Ecological hydrological benign relationship maintaining threshold defining method and system

Abstract

The invention discloses an ecological hydrological benign relationship maintenance threshold defining method and system, and relates to the technical field of ecological hydrology, and the method comprises the steps: obtaining multi-source data of a target drainage basin, and selecting a core decision variable; constructing a dual-objective optimization model comprising a hydrological objective function and an ecological objective function based on the core decision variable; constructing a plurality of constraint conditions based on the multi-source data; based on all constraint conditions, a multi-objective evolutionary algorithm is adopted to solve the dual-objective optimization model, and a Pareto optimal solution set is obtained; and determining a weight based on an expert scoring method, and selecting an optimal threshold value from the Pareto optimal solution set based on an ideal point method and a comprehensive satisfaction index. And collaborative optimization of ecological protection and economic development is realized, so that the stability and practicability of the threshold value under the climate change background are ensured.

Description

Technical Field

[0001] This invention relates to the field of ecohydrology, and more specifically to a method and system for defining thresholds for maintaining benign ecohydrological relationships. Background Technology

[0002] Semi-arid watersheds are extremely short of water resources and have a high sensitivity to the ecological environment. The dynamics of groundwater levels directly affect the composition, structure and function of grassland vegetation.

[0003] The following problems exist in the study of eco-hydrological thresholds in semi-arid watersheds: (1) Most studies only focus on the single boundary of ecological protection and ignore the multi-objective management needs of water resources systems; (2) There is a lack of overall consideration of the hydrological-ecological-economic system, making it difficult to reflect the complex feedback between subsystems; (3) Existing thresholds are mostly based on static analysis and do not adequately consider the adaptability to climate fluctuations and human activities; (4) The actual needs of economic development in pastoral areas have not been fully considered, making it difficult to implement management measures.

[0004] Therefore, how to achieve synergistic optimization between ecological protection and economic development, and thus ensure the stability and practicality of the threshold in the context of climate change, is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] In view of the above problems, this invention is proposed to provide a method and system for defining a threshold for maintaining a benign eco-hydrological relationship that overcomes or at least partially solves the above problems, thereby achieving synergistic optimization of ecological protection and economic development, and ensuring the stability and practicality of the threshold in the context of climate change.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] In a first aspect, embodiments of the present invention provide a method for defining a threshold for maintaining a benign eco-hydrological relationship, comprising: Acquire multi-source data of the target watershed and select the core decision variables; Based on the core decision variables, a dual-objective optimization model including hydrological objective functions and ecological objective functions is constructed; Multiple constraints are constructed based on the multi-source data; Based on all the aforementioned constraints, a multi-objective evolutionary algorithm is used to solve the bi-objective optimization model to obtain the Pareto optimal solution set. The weights are determined based on expert scoring, and the optimal threshold is selected from the Pareto optimal solution set based on the ideal point method and the comprehensive satisfaction index.

[0008] In another embodiment, the multi-source data includes: meteorological data, hydrological data, ecological data, and economic data; The average groundwater level depth during the growing season of the target watershed is obtained based on the hydrological data and used as the core decision variable.

[0009] In another embodiment, the method for constructing the dual-objective optimization model is as follows: The absolute value of the difference between the average groundwater level depth during the growing season of the watershed and the ideal depth is used as the depth deviation; Minimize the burial depth deviation as the hydrological objective function; Based on the average groundwater level depth during the growing season of the watershed and the ecological data, the vegetation coverage of the target watershed is obtained. Maximizing the vegetation coverage is taken as the ecological objective function; The bi-objective optimization model is composed of the hydrological objective function and the ecological objective function.

[0010] In another embodiment, the method for determining the ideal burial depth is as follows: Obtain the transpiration rate and photosynthetic rate of plants at different groundwater levels in the target watershed; Water use efficiency is defined as the ratio of the photosynthetic rate to the plant transpiration rate. Plot the relationship curve between water use efficiency and groundwater level depth; Based on the relationship curve, the groundwater level depth corresponding to the maximum water use efficiency is selected as the ideal depth.

[0011] In another embodiment, the constraints include: water balance constraints, ecological protection constraints, socio-economic constraints, and system stability constraints; Water balance constraints are constructed based on the meteorological data. Based on the aforementioned ecological data, the minimum vegetation cover required to maintain ecosystem stability is determined. Ecological protection constraints are constructed based on the vegetation coverage and the minimum vegetation coverage. Socioeconomic constraints are constructed based on the aforementioned economic data; System stability constraints are constructed based on the meteorological data and stability thresholds.

[0012] In another embodiment, the Pareto optimal solution set is obtained as follows: The multi-objective optimization model is solved by using a non-dominated sorting genetic algorithm with an elitist strategy, combined with all the aforementioned constraints. By running the solution set independently multiple times to ensure its stability, the Pareto optimal solution set, which represents the trade-off between different objectives, is obtained.

[0013] In another embodiment, the optimal threshold acquisition method is as follows: Based on the Pareto optimal solution set, ideal points and negative ideal points are selected that enable the bi-objective optimization model to achieve optimal and worst values, respectively. The objective function value of each solution is obtained based on the Pareto optimal solution set, and together they form a decision matrix; The comprehensive satisfaction index is obtained based on the distances between the decision matrix and the ideal point and the negative ideal point, respectively. Based on the Pareto optimal solution set, the solution with the largest comprehensive satisfaction index is selected as the final recommendation threshold scheme; The optimal threshold is defined based on the decision variable corresponding to the recommended threshold scheme.

[0014] In another embodiment, the method for obtaining the ideal point and the negative ideal point is as follows: Based on the Pareto optimal solution set, the minimum value of the hydrological objective function is selected as the optimal value of the hydrological objective. The minimum value of the negative value of the ecological objective function is selected as the optimal value of the ecological objective. The ideal point is formed based on the optimal values ​​of the hydrological and ecological objectives. Based on the Pareto optimal solution set, the maximum value of the hydrological objective function is selected as the worst value of the hydrological objective. The maximum negative value of the ecological objective function is selected as the worst value of the ecological objective. The negative ideal point is formed based on the worst values ​​of the hydrological and ecological objectives.

[0015] In another embodiment, the method for obtaining the comprehensive satisfaction index is as follows: Based on the decision matrix, normalization processing is performed to obtain the target normalized value of the objective function value corresponding to each solution; The relevant weights for each objective function value are determined based on expert scoring. The weighted value is obtained by combining the relevant weights with the corresponding target normalized value; Based on the distances between the weighted value and the ideal point and the negative ideal point, the first distance and the second distance are obtained accordingly; The comprehensive satisfaction index corresponding to each solution is obtained based on the first distance and the second distance.

[0016] Secondly, embodiments of the present invention provide a threshold definition system for maintaining benign eco-hydrological relationships, comprising: a data acquisition module, a model building module, a constraint building module, an optimal solution set acquisition module, and an optimal threshold output module; The data acquisition module is used to acquire multi-source data of the target watershed and select the core decision variables; The model building module is used to construct a bi-objective optimization model, including a hydrological objective function and an ecological objective function, based on the core decision variables. The constraint construction module is used to construct multiple constraint conditions based on the multi-source data; The optimal solution set acquisition module is used to solve the bi-objective optimization model based on all the constraints using a multi-objective evolutionary algorithm to obtain the Pareto optimal solution set. The optimal threshold output module is used to determine the weights based on the expert scoring method and to select the optimal threshold from the Pareto optimal solution set based on the ideal point method and the comprehensive satisfaction index.

[0017] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a method and system for defining the threshold for maintaining a benign relationship between eco-hydrology and water resources. By coupling hydrological, ecological and economic systems to construct a dual-objective optimization model and introducing multiple types of constraints, it fundamentally overcomes the limitations of existing research that has a single objective and lacks systematic trade-offs. Furthermore, it uses a multi-objective evolutionary algorithm to obtain the Pareto optimal solution set, and combines the ideal point method and expert weights for decision-making. This ensures that the final defined threshold not only scientifically represents the trade-off relationship between different objectives, but also has good climate adaptability and management practicality, thus providing a stable, reliable and operable decision-making basis for water resource management and ecological protection in semi-arid watersheds. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of a method for defining the threshold for maintaining a benign eco-hydrological relationship, provided in an embodiment of the present invention.

[0020] Figure 2 This is a schematic diagram of the process for constructing a dual-objective optimization model provided in an embodiment of the present invention.

[0021] Figure 3 This is a schematic diagram of a threshold definition system for maintaining a benign eco-hydrological relationship, provided in an embodiment of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Example 1 To address the issues of existing threshold definition methods being singular in their objectives and lacking a systematic trade-off, such as... Figure 1 As shown, this invention discloses a method for defining the threshold for maintaining a benign eco-hydrological relationship, including the following steps. For ease of description, these steps are numbered S1 to S5, but these numbers are not used to limit the sequential relationship between the various steps of this invention: S1 acquires multi-source data of the target watershed and selects the core decision variables.

[0024] Furthermore, multi-source data includes: meteorological data, hydrological data, ecological data, and economic data; The average groundwater level depth D during the growing season of the target watershed is obtained from hydrological data and used as the core decision variable.

[0025] Furthermore, the meteorological data comes from observations at meteorological stations and includes: monthly precipitation, temperature, and evaporation; Hydrological data comes from the local monitoring well network, including: monthly groundwater level monitoring data, used to calculate the average groundwater level depth D during the growing season of the watershed; Ecological data comes from MODIS series satellite data (spatial resolution 500m, temporal resolution 16 days): monthly vegetation cover (VFC) retrieved by remote sensing, with the maximum value extracted during the growing season; The economic data comes from statistical yearbooks and field surveys, including the total annual output value of the livestock industry and the number of livestock on hand.

[0026] S2 constructs a bi-objective optimization model based on core decision variables, including hydrological and ecological objective functions.

[0027] Furthermore, such as Figure 2 As shown, the method for constructing the dual-objective optimization model is as follows: The depth deviation is based on the absolute value of the difference between the average groundwater level depth during the growing season and the ideal depth. Minimizing the burial depth deviation is taken as the hydrological objective function; Based on the average groundwater level depth during the growing season and ecological data, the vegetation cover of the target watershed is obtained. Maximizing vegetation cover is used as the ecological objective function; The optimization model is based on a dual-objective model composed of hydrological and ecological objective functions.

[0028] Furthermore, the hydrological objective function MinF1 is: MinF1 = Min|D-D0|; Where D0 represents the ideal burial depth.

[0029] Furthermore, the method for determining the ideal burial depth D0 is as follows: To obtain plant transpiration and photosynthetic rates at different groundwater levels in the target watershed; Water use efficiency (WUE) is calculated as the ratio of photosynthetic rate to plant transpiration rate. Plot the relationship curve between water use efficiency and groundwater level depth; Based on the principle of optimal plant water use efficiency, the groundwater level depth corresponding to the maximum water use efficiency is selected from the relationship curve as the ideal depth D0.

[0030] Furthermore, the ecological objective function Max F2: Max F2 = Max VFC; VFC represents vegetation coverage.

[0031] Furthermore, VFC calculates using the established groundwater level-vegetation response model: VFC = a × exp(-b × D) + c; Where a, b, and c all represent parameters, which are determined by fitting historical monitoring data using the nonlinear least squares method.

[0032] S3 constructs multiple constraints based on multi-source data.

[0033] Furthermore, the constraints include: water balance constraints, ecological protection constraints, socio-economic constraints, and system stability constraints; Water balance constraints are constructed based on meteorological data; Based on ecological data, determine the minimum vegetation cover required to maintain ecosystem stability; Ecological protection constraints are constructed based on vegetation coverage and minimum vegetation coverage. Constructing socioeconomic constraints based on economic data; System stability constraints are constructed based on meteorological data and stability thresholds.

[0034] Furthermore, the water balance constraints are specifically as follows: P+Q in -ET-Q out -Q p =ΔS±ε; Where P represents precipitation, obtained based on meteorological data; Q inIndicates the lateral inflow of groundwater; ET represents evapotranspiration, obtained using MODIS remote sensing evapotranspiration products; Q out Indicates the lateral outflow of groundwater; Q p ΔS represents the amount of groundwater extracted, obtained based on water usage statistics and field surveys; ΔS represents the change in groundwater reserves, calculated using groundwater dynamics methods; ε represents the allowable error of equilibrium, determined based on data accuracy.

[0035] Furthermore, the lateral inflow of groundwater Q in Calculated using Darcy's law: Q in =K×i×A×t; Where K represents the permeability coefficient, i represents the hydraulic gradient, A represents the cross-sectional area of ​​the water passage, and t represents time; Lateral outflow of groundwater Q out With groundwater lateral inflow Q in The calculation method is the same.

[0036] Furthermore, the specific constraints on ecological protection are as follows: VFC ≥ VFC min , and |ΔVFC|≤ δ; Among them, VFC min The minimum vegetation cover is defined as the minimum vegetation cover required to maintain the stability of the grassland ecosystem. It is determined by taking a specific low quantile (25th percentile) of the vegetation cover time series based on long-term ecological monitoring data. ΔVFC represents the interannual variation of vegetation cover. δ represents the interannual variability threshold of vegetation cover, which is determined based on the standard deviation of the historical interannual variation series of vegetation cover.

[0037] Furthermore, the socioeconomic constraints are specifically as follows: I g ≥ I min And |ΔI g | ≤ σ; Among them, I g This represents total income from animal husbandry, calculated based on grassland yield and livestock market prices; I min This represents the minimum acceptable income level determined based on a survey of herders' livelihoods; ΔI g σ represents the change in total income from animal husbandry; σ represents the threshold for interannual income fluctuation, which is determined based on the fluctuation characteristics of historical income data.

[0038] Furthermore, the system stability constraints are specifically as follows: |ΔVFC / ΔP| ≤ K; Where ΔP represents the change in precipitation; K represents the stability threshold.

[0039] Furthermore, the stability threshold K is determined in the following way: Calculate the interannual variation of vegetation cover ΔVFC in historical years hist Interannual variation of precipitation ΔP hist The ratio, i.e., |ΔVFC hist / ΔP hist |, take a specific percentile (75th percentile) of the ratio sequence as the stability threshold K.

[0040] S4 uses a multi-objective evolutionary algorithm to solve the bi-objective optimization model based on all constraints, and obtains the Pareto optimal solution set.

[0041] Furthermore, the method for obtaining the Pareto optimal solution set is as follows: A non-dominated sorting genetic algorithm with an elitist strategy is used to solve the multi-objective optimization model, taking into account all constraints. By running the solution set independently multiple times, the stability of the solution set is ensured, and the Pareto optimal solution set, which represents the trade-off between different objectives, is obtained.

[0042] Furthermore, in this embodiment, during the process of solving the multi-objective optimization model, the population size is set to 100, the number of generations is 1000, the crossover probability is 0.9, and the mutation probability is 0.1.

[0043] S5 determines the weights based on expert scoring and selects the optimal threshold from the Pareto optimal solution set based on the ideal point method and the comprehensive satisfaction index.

[0044] Furthermore, the optimal threshold is obtained as follows: Based on the Pareto optimal solution set, ideal points and negative ideal points are selected to enable the bi-objective optimization model to achieve optimal and worst values, respectively. The objective function value of each solution is obtained based on the Pareto optimal solution set, and together they form the decision matrix. The overall satisfaction index is obtained based on the distances of the decision matrix to the ideal point and the negative ideal point, respectively. Based on the Pareto optimal solution set, the solution with the largest comprehensive satisfaction index is selected as the final recommendation threshold scheme; The optimal threshold is defined based on the decision variables corresponding to the recommended threshold scheme.

[0045] Furthermore, the methods for obtaining the ideal point and the negative ideal point are as follows: Based on the Pareto optimal solution set, the minimum value of the hydrological objective function is selected as the optimal value of the hydrological objective. The minimum value of the negative value of the ecological objective function is selected as the optimal value of the ecological objective. The ideal point is formed by combining the optimal values ​​of hydrological and ecological objectives; Based on the Pareto optimal solution set, the maximum value of the hydrological objective function is selected as the worst value of the hydrological objective. The maximum negative value of the ecological objective function is selected as the worst value of the ecological objective. The negative ideal point is formed by combining the worst values ​​of the hydrological and ecological objectives.

[0046] Furthermore, an ideal point is a theoretical point in the objective function space where each objective reaches its optimal value; for a minimization problem, the ideal point is formed by the minimum value of all solutions on the Pareto front: For the hydrological objective function value F1 = |D - D0|, the smaller the better; the optimal hydrological objective value is F1. It is the minimum value of the hydrological objective function F1 in the Pareto optimal solution set, i.e., F1 =min(F1); For the ecological objective function value F2 = VFC, the larger the better. In decision-making, it is usually transformed into a minimization problem, i.e., F2' = -VFC; the optimal ecological objective value is F2'. The minimum value of F2' is equivalent to the maximum value of VFC, i.e., F2'. =min(F2') = -max(VFC); Ideal point Z = (F1) , F2' ).

[0047] Furthermore, the negative ideal point, the opposite of the ideal point, refers to the point where each objective reaches its worst-case scenario. It represents the least desirable outcome. Worst hydrological target F 1nadir =max(F1); Worst value of ecological target F 2nadir =max(F2'); Negative ideal point Z nadir =(F 1nadir , F 2nadir ').

[0048] Furthermore, the method for obtaining the overall satisfaction index is as follows: Based on the decision matrix, normalization is performed to obtain the target normalized value of the objective function value corresponding to each solution; The relevant weights for each objective function value are determined based on expert scoring. The weighted value is obtained by combining the relevant weights with the corresponding target normalized value; Based on the distances between the weighted values ​​and the ideal point and the negative ideal point, the first distance and the second distance are obtained accordingly; The comprehensive satisfaction index corresponding to each solution is obtained based on the first distance and the second distance.

[0049] Furthermore, the comprehensive satisfaction index is used to quantify how close each Pareto optimal solution is to the "ideal solution" and how far it is from the "worst solution," and is determined using the ranking method for approximating the ideal solution.

[0050] Furthermore, the Pareto optimal solution set is considered as the schemes to be decided, and the objective function values ​​(F1, F2) of each scheme constitute the decision matrix F: Suppose there are m candidate solutions and n = 2 objectives, then the decision matrix F is: ; in, , Write it as an explicit matrix: ; To eliminate the influence of different objective dimensions, the decision matrix is ​​normalized to obtain the target normalized value of the objective function value corresponding to each solution; For the j-th objective function value F of the i-th solution ij Its normalized value r ij for: ; in, Let m represent the square of the j-th objective function value of the k-th solution, and m represent the total number of candidate solutions in the Pareto optimal solution set; Let represent the sum of squares of the j-th objective function values ​​of all solutions.

[0051] Considering that different objectives may have different levels of importance, an expert scoring method is used to assign a weight w to each objective. j The weight of the hydrological objective is w1, the weight of the ecological objective is w2, and w1+w2=1.

[0052] Calculate the weighted normalized evaluation value, or weight value, for each solution: v ij =w j ×r ij .

[0053] Furthermore, calculate the weighted value v for each solution. ij The first distance S to the ideal point i : ; in, , representing the component of the ideal point Z on the j-th objective, that is, the weighted normalized value v of the j-th objective among all candidate solutions. ij The minimum value; since the ecological objective function has been transformed into a minimization form when constructing the decision matrix, the ideal point is composed of the minimum values ​​of each objective for the two minimization objectives.

[0054] Calculate each weighted value v ij The second distance to the negative ideal point, Sinadir: Sinadir = sqrt(Σ(v ij -v jnadir ) 2 ); in, , representing the negative ideal point Z nadir The weighted normalized value on the j-th objective.

[0055] Furthermore, the overall satisfaction index (relative closeness) of each solution is calculated: C i =Sinadir / (Si +Sinadir); The value of Ci ranges from [0,1]. Ci=1 indicates that the solution is the ideal point, and Ci=0 indicates that the solution is the negative ideal point. The larger the value of Ci, the closer the solution is to the ideal point and the farther it is from the negative ideal point, and the higher the overall satisfaction index.

[0056] Furthermore, the Pareto solution with the largest overall satisfaction index Ci is selected from the Pareto optimal solution set as the final recommended threshold scheme; The optimal threshold is defined based on the decision variable (average groundwater level depth D during the growing season) corresponding to the recommended threshold scheme.

[0057] Furthermore, it also includes: verifying the effectiveness of the optimal threshold using historical data that was not involved in the modeling, and integrating the optimal threshold into the watershed management decision support system to establish a monitoring and early warning mechanism.

[0058] Furthermore, the effectiveness of the optimal threshold is verified by using historical data that was not involved in the modeling. Evaluate the performance of threshold schemes in terms of ecological protection, economic development, and system stability; The verified thresholds will be integrated into the watershed management decision support system to establish a corresponding monitoring and early warning mechanism.

[0059] Example 2 like Figure 3 As shown, based on the same inventive concept, this embodiment of the invention also provides a threshold definition system for maintaining benign eco-hydrological relationships, including: a data acquisition module, a model building module, a constraint building module, an optimal solution set acquisition module, and an optimal threshold output module; The data acquisition module is used to acquire multi-source data of the target watershed and select the core decision variables; The model building module is used to construct a bi-objective optimization model based on core decision variables, including hydrological objective functions and ecological objective functions; The constraint building module is used to construct multiple constraints based on multi-source data. The optimal solution set acquisition module is used to solve the bi-objective optimization model based on all constraints using a multi-objective evolutionary algorithm to obtain the Pareto optimal solution set. The optimal threshold output module is used to determine the weights based on the expert scoring method and select the optimal threshold from the Pareto optimal solution set based on the ideal point method and the comprehensive satisfaction index.

[0060] Furthermore, in this embodiment, the functional implementation methods of each functional module correspond one-to-one with the methods described above, and will not be repeated here.

[0061] Example 3 Based on the same inventive concept, the present invention also provides an electronic device, which includes a processor and a memory, wherein the memory stores instructions, characterized in that the instructions are loaded and executed by the processor to implement a method for defining the threshold for maintaining a benign eco-hydrological relationship as in Example 1.

[0062] Based on the same inventive concept, the present invention also provides a computer device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When the processor executes a program stored in the memory, it can implement a method for defining the threshold for maintaining a benign eco-hydrological relationship, as shown in Example 1.

[0063] The electronic device may include a processor, a communications interface, a memory, and a communication bus, wherein the processor, communications interface, and memory communicate with each other via the communication bus. The processor can invoke logical instructions in the memory to execute a method for defining the threshold for maintaining a benign eco-hydrological relationship, as described in Embodiment 1.

[0064] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0065] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0066] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for defining the threshold for maintaining a benign eco-hydrological relationship, characterized in that, include: Acquire multi-source data of the target watershed and select the core decision variables; Based on the core decision variables, a dual-objective optimization model including hydrological objective functions and ecological objective functions is constructed; Multiple constraints are constructed based on the multi-source data; Based on all the aforementioned constraints, a multi-objective evolutionary algorithm is used to solve the bi-objective optimization model to obtain the Pareto optimal solution set. The weights are determined based on expert scoring, and the optimal threshold is selected from the Pareto optimal solution set based on the ideal point method and the comprehensive satisfaction index.

2. The method for defining the threshold for maintaining a benign eco-hydrological relationship according to claim 1, characterized in that, The multi-source data includes: meteorological data, hydrological data, ecological data, and economic data; The average groundwater level depth during the growing season of the target watershed is obtained based on the hydrological data and used as the core decision variable.

3. The method for defining the threshold for maintaining a benign eco-hydrological relationship according to claim 2, characterized in that, The method for constructing the dual-objective optimization model is as follows: The absolute value of the difference between the average groundwater level depth during the growing season of the watershed and the ideal depth is used as the depth deviation; Minimize the burial depth deviation as the hydrological objective function; Based on the average groundwater level depth during the growing season of the watershed and the ecological data, the vegetation coverage of the target watershed is obtained. Maximizing the vegetation coverage is taken as the ecological objective function; The bi-objective optimization model is composed of the hydrological objective function and the ecological objective function.

4. The method for defining the threshold for maintaining a benign eco-hydrological relationship according to claim 3, characterized in that, The method for determining the ideal burial depth is as follows: Obtain the transpiration rate and photosynthetic rate of plants at different groundwater levels in the target watershed; Water use efficiency is defined as the ratio of the photosynthetic rate to the plant transpiration rate. Plot the relationship curve between water use efficiency and groundwater level depth; Based on the relationship curve, the groundwater level depth corresponding to the maximum water use efficiency is selected as the ideal depth.

5. The method for defining the threshold for maintaining a benign eco-hydrological relationship according to claim 3, characterized in that, The constraints include: water balance constraints, ecological protection constraints, socio-economic constraints, and system stability constraints. Water balance constraints are constructed based on the meteorological data. Based on the aforementioned ecological data, the minimum vegetation cover required to maintain ecosystem stability is determined. Ecological protection constraints are constructed based on the vegetation coverage and the minimum vegetation coverage. Socioeconomic constraints are constructed based on the aforementioned economic data; System stability constraints are constructed based on the meteorological data and stability thresholds.

6. The method for defining the threshold for maintaining a benign eco-hydrological relationship according to claim 5, characterized in that, The method for obtaining the optimal Pareto solution set is as follows: The multi-objective optimization model is solved by using a non-dominated sorting genetic algorithm with an elitist strategy, combined with all the aforementioned constraints. By running the solution set independently multiple times to ensure its stability, the Pareto optimal solution set, which represents the trade-off between different objectives, is obtained.

7. The method for defining the threshold for maintaining a benign eco-hydrological relationship according to claim 6, characterized in that, The method for obtaining the optimal threshold is as follows: Based on the Pareto optimal solution set, ideal points and negative ideal points are selected that enable the bi-objective optimization model to achieve optimal and worst values, respectively. The objective function value of each solution is obtained based on the Pareto optimal solution set, and together they form a decision matrix; The comprehensive satisfaction index is obtained based on the distances between the decision matrix and the ideal point and the negative ideal point, respectively. Based on the Pareto optimal solution set, the solution with the largest comprehensive satisfaction index is selected as the final recommendation threshold scheme; The optimal threshold is defined based on the decision variable corresponding to the recommended threshold scheme.

8. The method for defining the threshold for maintaining a benign eco-hydrological relationship according to claim 7, characterized in that, The method for obtaining the ideal point and the negative ideal point is as follows: Based on the Pareto optimal solution set, the minimum value of the hydrological objective function is selected as the optimal value of the hydrological objective. The minimum value of the negative value of the ecological objective function is selected as the optimal value of the ecological objective. The ideal point is formed based on the optimal values ​​of the hydrological and ecological objectives. Based on the Pareto optimal solution set, the maximum value of the hydrological objective function is selected as the worst value of the hydrological objective. The maximum negative value of the ecological objective function is selected as the worst value of the ecological objective. The negative ideal point is formed based on the worst values ​​of the hydrological and ecological objectives.

9. The method for defining the threshold for maintaining a benign eco-hydrological relationship according to claim 8, characterized in that, The method for obtaining the overall satisfaction index is as follows: Based on the decision matrix, normalization processing is performed to obtain the target normalized value of the objective function value corresponding to each solution; The relevant weights for each objective function value are determined based on expert scoring. The weighted value is obtained by combining the relevant weights with the corresponding target normalized value; Based on the distances between the weighted value and the ideal point and the negative ideal point, the first distance and the second distance are obtained accordingly; The comprehensive satisfaction index corresponding to each solution is obtained based on the first distance and the second distance.

10. A threshold definition system for maintaining benign eco-hydrological relationships, used to execute a threshold definition method for maintaining benign eco-hydrological relationships as described in any one of claims 1-9, characterized in that, include: The module includes a data acquisition module, a model building module, a constraint building module, an optimal solution set acquisition module, and an optimal threshold output module. The data acquisition module is used to acquire multi-source data of the target watershed and select the core decision variables; The model building module is used to construct a bi-objective optimization model, including a hydrological objective function and an ecological objective function, based on the core decision variables. The constraint construction module is used to construct multiple constraint conditions based on the multi-source data; The optimal solution set acquisition module is used to solve the bi-objective optimization model based on all the constraints using a multi-objective evolutionary algorithm to obtain the Pareto optimal solution set. The optimal threshold output module is used to determine the weights based on the expert scoring method and to select the optimal threshold from the Pareto optimal solution set based on the ideal point method and the comprehensive satisfaction index.

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