science and technology; Indicates the proportion of investment in science and technology; represents the output index of technology investment; is the constitutive relation that describes the relationship between the GDP and Feedback between It is the community sensitivity influencing factor.

[0054] Optionally, the stock further includes the population number associated with the population subsystem; the multiple flows further include population growth rate, natural population growth rate and water resource carrying capacity;

[0055] The constructing of a control equation between the inventory and the flow rate includes:

[0056] Based on the population size, the population growth rate, the natural population growth rate and the water resources carrying capacity, a population size control equation is constructed:

[0057] ;

[0058] in,

[0059] ;

[0060] ;

[0061] ;

[0062] is the number of said population; is the population growth rate; is the natural growth rate of the population; is the influencing factor of education level; is the influencing factor of age structure; is the factor affecting community sensitivity on population growth rate; is the water resources carrying capacity; is the amount of available water resources; is the comprehensive water consumption per capita; They all represent the corresponding constitutive relations.

[0063] Optionally, the inventory further includes a water resource amount associated with the water resource subsystem; the multiple flows further include precipitation affecting the water resource amount;

[0064] The constructing of a control equation between the inventory and the flow rate includes:

[0065] Based on the water resources and the precipitation, a water resources control equation is constructed:

[0066] ;

[0067] in, is the amount of water resources; is the precipitation amount; is the runoff coefficient;

[0068] The constructing of the control equation between the stock and the flow also includes: constructing a control equation for domestic water demand: ;

[0069] in,

[0070] ;

[0071] is the domestic water intensity; Minimum domestic water consumption; The maximum domestic water consumption; These are the corresponding constitutive relations;

[0072] The constructing of the control equation between the stock and the flow also includes: constructing a control equation for domestic water demand:

[0073] ;

[0074] in, is the amount of available water resources, is the ratio of available water resources.

[0075] Optionally, constructing an initial drought resilience assessment index system based on the causal relationship loop diagram and the relationship between the stock and flow includes:

[0076] Based on the causal relationship loop diagram and the relationship between the stock and the flow, select a plurality of key indicators corresponding to each subsystem from the plurality of resilience factors, the stock and the flow;

[0077] Based on the multiple key indicators, the initial drought resilience assessment index system is constructed.

[0078] Compared with the prior art, the regional drought resilience simulation and analysis method provided by the present invention constructs a regional drought resilience system dynamics model to analyze the causal relationship and stock-flow relationship of each resilience element in the regional drought resilience system under different simulation scenarios, and adjusts the regional drought resilience assessment index system according to the model output results and calculates the final resilience index, thereby realizing a dynamic assessment of regional drought resilience and improving the accuracy of regional drought resilience simulation analysis.

[0079] In a second aspect, the present invention further provides a regional drought resilience simulation and analysis device, comprising:

[0080] An acquisition module, used to acquire observation data of a target drought resilience system corresponding to a target area; the observation data at least includes socio-economic data, precipitation data and water use data;

[0081] A first construction module is used to construct a regional drought resilience system dynamics model corresponding to the target drought resilience system based on a preset system boundary and the observation data; the regional drought resilience system dynamics model at least includes a causal relationship loop diagram and a control equation; the causal relationship loop diagram is used to describe the interaction between multiple resilience elements in the regional drought resilience system; the control equation is used to describe the relationship between the stock and flow in the regional drought resilience system; the multiple resilience elements are identified based on the observation data; the stock and the flow are identified based on the causal relationship diagram;

[0082] A second construction module is used to construct an initial drought resilience assessment indicator system based on the causal relationship loop diagram and the relationship between the stock and flow;

[0083] A simulation module, for performing scenario simulation based on a preset simulation scenario using the regional drought resilience system dynamics model and outputting corresponding dynamic change results;

[0084] An adjustment module, used for adjusting the initial drought resilience assessment index system based on the dynamic change result to obtain a target drought resilience assessment index system;

[0085] A calculation module is used to calculate the resilience index of the target drought resilience assessment index system. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0087] Figure 1 One of the flow charts of a regional drought resilience simulation analysis method provided by an embodiment of the present invention;

[0088] Figure 2 A causal loop diagram provided for one embodiment of the present invention;

[0089] Figure 3 A stock-flow diagram provided for one embodiment of the present invention;

[0090] Figure 4 Model simulation output results of simulated and observed values ​​of GDP provided for one embodiment of the present invention;

[0091] Figure 5 Model simulation output results of simulated values ​​and observed values ​​of community sensitivity provided for one embodiment of the present invention;

[0092] Figure 6 Model simulation output results of simulated values ​​and observed values ​​of technology level provided for one embodiment of the present invention;

[0093] Figure 7 Model simulation output results for simulated values ​​and observed values ​​of population numbers provided for one embodiment of the present invention;

[0094] Figure 8 A model simulation output result of a simulated value and an observed value of production water consumption provided for one embodiment of the present invention;

[0095] Fig. 9 A model simulation output result of simulated values ​​and observed values ​​of domestic water consumption provided by one embodiment of the present invention;

[0096] Fig.10 A model simulation output result of a simulated value and an observed value of a water resource amount provided by an embodiment of the present invention;

[0097] Fig.11A model simulation output result on regional drought resilience level provided by one embodiment of the present invention;

[0098] Fig.12 A second flow chart of a regional drought resilience simulation analysis method provided by an embodiment of the present invention;

[0099] Fig.13 A schematic structural diagram of a regional drought resilience simulation and analysis device provided for one embodiment of the present invention. DETAILED DESCRIPTION

[0100] In order to clearly describe the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, words such as "first" and "second" are used to distinguish the same items or similar items with basically the same functions and effects. For example, the first threshold and the second threshold are only used to distinguish different thresholds, and their order is not limited. Those skilled in the art can understand that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit them to be different.

[0101] Fig.13 A schematic structural diagram of a regional drought resilience simulation and analysis device provided for one embodiment of the present invention.

[0102] It should be noted that, in the present invention, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0103] In the present invention, "at least one" means one or more, "more than one" means two or more. "And / or" describes the association relationship of the associated objects, indicating that three types of relationships may exist.

[0104] like Figure 1 As shown, an embodiment of the present invention provides a regional drought resilience simulation analysis method, which may include:

[0105] Step 110: Obtain observation data of the target drought resilience system corresponding to the target area; the observation data at least includes socio-economic data, precipitation data, and water use data;

[0106] Drought resilience refers to the ability of arid ecosystems to adapt and maintain functionality by adjusting their spatial structure in the face of changing environmental conditions such as water scarcity.

[0107] For example, the target area may be a city, and the city may be City A.

[0108] Observational data are all observed values ​​measured on a digital scale, and the results can be expressed as specific values. For example, GDP and employment data in socio-economic data; precipitation data such as precipitation and precipitation intensity; water use data such as water consumption and water use efficiency. These data are collected through surveys or observations, and are not obtained under conditions of artificial control. For example, socio-economic data are often obtained through statistics and surveys; precipitation data are obtained through meteorological observations; and water use data are obtained through water resources management and monitoring activities.

[0109] Step 120: Based on the preset system boundary and observation data, a regional drought resilience system dynamics model corresponding to the target drought resilience system is constructed; the regional drought resilience system dynamics model at least includes a causal relationship loop diagram and a control equation; the causal relationship loop diagram is used to describe the interaction between multiple resilience elements in the regional drought resilience system; the control equation is used to describe the relationship between the stock and flow in the regional drought resilience system; multiple resilience elements are identified based on the observation data; the stock and flow are identified based on the causal relationship diagram;

[0110] Specifically, the beneficial effects of the system dynamics model are as follows: the system dynamics model has the advantage of capturing the long-term evolution characteristics of complex systems. Based on the system dynamics model, a high-order, nonlinear, time-dynamic, and multiple feedback mechanism regional drought resilience system dynamics model can be constructed to achieve dynamic evaluation of the target drought resilience system. The embodiment of the present invention specifically constructs a regional drought resilience system dynamics model to achieve dynamic evaluation of the target drought resilience system. This can solve the problem that the static evaluation model lacks quantitative analysis of the feedback relationship between the internal elements of the regional drought resilience system and is difficult to effectively predict the drought resilience system with uncertainty. The prediction accuracy of drought resilience in the target area can be improved through dynamic evaluation results.

[0111] It can be understood that the stock (or state variable) is a variable that describes the state or quantity of the regional drought resilience system at a certain moment. It represents the accumulated amount of a certain resource, substance or energy within the system.

[0112] Flow is a variable that describes the rate at which a state variable changes over time. It represents the amount of resources, substances, or energy that enters or leaves a system per unit time.

[0113] The governing equation is a mathematical expression that describes how a state variable (stock) changes over time. It usually contains the derivative of the state variable (i.e., the change in flow) and the relationship with other related variables. The governing equation is a bridge between stock and flow. It describes how the stock changes according to the change in flow, thus providing a complete description of the dynamic behavior of the system. By solving the governing equation, the state (stock) and change in state (flow) of the regional drought resilience system at future moments can be predicted.

[0114] Step 120 may specifically include:

[0115] 1) Determine the preset system boundary as the system boundary of the target drought resilience system;

[0116] Specifically, the preset system boundary can be an administrative region boundary. The administrative region boundary of City A is used as the boundary of the regional drought resilience system, covering all administrative districts within City A and all land and water areas under the jurisdiction of the city. This boundary is clear and unambiguous, easy to obtain and apply to analysis, and can directly reflect the scope of urban administration.

[0117] 2) Structural division of the target drought resilience system to obtain multiple subsystems; the subsystems include water resources subsystem, population subsystem, economic subsystem, science and technology subsystem and social subsystem;

[0118] In step 2), the purpose of structural division of the regional drought resilience system is to analyze the main elements that constitute the system (that is, the various subsystems), and then analyze the interactions and dependencies between the various subsystems, so as to understand how the target drought resilience system responds to drought stress.

[0119] After the system is divided, we can analyze how water availability affects population growth and economic development (GDP), and how scientific and technological progress can improve water resource utilization efficiency and enhance the drought adaptation capacity of communities. By analyzing the material flow, energy flow, information flow and positive and negative feedback loops between these subsystems, we can more clearly understand the operating mechanism of the regional drought resilience system and provide a scientific basis for enhancing its resilience.

[0120] 3) Analyze and identify multiple resilience factors from the observed data. For example, multiple resilience factors may include: Figure 2 Production water use, water resource quantities, community sensitivity, etc. as shown.

[0121] 4) Based on multiple resilience factors, the Liang-Kleeman information flow analysis method is used to construct a causal relationship diagram;

[0122] The advantages of the causal cycle diagram are mainly reflected in the following aspects: First, the causal relationship is displayed intuitively, that is, the complex causal relationship is clearly presented in a graphical way, which is easy to understand and analyze; second, the cycle diagram can show the multiple causes of the problem and their interactions, which helps to deeply analyze the essence of the problem and find the root cause of the problem; third, by intuitively displaying the causal relationship, decision makers can grasp the key to the problem more quickly and formulate more effective decision-making plans; fourth, the causal cycle diagram can dynamically reflect the changing process of the system, help people understand the development trend of the system, and provide strong support for prediction and planning.

[0123] Step 4) may specifically include:

[0124] Analyze variables sub-step: Analyze multiple resilience factors to obtain multiple variables; the multiple variables at least include Figure 2 The variables shown include production water use, water resources, community sensitivity, gross domestic product (GDP), technology level, GDP share of production water use, precipitation, drought, domestic water use, domestic water use intensity, population control policy, population size, drought response, etc.

[0125] Regression module construction sub-step: Construct a vector autoregression model of the first variable and the second variable among multiple variables:

[0126]

[0127] Wherein, the first variable and the second variable are any two variables among the multiple variables; Y(t) represents the value of the first variable Y at time t; X(t) represents the value of the second variable X at time t; α and α' are constant terms; βᵢ, γⱼ, β'ᵢ, γ'ⱼ are preset model coefficients; ε(t) and ε'(t) are error terms;

[0128] It should be noted that the vector autoregression model is suitable for the analysis and prediction of multivariate time series. It is used to estimate the dynamic relationship of joint endogenous variables, capture the dynamic interaction between multiple variables, reveal their changing patterns over time, and is easy to predict.

[0129] Information flow calculation sub-step: based on the vector autoregression model, using the Liang-Klieman information flow formula, calculate the first information flow of the first variable to the second variable, and calculate the second information flow of the second variable to the first variable;

[0130] The causal relationship judgment sub-steps are as follows:

[0131] Comparing the size of the first information flow and the second information flow;

[0132] If the first information flow is greater than the second information flow by a first preset threshold, a first causal relationship is marked; the first causal relationship indicates that the first variable has a causal influence on the second variable, and the causal influence direction is from the first variable to the second variable;

[0133] If the second information flow is greater than the first information flow by a second preset threshold, a second causal relationship is marked; the second causal relationship indicates that the second variable has a causal influence on the first variable, and the causal influence direction is from the second variable to the first variable;

[0134] If the difference between the first information flow and the second information flow is less than a preset bidirectional causal determination threshold, and both the first information flow and the second information flow are greater than a third preset threshold, then a bidirectional causal relationship is marked; the bidirectional causal relationship indicates that the first variable and the second variable are mutually causal;

[0135] If the difference between the first information flow and the second information flow is greater than or equal to the bidirectional causal determination threshold, no causal relationship is marked;

[0136] If both the first information flow and the second information flow are smaller than the third preset threshold, no causal relationship is marked.

[0137] The third preset threshold may also be referred to as a significance threshold.

[0138] The significance threshold is less than the preset bidirectional causal determination threshold, the preset bidirectional causal determination threshold is less than or equal to the first preset threshold, the preset bidirectional causal determination threshold is less than or equal to the second preset threshold, and the first preset threshold is approximately equal to the second preset threshold.

[0139] Finally, closed causal chains are identified through loop analysis to form feedback loops, and they are determined to be positive feedback (self-reinforcement) or negative feedback (self-regulation) to fully understand the interactions and dynamic changes of various elements in the regional drought resilience system.

[0140] When all variables are analyzed, a cause-effect diagram is obtained, see Figure 2 .

[0141] 5) Based on the cause-effect diagram, the system components of each subsystem are identified. The system components include endogenous variables and exogenous variables.

[0142] Endogenous variables are used to describe the internal state and dynamic changes of the system. They are usually unknown quantities that the model needs to solve, including state variables, rate variables, auxiliary variables, and constants. Exogenous variables are used to describe the external environment of the system, representing external driving factors, and their values ​​are usually known input conditions. Exogenous variables have an impact on endogenous variables, but they themselves are not controlled by the feedback regulation of the system.

[0143] It can be understood that endogenous variables include stocks and flows, while exogenous variables are external input variables independent of stocks or flows.

[0144] 6) Construct the control equation between stock and flow;

[0145] 7) Based on the cause-effect diagram and control equations, the initial regional drought resilience system dynamics model was constructed;

[0146] 8) The initial regional drought resilience system dynamics model was set up and verified to obtain the regional drought resilience system dynamics model.

[0147] Step 130: construct an initial drought resilience assessment indicator system based on the causal relationship loop diagram and the relationship between stock and flow;

[0148] Step 140: Based on the preset simulation scenario, a regional drought resilience system dynamics model is used to perform scenario simulation and output corresponding dynamic change results;

[0149] It is understandable that multiple possible future scenarios are constructed to evaluate the dynamic response and evolution trend of regional drought resilience systems under various conditions. The scenario design covers climate change (such as changes in precipitation and drought frequency), socio-economic development (such as population growth and changes in water demand), policy intervention (such as water-saving strategies and technological improvements), and extreme event scenarios (such as persistent drought). For different scenarios, dynamic simulations are performed by adjusting the exogenous variables of the model (such as precipitation, economic growth, etc.) and initial conditions to predict the changing trends of key resilience factors (such as water resources stock, community sensitivity) and the response pattern of the system to drought stress.

[0150] Step 150: adjusting the initial drought resilience assessment index system based on the dynamic change result to obtain a target drought resilience assessment index system;

[0151] It is understandable that adjusting the selected indicator system according to the results of dynamic changes can better represent the dynamic changes of the resilience system.

[0152] Step 160: Calculate the resilience index of the target drought resilience assessment index system.

[0153] It is understandable that since the evaluation index system may contain indicators of different dimensions and orders of magnitude, in order to eliminate the dimensional differences between the indicators, it is necessary to standardize the indicator data output by the model simulation. For example, the minimum-maximum normalization method can be selected to convert the indicator data into a dimensionless value so that it falls within the same range, such as between 0 and 1.

[0154] In order to reduce the impact of the correlation between indicators, the CRITIC-TOPSIS method is used to calculate the weight and comprehensive score of each indicator to obtain the results of the toughness index during the simulation period.

[0155] From the above content, it can be seen that the embodiments of the present invention have the following beneficial effects: The present invention provides a regional drought resilience simulation analysis method based on a system dynamics model, which can make up for the low prediction accuracy caused by static evaluation of regional drought resilience in the prior art. This method analyzes the causal relationship and stock-flow relationship of each resilience element in the regional drought resilience system under different simulation scenarios by constructing a regional drought resilience system dynamics model, and adjusts the regional drought resilience evaluation index system according to the model output results and calculates the final resilience index, thereby realizing a dynamic evaluation of regional drought resilience and improving the accuracy of regional drought resilience simulation analysis, thereby providing scientific guidance for local resilience management decision support and providing reference for other regions facing similar problems.

[0156] In an optional embodiment, in combination Figure 3 To understand, in step 5), the stock at least includes the gross domestic product (GDP) associated with the economic subsystem; multiple flows at least include the GDP growth rate that affects the GDP. Then, step 6) can specifically include: based on the GDP and GDP growth rate, construct the socio-economic control equation:

[0157] ;

[0158] in, is the gross domestic product, is the GDP growth rate, is the initial value of GDP.

[0159] In an optional embodiment, in combination Figure 3 Understand that in step 5), the stock also includes community sensitivity associated with the social subsystem; multiple flows also include sensitive indicators and weighting factors that affect community sensitivity; the weighting factors are used to quantify the relative importance of water resources and GDP to community sensitivity; then step 6) can specifically include:

[0160] Based on community sensitivity, sensitivity index and weighting factor, the community sensitivity control equation is constructed:

[0161]

[0162] in, It is community sensitivity; It is a simplified sensitivity index, which reflects the impact of the macro environment on community sensitivity; is the weighting factor; for water resources; in addition, It is a critical community sensitivity value.

[0163] In the embodiment of the present invention, constructing the community sensitivity control equation has the following benefits: community sensitivity is used to dynamically simulate the feedback between the water system and human society, that is, the human decision-making process, to describe human perception of changes in ecosystem conditions and determine human behavioral responses. Water resource conditions are used as the main variable driving community sensitivity. In addition, the level of urban economy can improve environmental resilience and reduce community sensitivity.

[0164] In an optional embodiment, in combination Figure 3 To understand, in step 5), the stock also includes the technology level associated with the technology subsystem; multiple flows also include the technology level growth rate, technology investment proportion and technology investment output index that affect the technology level; then step 6) can specifically include:

[0165] Based on the technology level, technology growth rate, technology investment proportion and technology investment output index, the technology level control equation is constructed:

[0166]

[0167] in,

[0168]

[0169]

[0170] Indicates the level of science and technology; Indicates the growth rate of scientific and technological level; Indicates the growth coefficient of the level of science and technology; Indicates the proportion of investment in science and technology; represents the output index of technology investment; is the constitutive relation that describes the relationship between GDP and Feedback between is the community sensitivity influencing factor; the feedback between the community sensitivity influencing factor and community sensitivity is described by the constitutive relationship.

[0171] In the embodiment of the present invention, constructing a technology level control method has the following advantages: the technology level represents the ability of human beings to develop and utilize water resources, which is set as a function of social economy. In the present invention, the influence of community sensitivity is added. In the above equation, in the equation, Describes the bottleneck effect of scientific and technological development; components represents the role of science and technology investment in promoting science and technology. In addition, in this study, It can be roughly defined as ,in is the minimum water demand per 10,000 yuan of GDP. is the water demand per 10,000 G DP, i.e. The value of can be approximately taken as .

[0172] In an optional embodiment, in combination Figure 3 It is understood that in step 5), the stock also includes the population associated with the population subsystem; multiple flows also include population growth rate, natural population growth rate and water resource carrying capacity; then step 6) may specifically include:

[0173] Based on population size, population growth rate, natural population growth rate and water resources carrying capacity, the population control equation is constructed:

[0174]

[0175] in,

[0176]

[0177] is the population; is the population growth rate; is the natural population growth rate; is the influencing factor of education level; is the influencing factor of age structure; is the factor affecting community sensitivity on population growth rate; is the water resources carrying capacity; is the amount of available water resources; is the comprehensive water consumption per capita; They all represent the corresponding constitutive relations.

[0178] It is understandable that the population control equation has the following benefits: by dynamically linking the population growth rate with the water resources carrying capacity, it can reflect the impact of population growth on the sustainability of natural resources and the key role of community sensitivity in regulating population dynamics. Furthermore, by introducing social factors such as education level and age structure into the model, it is possible to accurately describe the social driving mechanism of population growth and capture the interaction between human activities and the ecosystem through water resource constraints, thus providing a reference for policy making for the coordinated development of population and resources.

[0179] In an optional embodiment, in combination Figure 3It is understood that in step 5), the stock also includes the water resources associated with the water resources subsystem; the multiple flows also include the precipitation that affects the water resources; then step 6) may specifically include:

[0180] Based on water resources and precipitation, the water resources control equation is constructed:

[0181]

[0182] in, is the amount of water resources; is the precipitation; is the runoff coefficient;

[0183] It can be understood that the water resource equation represents the dynamic changes in the amount of water resources, assuming that urban water resource storage will be consumed by production water and domestic water use, and the outflow for downstream discharge is almost zero.

[0184] In an optional embodiment, the total water requirement Including production water requirements , Living water demand , production water demand includes agricultural water demand and industrial water demand .

[0185]

[0186] In urban areas, the reduction in production water demand mainly comes from industrial structural transformation. Here, this study uses the evolution of production water demand share to reflect industrial structural transformation. The logic equation is as follows:

[0187]

[0188] in is the rate of change of production water demand, is the minimum water requirement for production, It is the shifting change in the share of GDP produced. It is the ten-year moving average of the production share of GDP. It is the difference between the average of the production GDP shares in the last five years and the first five years of the ten-year period. It is a factor that indicates the impact of economic conditions on the reduction of production water requirements.

[0189] Domestic water demand is the product of the number of residents and the intensity of domestic water use. Economic growth will increase domestic water use intensity, while the improvement of scientific and technological level will promote the development of water-saving technologies to reduce domestic water use intensity. In addition, government decisions driven by community sensitivity, such as raising water prices and limiting water consumption, can significantly reduce residents' water use. Therefore, considering the above three aspects, the equation for domestic water demand is expressed as follows:

[0190]

[0191] in, ;

[0192] is the domestic water intensity; Minimum domestic water consumption; The maximum domestic water consumption; These are the corresponding constitutive relations.

[0193] In an optional implementation, constructing a target control equation based on the inventory and multiple flow rates also includes constructing a water resource supply control equation:

[0194]

[0195] in, is the amount of available water resources, is the ratio of available water resources.

[0196] In an embodiment of the present invention, constructing the production water demand, living water demand and water resources supply control equations has the following benefits: it can comprehensively and dynamically simulate the relationship between human production and life and water resources. The production water demand equation reflects the dynamic adjustment of water demand to industrial structure transformation and economic conditions, and captures the role of industrial upgrading in promoting water resource conservation. The living water demand equation combines economic growth, community sensitivity and scientific and technological level to show the dynamic evolution characteristics of residents' water use patterns in changing social, economic and technological conditions. In addition, the water resources supply equation can reflect the fluctuations in water resource availability and its restrictive regulation on water demand. This system helps to reveal the mutual feedback mechanism between water resources and socio-economic systems, and provides a scientific basis for formulating regional water resources sustainable management policies. In an optional implementation, step 130 may specifically include:

[0197] Based on the causal loop diagram and the relationship between stocks and flows, multiple key indicators corresponding to each subsystem are selected from multiple resilience factors, stocks and flows;

[0198] Based on multiple key indicators (such as annual precipitation and population density in Table 1), an initial drought resilience assessment index system is constructed, see Table 1 for example.

[0199] Table 1: Regional drought resilience indicator system

[0200] Indicator layer unit Data Source property Annual precipitation mm "H Province Statistical Yearbook" Positive Population Density People / km² "H Province Statistical Yearbook" Negative Domestic water consumption <![CDATA[hundred million m 3 > H Province Water Resources Bulletin Negative Production water <![CDATA[hundred million m 3 > H Province Water Resources Bulletin Negative Per capita available water resources m3 H Province Water Resources Bulletin Positive Reservoir capacity <![CDATA[hundred million m 3 > H Province Water Resources Bulletin Positive Water supply network density km / km² China Urban Construction Statistical Yearbook Positive GDP per capita Yuan "H Province Statistical Yearbook" Positive Investment in water conservancy facilities Ten thousand yuan "H Province Statistical Yearbook" Positive Community Sensitivity / Search Engines Positive Technology level / "H Province Statistical Yearbook" Positive

[0201] In an optional embodiment, in step 140, based on a preset simulation scenario, a regional drought resilience system dynamics model is used to perform scenario simulation and output corresponding dynamic change results. Prior to this, it includes: constructing two simulation scenarios, considering two different socioeconomic pathways (Shared Socioeconomic Pathways, SSPs) and corresponding emission scenarios (Representative Concentration Pathways, RCPs), and constructing two scenarios, namely, a low emission scenario of SSP2-4.5 and a high emission scenario of SSP5-8.5.

[0202] The first scenario: SSP2-4.5 low-emission scenario. This scenario assumes that future socio-economic development is relatively sustainable and greenhouse gas emissions are under certain control. Specifically, the model input uses the precipitation forecast data under the low-emission scenario corresponding to the SSP2-4.5 scenario, and assumes a relatively low GDP growth rate. This means that under this scenario, the impact of future climate change is relatively mild, and socio-economic development is also relatively stable.

[0203] The second scenario: SSP5-8.5 high emission scenario. This scenario assumes that future socio-economic development is dominated by fossil fuels and greenhouse gas emissions continue to grow. The model input uses the precipitation forecast data under the high emission scenario corresponding to the SSP5-8.5 scenario, and assumes a relatively high GDP growth rate. This means that under this scenario, the impact of future climate change will be more severe, the risk of drought may increase, and the rapid socio-economic development may also put greater pressure on water resources.

[0204] In an optional implementation, step 8) performs model setting and model verification on the initial regional drought resilience system dynamics model to obtain the regional drought resilience system dynamics model.

[0205] The model setting may include system constitutive relationship estimation, system initial condition setting and system parameter setting. The system constitutive relationship estimation is shown in Table 2, the system initial condition setting is shown in Table 3, and the system parameter setting is shown in Table 4.

[0206] According to the complexity of the constitutive relations, they are divided into two categories. Type I means that the constitutive relations can be directly obtained through the definition of variables. Type II means that the constitutive relations should be obtained through data analysis, literature of related studies, or appropriately estimated based on consensus principles (such as logistic growth), as shown in Table 2.

[0207] Table 2: System constitutive relationship estimation

[0208]

[0209] The initial conditions of the variables are obtained based on the observed data, i.e. the first value of the observed data. The initial conditions of water resources are unnecessary because of the assumptions in the hydrological equations.

[0210] Table 3: System initial condition settings

[0211] variable describe unit value <![CDATA[Pop S ]]> Population size 10,000 people 771.3 <![CDATA[GDP S ]]> gross domestic product 100 million yuan 2540.416 <![CDATA[CS S ]]> Community Sensitivity / 2995 <![CDATA[STL S ]]> Technology level / 0.1987 <![CDATA[WR S ]]> Water resources <![CDATA[hundred million m 3 > 90.65 <![CDATA[DWC S ]]> Domestic water consumption <![CDATA[hundred million m 3 > 4.19009 <![CDATA[PWC S ]]> Production water consumption <![CDATA[hundred million m 3 > 35.04

[0212] Some parameters, such as the runoff coefficient, were estimated from precipitation and water resources observation data. Some parameters, such as the proportion of available water resources, were set with reference to previous research work. Other model parameters were obtained through trial and error, as shown in Table 4.

[0213] Table 4: System parameter settings

[0214] variable describe unit value <![CDATA[AF E Pop ]]> Age influence factor / 0.1545 <![CDATA[RC C CS ]]> Relative coefficient / 0.022 <![CDATA[MCIF C CS ]]> Macro-environmental impact coefficient / 0.039 <![CDATA[CCS C CS ]]> Critical Community Sensitivity / 3000 <![CDATA[GR R GDP ]]> GDP Growth Rate / 0.149 <![CDATA[OI C STL ]]> Technology Investment Output Index / 0.282 <![CDATA[PC C STL ]]> Technology level growth coefficient / 0.0006 <![CDATA[minWDGDP C WR ]]> Minimum water demand per 10,000 yuan of GDP <![CDATA[m 3 ]]> 10 <![CDATA[AFIC C Pop ]]> Age structure factor influence coefficient / 0.01 <![CDATA[PIFIC C Pop ]]> Policy impact factor / 0.0482 <![CDATA[CSFIC C Pop ]]> Community sensitivity impact coefficient, affecting population / 0.0273 <![CDATA[RC C WR ]]> Runoff coefficient / 0.5893 <![CDATA[PEWR C WR ]]> Ratio of available water resources / 0.4 <![CDATA[minDWI A WR ]]> Minimum domestic water intensity m³ / (person*year) 43.8 <![CDATA[maxDWI A WR ]]> Maximum domestic water intensity m³ / (person*year) 65.7 <![CDATA[CSIC C DW ]]> Community sensitivity impact coefficient, affecting domestic water use / 0.00052 <![CDATA[STLIC C DW ]]> The influence coefficient of scientific and technological level, affecting domestic water use / 0.99 <![CDATA[GDPIC C DW ]]> GDP impact coefficient, affecting domestic water use / 0.000059 <![CDATA[CSIC C PW ]]> Community sensitivity impact coefficient, affecting production water use / 0.0001 <![CDATA[GDPIC C PW ]]> GDP impact coefficient, affecting production water use / 0.0002

[0215] As for model verification, two methods, dimensional consistency test and historical data test, were used to verify the model. The dimensional consistency test was implemented by using the "unit check" function in the system dynamics software anylogic. The test results showed that the dimensions of each equation in the model were consistent and the model passed the test. In terms of historical data testing, due to the relatively short historical observation data sequence, seven state variables (GDP, community sensitivity, technological level, population, production water consumption, domestic water consumption, and water resources) were selected. The root mean square error and correlation coefficient of the historical measured values ​​and simulated values ​​of these variables were calculated for model verification to evaluate the accuracy and reliability of the model, such as Figure 4 , Figure 5 , Figure 6 , Figure 7 , Figure 8 , Fig. 9 , Fig.10 and Table 5.

[0216] Table 5: Performance metrics for evaluating system dynamics model performance

[0217] variable Water resources Population GDP Technology level Community Sensitivity Production water consumption Domestic water consumption NSE 0.788 0.926 0.980 0.992 0.846 0.966 0.747 MAPE 10.07% 3.5% 5.04% 2.34% 9.81% 3.53% 5.55%

[0218] In an optional embodiment, the regional drought resilience simulation analysis method may also include a regional drought resilience grade classification.

[0219] The natural breakpoints are used to classify the comprehensive scores of drought resilience in the sorted regions to determine different levels of drought resilience, such as high resilience, medium resilience, and low resilience. The natural breakpoint method is a classification method based on the distribution characteristics of data values. Its goal is to maximize the score differences between different levels by minimizing the within-group sum of squared deviations (WGSS) and to ensure that the score differences within the same level are minimized. The specific steps are as follows:

[0220] (1) Data preparation: Arrange the comprehensive drought resilience scores of all cities in ascending order.

[0221] (2) Iterative optimization: The algorithm traverses all possible breakpoint combinations. For each breakpoint combination, the corresponding WGSS value is calculated. The calculation formula of WGSS is as follows:

[0222]

[0223] Where i represents the level index, j represents the index of the data point within the level, and xᵢⱼ represents the value of the jth data point in the i-th level. Represents the average value of the data points in the i-th level.

[0224] 3) Determine the best breakpoint: Select the breakpoint combination with the smallest WGSS value as the final breakpoint.

[0225] (4) Classification: Cities are divided into different resilience levels based on the final breakpoints.

[0226] The classification results can be seen in Table 6.

[0227] Table 6: Regional drought resilience classification

[0228] Low toughness Medium toughness Higher toughness High toughness r<0.216 0.216<r<0.404 0.404<r<0.534 0.534<r<0.728

[0229] For example, the drought resilience level of the target area can be found in Fig.11 .

[0230] Combination Fig.12, and sorted out the regional drought resilience simulation analysis methods. The first step is to obtain socio-economic data, precipitation data and water use data; the next step is to construct a system dynamics model (that is, the regional drought resilience system dynamics model mentioned above). During the construction process, the first step is to determine the system boundary and analyze the structure, so as to divide the subsystem into five subsystems. The five subsystems involve population, economy, technology, community sensitivity and water resources (of course, the resilience assessment index system can be screened from the above data based on these five subsystems). The second step is to use the information flow method to analyze the causal relationship of the resilience system after obtaining the five subsystems. The third step is to establish the control equation for the stock flow analysis of the resilience system. The fourth step is to set up and calibrate the model. In the model setting and calibration, exogenous variables, initial conditions, model parameters and constitutive relationships can be set. The fifth step is to verify the model. In the model verification, dimensional consistency tests and historical data tests can be performed. Then, scenario settings are performed. The model simulates the dynamic changes of resilience factors under different scenarios and outputs drought resilience results. Next, on the basis of the resilience assessment index system, the CRITIC-TOPSIS method is used to calculate the drought resilience index. Finally, the natural breakpoint method is used to classify the drought resilience levels.

[0231] like Fig.13 As shown, the present invention also provides a regional drought resilience simulation and analysis device, comprising:

[0232] An acquisition module 1310 is used to acquire observation data of a target drought resilience system corresponding to a target area; the observation data at least includes socio-economic data, precipitation data, and water use data;

[0233] The first construction module 1320 is used to construct a regional drought resilience system dynamics model corresponding to the target drought resilience system based on the preset system boundary and observation data; the regional drought resilience system dynamics model at least includes a causal relationship loop diagram and a control equation; the causal relationship loop diagram is used to describe the interaction between multiple resilience elements in the regional drought resilience system; the target control equation is used to describe the relationship between the stock and flow in the regional drought resilience system; multiple resilience elements are identified based on the observation data; the stock and flow are identified based on the causal relationship diagram;

[0234] The second construction module 1330 is used to construct an initial drought resilience assessment indicator system based on the causal relationship loop diagram and the relationship between the stock and the flow;

[0235] The simulation module 1340 is used to perform scenario simulation based on a preset simulation scenario using a regional drought resilience system dynamics model and output corresponding dynamic change results;

[0236] An adjustment module 1350 is used to adjust the initial drought resilience assessment index system based on the dynamic change result to obtain a target drought resilience assessment index system;

[0237] The calculation module 1360 is used to calculate the resilience index of the target drought resilience assessment index system.

[0238] Although the present invention is described herein in conjunction with various embodiments, in the process of implementing the claimed invention, those skilled in the art may understand and implement other variations of the disclosed embodiments by viewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "one" or "an" does not exclude multiple situations. A single processor or other unit may implement several functions listed in the claims. Certain measures are recorded in mutually different dependent claims, but this does not mean that these measures cannot be combined to produce good results. Although the present invention has been described in conjunction with specific features and embodiments thereof, it is obvious that various modifications and combinations may be made to them without departing from the spirit and scope of the present invention. Accordingly, this specification and the drawings are merely exemplary illustrations of the present invention as defined by the appended claims, and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present invention. Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A regional drought resilience simulation analysis method, characterized in that: include: Obtain observation data of the target drought resilience system corresponding to the target area; The observation data at least include socio-economic data, precipitation data and water use data; Based on the preset system boundary and the observation data, a regional drought resilience system dynamics model corresponding to the target drought resilience system is constructed; the regional drought resilience system dynamics model at least includes a causal relationship loop diagram and a control equation; the causal relationship loop diagram is used to describe the interaction between multiple resilience elements in the regional drought resilience system; the control equation is used to describe the relationship between the stock and flow in the regional drought resilience system; The plurality of resilience factors are identified based on the observation data; The stock and the flow are obtained based on the cause-effect relationship diagram; Based on the causal relationship loop diagram and the relationship between the stock and flow, an initial drought resilience assessment index system is constructed; Based on the preset simulation scenario, the regional drought resilience system dynamics model is used to perform scenario simulation and output corresponding dynamic change results; Adjusting the initial drought resilience assessment index system based on the dynamic change result to obtain a target drought resilience assessment index system; Calculate the resilience index of the target drought resilience assessment index system.

2. The regional drought resilience simulation analysis method according to claim 1, characterized in that: The constructing of a regional drought resilience system dynamics model corresponding to the target drought resilience system based on the preset system boundary and the observation data includes: Determining the preset system boundary as the system boundary of the target drought resilience system; Structural division of the target drought resilience system is performed to obtain multiple subsystems; the subsystems include a water resources subsystem, a population subsystem, an economic subsystem, a science and technology subsystem, and a social subsystem; Analyzing and identifying the observation data to obtain the plurality of resilience factors; Based on the multiple resilience factors, the causal relationship diagram is constructed by using the Liang-Kleeman information flow analysis method; Based on the cause-effect relationship diagram, system components of each subsystem are identified, wherein the system components include the stock and the flow; the flow is an influencing factor of the stock; constructing a control equation between the inventory and the flow; Based on the cause-effect relationship diagram and the control equation, an initial regional drought resilience system dynamics model is constructed; The initial regional drought resilience system dynamics model is set up and verified to obtain the regional drought resilience system dynamics model.

3. The regional drought resilience simulation analysis method according to claim 2, characterized in that: Based on the multiple resilience factors, the causal relationship diagram is constructed by using the Liang-Kleeman information flow analysis method, including: Analyzing and obtaining a plurality of variables from the plurality of resilience factors; Construct a vector autoregression model of the first variable and the second variable in the multiple variables: Wherein, the first variable and the second variable are any two variables among a plurality of variables; Y(t) represents the value of the first variable Y at time t; X(t) represents the value of the second variable X at time t; α and α' are constant terms; βi, γj, β'i, γ'j are preset model coefficients; ε(t) and ε'(t) are error terms; Based on the vector autoregression model, using the Liang-Klieman information flow formula, a first information flow of the first variable to the second variable is calculated, and a second information flow of the second variable to the first variable is calculated; comparing the sizes of the first information flow and the second information flow; If the first information flow is larger than the second information flow by a first preset threshold, a first causal relationship is marked; the first causal relationship indicates that the first variable has a causal influence on the second variable, and the causal influence direction is from the first variable to the second variable; If the second information flow is greater than the first information flow by a second preset threshold, a second causal relationship is marked; the second causal relationship indicates that the second variable has a causal influence on the first variable, and the causal influence direction is from the second variable to the first variable; If the difference between the first information flow and the second information flow is less than a preset bidirectional causal determination threshold, and both the first information flow and the second information flow are greater than a third preset threshold, then a bidirectional causal relationship is marked; the bidirectional causal relationship indicates that the first variable and the second variable are mutually causal; If the difference between the first information flow and the second information flow is greater than or equal to the bidirectional causal determination threshold, no causal relationship is marked; If both the first information flow and the second information flow are less than the third preset threshold, no causal relationship is marked; wherein the third preset threshold is less than the preset bidirectional causal determination threshold, the preset bidirectional causal determination threshold is less than or equal to the first preset threshold, and the preset bidirectional causal determination threshold is less than or equal to the second preset threshold; When all variables are analyzed, the causal relationship diagram is obtained.

4. The regional drought resilience simulation analysis method according to claim 3, characterized in that: The stock at least includes the gross domestic product associated with the economic subsystem; the flow at least includes the GDP growth rate affecting the gross domestic product; The constructing of a control equation between the inventory and the flow rate includes: Based on the gross domestic product and the GDP growth rate, the socioeconomic control equation is constructed: Among them, GDP S is the gross domestic product, is the GDP growth rate, GDP S0 is the initial value of GDP.

5. The regional drought resilience simulation analysis method according to claim 4, characterized in that: The stock also includes community sensitivity associated with the social subsystem; the flow also includes sensitive indicators and weighting factors that affect the community sensitivity; the weighting factors are used to quantify the relative importance of water resources and the gross domestic product to the community sensitivity; The constructing of a control equation between the inventory and the flow rate includes: Based on the community sensitivity, the sensitivity index and the weighting factor, a community sensitivity control equation is constructed: Among them, CS S for community sensitivity; It is a simplified sensitivity index, which reflects the impact of the macro environment on community sensitivity; is the weighting factor; WR S for water resources; is the critical community sensitivity value.

6. The regional drought resilience simulation analysis method according to claim 5, characterized in that: The stock also includes the technology level associated with the technology subsystem; the flow also includes the technology level growth rate, technology investment proportion and technology investment output index that affect the technology level; The constructing of a control equation between the inventory and the flow rate includes: Based on the technology level, the technology level growth rate, the technology investment proportion and the output index of technology investment, the technology level control equation is constructed: in, STL S Indicates the level of science and technology; Indicates the growth rate of the level of science and technology; It indicates the growth coefficient of the scientific and technological level; Indicates the proportion of investment in science and technology; represents the output index of the above-mentioned scientific and technological investment; f STL-PI is the constitutive relation that describes the relationship between the GDP and Feedback between It is a factor affecting community sensitivity.

7. Control equation The regional drought resilience simulation analysis method according to claim 6 is characterized in that: The stock also includes the population number associated with the population subsystem; the flow also includes the population growth rate, the natural population growth rate and the water resources carrying capacity; The constructing of a control equation between the inventory and the flow rate includes: Based on the population size, the population growth rate, the natural population growth rate and the water resources carrying capacity, a population size control equation is constructed: in, Pop S is the number of said population; is the population growth rate; is the natural growth rate of the population; is the influencing factor of education level; is the influencing factor of age structure; is the factor affecting community sensitivity on population growth rate; is the water resources carrying capacity; is the amount of available water resources; is the per capita comprehensive water consumption; Pop-NGR 、f Pop-WRCC They all represent the corresponding constitutive relations.

8. Control equation The regional drought resilience simulation analysis method according to claim 7 is characterized in that: The stock also includes the water resource quantity associated with the water resource subsystem; the flow also includes the precipitation that affects the water resource quantity; The constructing of a control equation between the inventory and the flow rate includes: Based on the water resources and the precipitation, a water resources control equation is constructed: Among them, WR S is the amount of water resources; is the precipitation amount; is the runoff coefficient; The constructing of the control equation between the stock and the flow also includes: constructing a control equation for domestic water demand: in, is the domestic water intensity; Minimum domestic water consumption; is the maximum domestic water consumption; f WR-GDP (GDP S ), f WR-DA (CS S ), f WR-STL (STL S ) are the corresponding constitutive relations; The constructing of the control equation between the stock and the flow also includes: constructing a water resource supply control equation: in, is the amount of available water resources, is the ratio of available water resources.

9. Control equation The regional drought resilience simulation analysis method according to claim 6 is characterized in that: The initial drought resilience assessment index system is constructed based on the causal relationship loop diagram and the relationship between the stock and flow, including: Based on the causal relationship loop diagram and the relationship between the stock and the flow, select a plurality of key indicators corresponding to each subsystem from the plurality of resilience factors, the stock and the flow; Based on the multiple key indicators, the initial drought resilience assessment index system is constructed.

10. A regional drought resilience simulation and analysis device, characterized in that: include: An acquisition module is used to obtain observation data of a target drought resilience system corresponding to a target area; The observation data at least include socio-economic data, precipitation data and water use data; A first construction module is used to construct a regional drought resilience system dynamics model corresponding to the target drought resilience system based on a preset system boundary and the observation data; the regional drought resilience system dynamics model at least includes a causal relationship loop diagram and a control equation; the causal relationship loop diagram is used to describe the interaction between multiple resilience elements in the regional drought resilience system; the control equation is used to describe the relationship between the stock and flow in the regional drought resilience system; The plurality of resilience factors are identified based on the observation data; The stock and the flow are obtained based on the cause-effect relationship diagram; A second construction module is used to construct an initial drought resilience assessment indicator system based on the causal relationship loop diagram and the relationship between the stock and flow; A simulation module, for performing scenario simulation based on a preset simulation scenario using the regional drought resilience system dynamics model and outputting corresponding dynamic change results; An adjustment module, used for adjusting the initial drought resilience assessment index system based on the dynamic change result to obtain a target drought resilience assessment index system; A calculation module is used to calculate the resilience index of the target drought resilience assessment index system.

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