Optimization method and system for water resource allocation

Abstract

The invention discloses a water resource configuration optimization method, which comprises the following steps: by taking social benefits, economic benefits and ecological benefits as targets, constructing a target function of water resource optimization configuration, and determining constraint conditions of the target function; calculating parameters in the constructed target function based on an XGBoost algorithm, and then solving the calculated target function by adopting an NSGA-III optimization algorithm to obtain a non-inferior solution set; determining an equilibrium solution from the non-inferior solution set through a backstepping bargaining method, and taking the equilibrium solution as a final water resource allocation scheme; and comparing and analyzing the benefits of the water resource allocation schemes obtained by the objective function under different scenes so as to obtain the influence of different low water degrees on drainage basin water resource allocation. According to the method, uncertain parameter values can be taken in combination with actual conditions, the precision and efficiency of water resource allocation are improved, and the problems that an existing water resource allocation method is low in accuracy and low in efficiency are solved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of water conservancy and hydropower engineering, and particularly relates to a water resource allocation optimization method and system. BACKGROUND

[0002] The traditional water resource allocation method often relies on experience estimation or selection neglect in the face of the complexity and uncertainty of the water resource system, which makes the allocation result have large deviation and uncertainty. Therefore, it is of great significance to study the optimization method of water resource allocation for the sustainable development of regional ecology and economy.

[0003] At home and abroad, many scholars have carried out extensive and in-depth research on the model establishment of water resource optimization allocation. The water resource allocation optimization problem often obtains the optimal target value of the decision scheme by establishing the objective function and a series of constraints, and solving the decision variables. The research on water resource allocation takes the optimal scheduling of reservoirs as the starting point, and has experienced three important stages from the supply and demand balance allocation of water resources, the maximum economic benefit allocation, to the reasonable allocation for ecological benefits. For the uncertain parameter value in the water resource allocation, the existing researches mostly use stochastic programming, interval programming and the like to solve the problem, but cannot accurately reflect the actual situation of the region. Therefore, how to provide a water resource optimization allocation method which can effectively consider the uncertain parameter value in the water resource allocation process, reflect the influence of uncertain factors in the water resource optimization allocation system, comprehensively consider the regional social, economic and ecological benefits, and provide a reference basis for the sustainable utilization of water resources under the changing environment is an urgent problem to be solved. SUMMARY

[0004] The purpose of the present application is to solve the problems of low accuracy and low efficiency of the existing water resource allocation method by providing a water resource allocation optimization method which can effectively consider the uncertain parameter value in the water resource allocation process in combination with the actual situation, improve the accuracy and efficiency of water resource allocation.

[0005] To solve the above technical problems, the present application adopts the following technical scheme: A water resource allocation optimization method comprises the following steps: Step 1, constructing a target function of water resource optimization allocation with social benefit, economic benefit and ecological benefit as the target, and determining the constraint condition of the target function; Step 2, calculating the parameters in the target function constructed in step 1, and then solving the calculated target function by using NSGA-III optimization algorithm to obtain a non-inferior solution set; Step 3, determining an equilibrium solution from the non-inferior solution set by using the backtracking bargaining method as the final water resource allocation scheme; Step 4, comparative analysis of the benefits of water resources allocation schemes obtained by the objective function under different scenarios, to obtain the influence of different low water levels on the basin water resources allocation.

[0006] Further, in step 1, the objective function for constructing the optimal allocation of water resources in step 1 is: Design to maximize the satisfaction of regional water use as a social benefit goal, as shown in the following formula: (1) In formula (1), to study the water use satisfaction of the region; is the decision variable, representing the water use department p (%) when the water use department allocates the water quantity to the city in the study area; is the water demand of each water use department in each city in the study area; is the total number of water use departments; is the total number of cities in the study area; Design to maximize the total economic benefit of each water use department in the region as an economic benefit goal, as shown in the following formula: (2) In formula (2), is the water use economic benefit of the study area; is the water use benefit coefficient of the city ; Design to minimize the total sewage discharge of each water use department in the region as an ecological benefit goal, as shown in the following formula: (3) In formula (3), is the sewage discharge of the study area; is the sewage discharge coefficient of the city .

[0007] Further, the constraint condition determined by the objective function in step 2 is: 1) Available water constraint The total allocated water quantity cannot exceed the total available water quantity, as shown in the following formula: (4) In formula (4): is the available water quantity of the study area when the hydrological frequency is p (%); 2) Water demand constraint, The water quantity allocated to each department should not exceed its maximum expected water demand, as shown in the following formula: (5) In equation (5): Indicates water user department In the study area cities The minimum water demand, i.e. rigid water demand; 3) Nonnegativity constraint The amount of water allocated to each water-using department should be greater than zero, and the water demand of each water-using department should also be greater than zero, as shown in the following formula: (6) Furthermore, in step 2, the XGBoost algorithm is used to evaluate the water use efficiency coefficient in the economic benefits. And the wastewater discharge coefficient in ecological benefits The calculation is performed. Methods for calculating parameters using the XGBoost algorithm include: Data on water consumption, economic benefits, and wastewater discharge from industrial, agricultural, and residential sectors in the study area over the years were collected. Industrial, agricultural, and residential water consumption were used as independent variables, and economic benefit coefficients and wastewater discharge coefficients were used as dependent variables. The XGBoost algorithm was used for fitting and prediction to obtain the economic benefit coefficient and wastewater discharge coefficient under any decision variable in the water resource optimization allocation model. Finally, evaluation indicators were used to assess the calculation accuracy of the XGBoost algorithm.

[0008] Furthermore, in step 2, after calculating the parameters in the objective function, the NSGA-Ⅲ algorithm is used to solve the objective function to obtain the optimal Pareto front solution set among the three objectives of social, economic and ecological benefits in the optimal allocation of regional water resources.

[0009] Furthermore, the method for solving the objective function using the NSGA-Ⅲ algorithm is as follows: a. Initialize the population: Randomly generate a set of initial solutions, each solution representing a water resource allocation scheme, including the water allocation for industrial, agricultural, and domestic water use sectors; b. Non-dominated sorting: For each solution in the initial population, non-dominated sorting is performed based on the three objective function values ​​of social benefit, economic benefit and ecological benefit. c. Crowding degree calculation: For each non-dominated layer, calculate the crowding degree between solutions; d. Selection, crossover, and mutation: Select parent individuals based on non-dominated order and crowding, perform crossover on the selected parent individuals to generate offspring individuals, and perform mutation on the offspring individuals to increase the diversity of the population. e. Merge populations and reorder: Merge the parent population and the offspring population to form a new population, and re-perform non-dominated ordering and crowding calculation on the new population. f. Elite Preservation Strategy: Select the optimal non-dominated solution set from the new population as the population for the next generation of evolution; g. Iterative evolution: repeat steps d to f until a preset maximum number of iterations or a maximum number of function evaluations is reached; h. Obtaining a Pareto frontier solution set: the final non-dominated solution set is the optimal Pareto frontier solution set among the three targets of social, economic and ecological benefits in the optimal allocation of regional water resources.

[0010] Further, the method for determining the equilibrium solution in step 3 is: Step 4.1, respectively from the perspective of optimal social benefit, optimal economic benefit, optimal ecological benefit, all Pareto non-inferior solutions are sorted in order of benefit from high to low, and the Pareto solution with the highest benefit is the preferred water allocation scheme for this target; Step 4.2, respectively from the perspective of optimal social benefit, optimal economic benefit, optimal ecological benefit, consistently retreat from the preferred scheme to the scheme with lower and lower benefits, and after each retreat, check whether there is a Pareto solution that meets the current retreat level in terms of benefits on the three targets; if not, continue to retreat; when a Pareto solution is found that meets the current retreat level in terms of benefits on the three targets, stop retreating, and the solution is the equilibrium solution, which is the final water resources allocation scheme.

[0011] The embodiment of the present application also provides a system for implementing the above-mentioned optimization method of water resources allocation, comprising: A target function construction module is configured to construct a target function of water resources optimization allocation with social benefit, economic benefit and ecological benefit as targets, and determine the constraint conditions of the target function; A target function solving module is configured to calculate parameters in the target function constructed by the target function construction module based on an XGBoost algorithm, and then solve the calculated target function by using an NSGA-III optimization algorithm to obtain a non-inferior solution set; A water resources allocation scheme determination module is configured to determine an equilibrium solution from the non-inferior solution set by using a step-back bargaining method, as a final water resources allocation scheme; A water resources allocation scheme comparison module is configured to compare and analyze the benefits of the water resources allocation schemes obtained by the target function under different scenarios, so as to obtain the influence of different dryness degrees on the allocation of water resources in the basin.

[0012] An electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0013] A computer readable storage medium has a computer program stored thereon, and the computer program is executed by a processor to implement the steps of the above method.

[0014] Compared with the prior art, the beneficial effects of the present application are that the present application proposes a water resource allocation optimization model with relatively optimal comprehensive benefits, the model fully considers the uncertainty in water resource optimization allocation, determines the constraint conditions, obtains model parameters by training an XGBoost model, and uses the parameters for solving by using an NSGA-III optimization algorithm, and obtains a water resource optimization allocation scheme; by comparison with the present situation, the multi-objective optimization model can take into account the requirements of social fairness, economic development and water quality protection, and provide a balanced water resource allocation scheme. In addition, the present application can accurately simulate the actual situation to obtain uncertain parameters, and application of the optimization algorithm improves the quality and efficiency of the solution, and provides a new idea for future water resource optimization allocation. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 is a flowchart of the water resource allocation optimization method of the embodiment of the present application; Figure 2 is a flowchart of the embodiment of the present application using an XGBoost algorithm to obtain economic benefit coefficients and sewage discharge coefficients and used for water resource allocation; Figure 3 is a flowchart of the embodiment of the present application using an NSGA-III optimization algorithm for solving; Figure 4 is a water satisfaction priority solution, an economic benefit priority solution, a pollution minimum priority solution and a balanced solution in the Pareto frontier solution set of the embodiment of the present application. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the present application will be described clearly and completely below, obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0017] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0018] The present application will be further described below in combination with specific embodiments, but is not limited by the present application.

[0019] As shown in Figure 1 The embodiment of the present application discloses a water resource allocation optimization method, comprising the following steps: Step 1, taking social benefit, economic benefit and ecological benefit as the target, constructing a target function of water resource optimization allocation, and determining the constraint conditions of the target function; This embodiment is illustrated by taking the Jianghan River Basin as an example. The present application selects Shiyan City, Shennongjia Forest Region, Nanyang City, Xiangyang City, Jingmen City, Xiantao City, Tianmen City, Qianjiang City, Xiaogan City and Wuhan City in the middle and lower reaches of the Han River as research objects.

[0020] This embodiment collects data such as agricultural, industrial and domestic water consumption, GDP and population of each city in the middle and lower reaches of the Han River from 2000 to 2023 from the Water Resources Bulletin, and uses the quota method to predict the agricultural, industrial and domestic water consumption in the middle and lower reaches of the Han River in 2030. About 1 / 3 of the water used in Wuhan City is taken from the Han River, so the predicted water consumption of Wuhan City and the GDP data in the subsequent calculation in this embodiment are 1 / 3 of the original data. The water consumption results are shown in Table 1.

[0021] Table 1 Water consumption in the middle and lower reaches of the Han River in different years Unit: billion

[0022]

[0023] The annual average runoff of Huangjiagang from 1990 to 2023 is analyzed by using P-III frequency distribution curve, and 60% is selected as the water supply value of the middle and lower reaches of the Han River, and the water supply of the middle and lower reaches of the Han River under P=75%, P=90% and P=95% is 133.95 billion , 111.11 billion , 99.99 billion .

[0024] In this embodiment, the maximum water use satisfaction of the middle and lower reaches of the Han River is designed as the social benefit target, as shown in formula (1): (1) In formula (1), is the water use satisfaction of the research area, dimensionless; is the decision variable, representing the water quantity allocated by the water use department p in the research area city when the hydrological frequency is %, billion ; is the water consumption of each water use department in each city in the research area, billion ; is the total number of water use departments; is the total number of cities in the research area; The maximum total economic benefit of each water use department in the middle and lower reaches of the Han River is designed as the economic benefit target, as shown in formula (2): (2) In formula (2), The economic benefits of water use in the study area are estimated at 100 million yuan. For the study of regional cities Water use efficiency coefficient, yuan / It needs to be obtained through calculation; The design aims to minimize the total wastewater discharge from all water-using sectors in the middle and lower reaches of the Han River as the ecological benefit objective, as shown in formula (3): (3) In formula (3): The wastewater discharge volume in the study area is 100 million. ; For the study of regional cities The wastewater discharge coefficient is dimensionless and needs to be obtained through calculation.

[0025] After constructing the objective function, set the constraints for the objective function according to the actual situation: 1) Water supply constraints The total allocated water volume cannot exceed the total available water volume, as shown in formula (4): (4) In equation (4): For the middle and lower reaches of the Han River, the hydrological frequency is p Water supply at (%) 2) Water demand constraints A scientific and rational water resource allocation plan needs to guarantee the minimum water demand of each water-using sector to ensure its basic needs and normal operation. In order to avoid wasting water resources and improve water resource utilization efficiency, the amount of water allocated to each sector should not exceed its maximum expected water demand, as shown in formula (5): (5) In equation (5): Indicates water user department Cities in the middle and lower reaches of the Han River The minimum water demand, i.e. rigid water demand.

[0026] 3) Nonnegativity constraint The amount of water allocated to each water user should be greater than 0, and the water demand of each water user should also be greater than zero, as shown in formula (6): (6) Step 2: Calculate the parameters in the objective function constructed in Step 1 using the XGBoost algorithm, and then use the NSGA-Ⅲ optimization algorithm to solve the calculated objective function to obtain the non-dominated solution set; In this step, the water use efficiency coefficient in the economic benefits is calculated. And the wastewater discharge coefficient in ecological benefits The calculation is performed using the XGBoost algorithm in this embodiment. The specific calculation method is as follows: This embodiment collects water consumption data from the industrial, agricultural, and domestic sectors of cities in the middle and lower reaches of the Han River from 2000 to 2023, based on the "Water Resources Bulletin"; economic benefit (GDP) data from cities in the middle and lower reaches of the Han River from 2000 to 2023, based on the "China Urban Statistical Yearbook"; and wastewater discharge data from cities in the middle and lower reaches of the Han River from 2000 to 2023, based on the "China Urban Construction Statistical Yearbook". For example... Figure 2 As shown, dividing GDP by the total water consumption of the industrial, agricultural, and residential sectors yields the economic benefit coefficients for each city in the middle and lower reaches of the Han River from 2000 to 2023, in yuan / cubic meter. Dividing wastewater discharge by the total water consumption of the industrial, agricultural, and residential sectors yields the pollution discharge coefficients for each city in the middle and lower reaches of the Han River from 2000 to 2023, in dimensionless units.

[0027] Then, by taking industrial, agricultural, and domestic water consumption as independent variables and economic benefit coefficient and pollution discharge coefficient as dependent variables, respectively, and performing fitting and prediction based on the XGBoost algorithm, the economic benefit coefficient and pollution discharge coefficient under any decision variable in the water resource optimization allocation model can be obtained. In this way, the economic benefit objective function value and ecological benefit objective function value of the middle and lower reaches of the Hanjiang River can be calculated.

[0028] Finally, the accuracy of the XGBoost algorithm's predictions needs to be judged using evaluation metrics. To assess model performance, this embodiment uses four commonly used performance evaluation metrics: Mean Squared Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R-squared). The formulas for each indicator are as follows: (7) (8) (9) In the formula: For the first The predicted value for each sample, For the corresponding actual value, This represents the number of samples. Lower values ​​for MSE, MAE, and MAPE indicate smaller model prediction errors.

[0029] R 2 R reflects the degree to which explanatory variables explain the dependent variable. 2The closer to 1, the better the model fitting. The calculation formula is as follows: (10) In formula (10): is the average value of the actual value.

[0030] The test performance evaluation results of the XGBoost algorithm are shown in Table 2.

[0031] Table 2 Test performance of XGBoost algorithm

[0032] As can be seen from the table, the test performance of the XGBoost model is good, and can be used for parameter index value extraction.

[0033] After calculating the water benefit coefficient in the objective function and the sewage discharge coefficient in the ecological benefit , the social, economic and ecological benefit objective function values under any decision variable (water distribution scheme) can be calculated for the objective function at the hydrological frequency P = 95%, and the NSGA-Ⅲ algorithm is used to solve the objective function to obtain the optimal Pareto front solution set among the social, economic and ecological benefits in the regional water resources optimal allocation, as shown in Figure 3 The specific method is as follows: a. Initialize the population: randomly generate a set of initial solutions, each solution representing a water resources allocation scheme, including the allocation of water in the industrial, agricultural and domestic water sectors; b. Non-dominated sorting: for each solution in the initial population, perform non-dominated sorting according to its social, economic and ecological benefit objective function values. A non-dominated solution is one that does not have another solution that is superior to it in all objectives; c. Crowding degree calculation: for each non-dominated layer, calculate the crowding degree between solutions. Crowding degree is used to measure the distribution of solutions in the objective space, to avoid solutions being concentrated in a certain area and to maintain the diversity of solutions; d. Selection, crossover and mutation: select parent individuals according to non-dominated sorting and crowding degree, perform crossover operation on the selected parent individuals to generate offspring individuals, and perform mutation operation on the offspring individuals to increase the diversity of the population; e. Merge the population and reorder: merge the parent population and the offspring population to form a new population, and reorder the new population according to non-dominated sorting and crowding degree calculation; f. Elite retention strategy: select the optimal non-dominated solution set from the new population as the population for the next generation evolution. The elite retention strategy ensures that excellent solutions are not lost due to randomness in the evolution process; g. Iterative evolution: repeat steps d to f until a preset maximum number of iterations or a maximum number of function evaluations is reached; h. Obtain the Pareto frontier solution set: the final non-dominated solution set is the optimal Pareto frontier solution set among the three targets of social, economic and ecological benefits in the regional water resources optimal allocation.

[0034] Step 3, determine the equilibrium solution from the non-inferior solution set by the backtracking bargaining method as the final water resources allocation scheme; this step specifically includes; Step 3.1, sort all Pareto non-inferior solutions in order of benefit from high to low from the perspectives of optimal social benefit, optimal economic benefit, and optimal ecological benefit, and the Pareto solution with the highest benefit is the preferred water allocation scheme for this target; Step 3.2, consistently retreat from the preferred scheme to the scheme with lower and lower benefits from the perspectives of optimal social benefit, optimal economic benefit, and optimal ecological benefit, i.e. reduce social benefit, economic benefit, and ecological benefit at the same time each time, and the amount or proportion of reduction is according to the same retreat step size set, after each retreat, check whether there is a Pareto solution that meets the current retreat level in terms of benefits on the three targets. If not, continue to retreat. When a Pareto solution is found that meets the current retreat level in terms of benefits on the three targets, stop retreating. This solution is the equilibrium solution, which is the common choice with the smallest number of retreat steps from the three benefit target perspectives; The obtained Pareto equilibrium solution is the final water resources allocation scheme, and the obtained Pareto solution is the common choice with the smallest number of retreat steps from the three benefit target perspectives, which is a relatively fair and acceptable scheme that balances the demands of social benefit, economic benefit, and ecological benefit. In the backtracking bargaining process, all Pareto non-inferior solutions are sorted in order of benefit from high to low from the perspectives of optimal social benefit, optimal economic benefit, and optimal ecological benefit, and the Pareto solution with the highest benefit is the preferred water allocation scheme for this target.

[0035] The social benefit priority solution, economic benefit priority solution, ecological benefit priority solution, and equilibrium solution in the Pareto frontier solution set are as shown in Figure 4 Figure 4 ​As can be seen, in the single-target priority solution, the social benefit priority solution obtains the highest water use satisfaction, but lower economic benefit and larger pollution discharge; the economic benefit priority solution obtains the highest economic benefit, but lower water use satisfaction and larger pollution discharge; the ecological benefit priority solution obtains the lowest total pollution discharge, but also low water use satisfaction and economic benefit. The equilibrium solution determined based on the backtracking bargaining method shows certain applicability and acceptability in the three targets, takes into account water use satisfaction, economic development and water quality protection requirements, meets the demand for high-level protection and high-quality development, and can provide a more balanced water resource allocation scheme. This balanced water allocation strategy reflects the trade-off of interests of all parties, and also provides a scientific basis for the rational use and management of water resources.

[0036] Step 4, compare and analyze the benefits of the water resource allocation scheme obtained by the target function under different scenarios to obtain the influence of different dryness degrees on the allocation of the basin water resources; In order to illustrate the difference between the benefit results after optimization allocation and the original benefit results, the present application calculates and analyzes the three different benefit values of the Hanjiang River middle and lower reaches after water resource optimization allocation, and the analysis results at P=95% are shown in Table 3.

[0037] Table 3 Benefit values of the Hanjiang River middle and lower reaches in different levels of years

[0038] Compared with 2023 present situation year, the improvement of economic benefit, social benefit and ecological benefit of the Hanjiang River middle and lower reaches in 2030 is more obvious, and the allocation result is reasonable, which verifies the feasibility of the model.

[0039] The present application selects P=75%, P=90% and P=95% three typical dryness scenarios for the Hanjiang River middle and lower reaches, constructs a multi-objective optimization model for comparative analysis. Through NSGA-III algorithm solution, the water use satisfaction, economic benefit and total pollution discharge and other key indicators under each scenario are obtained, and the influence of different dryness degrees on the allocation of the basin water resources is further discussed. The allocation results under different typical dryness years are shown in Table 4.

[0040] Table 4 Comparison results of multi-objective model under different typical dryness years

[0041] The results show that the water use satisfaction generally presents a decreasing characteristic, and the higher the dryness degree, the lower the water use satisfaction. The economic benefit loss expands with the aggravation of dryness degree. The reduction range of pollution discharge is positively correlated with the reduction of available water. The reduction of available water directly limits the water resource allocation of each water use department, and the pollution discharge decreases with the reduction of production scale.

[0042] The embodiment of the present application further discloses a system for realizing the optimization method of water resource allocation, comprising: a target function construction module, which is used for constructing a target function of water resource optimal allocation with social benefit, economic benefit and ecological benefit as targets, and determining constraint conditions of the target function; a target function solving module, which is used for calculating parameters in the target function constructed by the target function construction module based on an XGBoost algorithm, and then solving the target function after calculation by using an NSGA-III optimization algorithm to obtain a non-inferior solution set; a water resource allocation scheme determination module, which is used for determining an equilibrium solution from the non-inferior solution set by using a backward bargaining method, as a final water resource allocation scheme; a water resource allocation scheme comparison module, which is used for comparing and analyzing benefits of water resource allocation schemes obtained by the target function under different scenarios to obtain influences of different low water levels on the water resource allocation of a basin.

[0043] An electronic device comprises a memory and a processor, the memory stores a computer program, and the processor realizes steps of the above method when executing the computer program.

[0044] A computer readable storage medium, which stores a computer program, the computer program is executed by a processor to realize steps of the above method.

[0045] The above is only the preferred embodiment of the present application, and does not limit the implementation and protection scope of the present application. It should be realized by those skilled in the art that any equivalent replacement and obvious change made according to the content of the present application should be included in the protection scope of the present application.

Claims

1. A method for optimizing water resource allocation, characterized in that, Includes the following steps: Step 1: Construct an objective function for optimal allocation of water resources, taking social, economic, and ecological benefits as objectives, and determine the constraints of the objective function; Step 2: Calculate the parameters in the objective function constructed in Step 1 based on the XGBoost algorithm, and then use the NSGA-Ⅲ optimization algorithm to solve the calculated objective function to obtain the non-dominated solution set; Step 3: Determine the equilibrium solution from the non-inferior solution set through the compromise bargaining method, which will serve as the final water resource allocation scheme; Step 4: Compare and analyze the benefits of water resource allocation schemes obtained from the objective function under different scenarios to obtain the impact of different drought levels on water resource allocation in the basin.

2. The method for optimizing water resource allocation according to claim 1, characterized in that, In step 1, the objective function for optimizing water resource allocation is: The design aims to maximize regional water user satisfaction as its social benefit objective, as shown in the following formula: （1） In equation (1), Water satisfaction in the study area; Let be the decision variable, representing the hydrological frequency. p (%), water-using departments In the study area cities The amount of water allocated above; To study the water demand of various water-using sectors in different cities within the region; Total number of water-using sectors; The total number of cities in the study area; The design aims to maximize the overall economic benefits of all water-using sectors in the region, as shown in the following formula: （2） In equation (2), To study the economic benefits of water use in the research area; For the city The water use efficiency coefficient; The design aims to minimize the total wastewater discharge from all water-using sectors in the region as the ecological benefit objective, as shown in the following formula: （3） In equation (3), Wastewater discharge in the study area; This represents the sewage discharge coefficient of city A.

3. The method for optimizing water resource allocation according to claim 2, characterized in that, Step 2 defines the constraints for the objective function as follows: 1) Water supply constraints The total allocated water volume cannot exceed the total available water volume, as shown in the following formula: （4） In equation (4): For the study area at a hydrological frequency of p Water supply at (%) 2) Water demand constraints, The amount of water allocated to each department should not exceed its maximum expected water demand, as shown in the following formula: （5） In equation (5): Indicates water user department In the study area cities The minimum water demand, i.e. rigid water demand; 3) Nonnegativity constraint The amount of water allocated to each water-using department should be greater than zero, and the water demand of each water-using department should also be greater than zero, as shown in the following formula: （6）。 4. The method for optimizing water resource allocation according to claim 2, characterized in that, In step 2, the XGBoost algorithm is used to evaluate the water use efficiency coefficient in the economic benefits. And the wastewater discharge coefficient in ecological benefits The calculations are performed; methods for calculating parameters using the XGBoost algorithm include: Data on water consumption, economic benefits, and wastewater discharge from industrial, agricultural, and residential sectors in the study area over the years were collected. Industrial, agricultural, and residential water consumption were used as independent variables, and economic benefit coefficients and wastewater discharge coefficients were used as dependent variables. The XGBoost algorithm was used for fitting and prediction to obtain the economic benefit coefficient and wastewater discharge coefficient under any decision variable in the water resource optimization allocation model. Finally, evaluation indicators were used to assess the calculation accuracy of the XGBoost algorithm.

5. The method for optimizing water resource allocation according to claim 1, characterized in that, In step 2, after calculating the parameters in the objective function, the NSGA-Ⅲ algorithm is used to solve the objective function to obtain the optimal Pareto front solution set among the three objectives of social, economic and ecological benefits in the optimal allocation of regional water resources.

6. The method for optimizing water resource allocation according to claim 5, characterized in that, The method for solving the objective function using the NSGA-Ⅲ algorithm is as follows: a. Initialize the population: Randomly generate a set of initial solutions, each solution representing a water resource allocation scheme, including the water allocation for industrial, agricultural, and domestic water use sectors; b. Non-dominated sorting: For each solution in the initial population, non-dominated sorting is performed based on the three objective function values ​​of social benefit, economic benefit and ecological benefit. c. Crowding degree calculation: For each non-dominated layer, calculate the crowding degree between solutions; d. Selection, crossover, and mutation: Select parent individuals based on non-dominated order and crowding, perform crossover on the selected parent individuals to generate offspring individuals, and perform mutation on the offspring individuals to increase the diversity of the population. e. Merge populations and reorder: Merge the parent population and the offspring population to form a new population, and re-perform non-dominated ordering and crowding calculation on the new population. f. Elite Preservation Strategy: Select the optimal non-dominated solution set from the new population as the population for the next generation of evolution; g. Iterative evolution: Repeat steps d to f until the preset maximum number of iterations or the maximum number of function evaluations is reached; h. Obtain the Pareto front solution set: The final non-dominated solution set is the optimal Pareto front solution set among the three objectives of social, economic and ecological benefits in the optimal allocation of regional water resources.

7. The method for optimizing water resource allocation according to claim 1, characterized in that, The method for determining the equilibrium solution in step 3 is as follows: Step 4.1: Sort all Pareto non-dominated solutions in descending order of benefits from the perspectives of optimal social benefits, optimal economic benefits, and optimal ecological benefits. The Pareto solution with the highest benefit is the preferred water allocation scheme for this objective. Step 4.2: From the perspectives of optimal social benefit, optimal economic benefit, and optimal ecological benefit, proceed in a consistent manner from the preferred solution to the solutions with progressively lower benefits. After each step backward, check if there exists a Pareto solution whose benefits in all three objectives simultaneously satisfy the level after the current step backward. If not, continue stepping backward. When a Pareto solution is found whose benefits in all three objectives simultaneously satisfy the level after the current step backward, stop stepping backward. This solution is the equilibrium solution, which is the final water resource allocation scheme.

8. A system for implementing the water resource allocation optimization method according to any one of claims 1-7, characterized in that, include: The objective function construction module is used to construct an objective function for the optimal allocation of water resources with social, economic and ecological benefits as objectives, and to determine the constraints of the objective function; The objective function solving module is used to calculate the parameters of the objective function constructed by the objective function construction module based on the XGBoost algorithm, and then use the NSGA-Ⅲ optimization algorithm to solve the calculated objective function to obtain the non-dominated solution set. The water resource allocation scheme determination module is used to determine the equilibrium solution from the set of non-inferior solutions through a backward bargaining method, which serves as the final water resource allocation scheme. The water resource allocation scheme comparison module is used to compare and analyze the benefits of water resource allocation schemes obtained from objective functions under different scenarios, in order to obtain the impact of different drought levels on watershed water resource allocation.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

Images