Water resource optimization scheduling method and system, computer equipment and storage medium

Abstract

The invention provides a water resource optimization scheduling method and system, computer equipment and a storage medium, and belongs to the field of water resource optimization scheduling, and the method comprises the steps: obtaining the local water supply amount, the department water demand amount and the water demand interval of each water receiving area; under the common constraint of the three parameters, the calculated economic output corresponding to the water resource input, the ecological service value of the water area, the water satisfaction quantitative result and the carbon emission in the water transfer process are used as optimization indexes, and the monthly scale is used as a time unit; according to an optimization result, a monthly water resource scheduling scheme is generated through iteration in sequence, and an annual water resource scheduling scheme is formed through integration; and determining a scheduling scheme with the optimal comprehensive benefit from the annual water resource scheduling schemes as a final water resource scheduling scheme. According to the method, the ecological benefit and social benefit of water transfer in the water transfer system can be coordinated, the ecological service value of a water area is remarkably improved and the carbon emission is reduced while the water demand of a water receiving area is ensured, and the comprehensive optimization of water resource scheduling is realized.

Description

Technical Field

[0001] This invention belongs to the field of water resource optimization scheduling technology, specifically relating to a water resource optimization scheduling method, system, computer equipment, and storage medium. Background Technology

[0002] The uneven spatial and temporal distribution of water resources and water demand is becoming increasingly prominent, and coordinating the contradiction between water supply and demand is a common challenge faced by many countries and regions. Although various water resource allocation methods have been proposed and applied in practical engineering, many technical bottlenecks and limitations still exist in dealing with the current complex and ever-changing allocation needs.

[0003] In terms of the target system, with the deepening of the concept of sustainable development, modern water resource allocation not only needs to maximize economic benefits, but also needs to take into account multiple objectives such as ecological environmental protection and social equity. However, existing allocation methods still have the problem of difficulty in effectively balancing and coordinating among various objectives in multi-objective synergistic optimization, which affects the comprehensive benefits of allocation results.

[0004] In terms of time scale, traditional methods often optimize based on a single time scale, failing to effectively coordinate the inherent multi-time scale coupling characteristics of water resource systems. Such fragmented optimization strategies are ill-suited to the dynamically changing management needs during actual operation, resulting in poor adaptability and stability of scheduling schemes in practical applications.

[0005] In conclusion, existing water resource allocation methods are insufficient to meet the needs of efficient, coordinated, and sustainable water resource management in the new era. Summary of the Invention

[0006] To address the challenges of multi-objective and multi-scale water resource allocation, this invention provides a water resource optimization allocation method that coordinates the ecological and social benefits of water transfer in a water transfer system, thereby achieving comprehensive and optimized water resource allocation.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A water resource optimization scheduling method, comprising: The local available water volume of each water-receiving area under different water inflow frequencies in the study area, and the departmental water demand under water-saving scenarios and normal water use scenarios are obtained. Based on the water demand of each department, the water demand range of the water-receiving area is determined. The water demand range of the water-receiving area is used to characterize the water consumption range under water-saving scenarios and normal water use scenarios. Under the common constraints of the local available water supply, the water demand of each sector and the water demand range of the water receiving area, the economic output corresponding to water resource input, the ecological service value of the water area, the quantitative results of water user satisfaction and the carbon emissions of the water transfer process are calculated. Using a monthly timeframe as the unit of time, and with economic output, water ecosystem service value, water user satisfaction quantification results, and carbon emissions as optimization indicators, the system allocates local available water and external water transfer volume, optimizes ecological benefit targets, water user satisfaction targets, and carbon emission reduction targets on a monthly basis, and generates monthly water resource scheduling plans iteratively based on the optimization results. The monthly water resource scheduling plans for 12 months are then integrated to form an annual water resource scheduling plan. The optimal water resource allocation plan with the best overall benefits is determined from the annual water resource allocation plans and used as the final water resource allocation plan.

[0008] Preferably, determining the optimal water resource allocation scheme from the annual water resource allocation schemes as the final water resource allocation scheme specifically includes the following steps: Using an annual time scale as the unit of time, the economic benefit target of the annual water resource allocation plan is calculated based on the economic output corresponding to the water resource input, and the annual water resource allocation plan and its corresponding economic benefit target are stored in the candidate solution set; wherein, the economic output of each month is accumulated and summarized to form the total annual economic output, which is used as the economic benefit target; Repeat the monthly-scale solution and annual-scale evaluation process, continuously adding qualified solutions to the candidate solution set; until the preset maximum number of iterations is reached, and the candidate solution set is accumulated. An improved multi-objective particle swarm optimization algorithm is used to solve for the candidate solution set, and the Pareto solution set is obtained. By using fuzzy hierarchical analysis and entropy weight method for multi-attribute decision-making, the final water resource allocation scheme is selected from the Pareto solution set.

[0009] Preferably, the step of using fuzzy hierarchical analysis and entropy weight method to perform multi-attribute decision-making and select the final water resource allocation scheme from the Pareto solution set specifically includes the following steps: The decision indicators and decision objects are determined; the decision indicators include annual economic benefit targets, monthly ecological benefit targets, social benefit targets, and environmental benefit targets; the decision objects are all non-dominated solutions in the Pareto solution set. Based on decision indicators and decision objects, subjective weights are calculated using fuzzy hierarchical analysis and objective weights are calculated using entropy weight method. The subjective weights and objective weights are combined to obtain the combined weights; Based on the combined weights, the schemes in the Pareto solution set are evaluated for multiple attributes, and the final water resource scheduling scheme is selected based on the evaluation results.

[0010] Preferably, the economic output corresponding to water resource input is calculated using a water economic production model. The improved Cobb-Douglas function, which incorporates a technological progress factor, is used to obtain the water economic production model, which is specifically as follows: ; in, The numbering of the water-receiving area for Economic output of the water-receiving area; Based on basic production efficiency parameters; This indicates technological progress or productivity growth. The rate of technological progress or the growth rate of production efficiency. This is the total number of years from the beginning of the fitting period to the planning year; , , , These represent the four factors of production: labor input, capital input, energy input, and water resource input. , , , These are the elasticity coefficients of each production factor; The water ecosystem service value is quantified using a water ecosystem service value model, which specifically includes: ; in, for The ecological service value of water bodies within the time system. The equivalent factor of ecosystem service value per unit area of ​​water. The economic value per unit equivalent; for The system's water area and water storage capacity at any given time. There is a functional relationship; The quantitative result of water use satisfaction is calculated using a water use satisfaction model, which is specifically as follows: ; in, for Quantitative results of water user satisfaction in the water-receiving area To achieve the basic satisfaction level at the lower limit of water demand, and These are the upper and lower limits of water demand, respectively. For the local water supply, for Water diversion volume from outside the water receiving area; The carbon emissions from the water diversion process are calculated using a carbon emission accounting model, which specifically refers to: ; in, This refers to the total carbon emissions during the water diversion process. To quantify the carbon emissions generated by the energy consumption of pumping stations during water transfer, Changes in ecosystem carbon sequestration caused by variations in the total amount of water resources within the system. Carbon emission coefficient per unit energy consumption This refers to the total energy consumption generated by the pumping station during the water diversion process. Carbon sequestration per unit area of ​​lakes and wetlands This represents the change in the area of ​​lakes and wetlands caused by human water diversion; an increase in area is positive, and a decrease in area is negative.

[0011] Preferably, the economic benefit target is an outer annual target, and the optimization model for the outer annual target is: ; in, For economic benefit objectives; The ecological benefit target, water use satisfaction target, and carbon emission reduction target are the inner-layer monthly targets, and the optimization model for the inner-layer monthly targets is as follows: ; ; ; in, , To serve the value goal of ecology For the first The ecological service value of water bodies within a given time period. To achieve the goal of water user satisfaction, For the first Time period Water satisfaction in the water-receiving area To achieve carbon emission reduction targets, For the first Carbon emissions of the system during a given time period.

[0012] Preferably, the improved multi-objective particle swarm optimization algorithm is based on the original multi-objective particle swarm optimization algorithm, introducing a dynamic inertia weight strategy and an alternating projection correction algorithm, specifically including: Dynamic inertia weighting strategy: Introducing inertia weights and a learning factor, combining individual and group extreme values ​​to update particle velocity and position; assuming that in 3D search space, by Each particle forms a population , of which Each particle can be represented as one dimensional vector Then its core formula is expressed as: ; ; in, , These are the individual extreme value and the group extreme value, respectively. Inertial weights; For the dimensions of the search space; This represents the current iteration number; For the first The velocity vector of each particle; , is a non-negative constant, and is the learning factor for the particle; , It is distributed in Random numbers between; For exercise time; The alternating projection correction algorithm consists of the following steps: Equality constraint projection correction: Denote the equality constraint as In order to correct To satisfy this equality constraint, the least squares method is used to minimize the error for the current... The steps for projection are as follows: ; in, It is a matrix for calculating the correction direction, adjusting the error along the direction of the equality constraint; It is the residual of the equality constraint, representing the current... The degree to which the constraints were violated; Adjustment of upper and lower limits for independent variables: The independent variable has upper and lower limit constraints. If the limits are exceeded, a truncation method can be used for correction. ; Correct out-of-range values ​​to between the upper and lower limits of the independent variable to ensure the new... Satisfy its own upper and lower limit constraints; Other inequality constraint corrections: In addition to the upper and lower bound constraints of the independent variable itself, there may be other more complex inequality constraints. Identify cases where constraints are not met, and use a fixed-step direct correction method to gradually correct them until the lake's water storage constraints are satisfied, then return to the latest... value; Alternating iteration: The above three steps are executed alternately to gradually approach the value that simultaneously satisfies equality constraints and inequality constraints; Convergence is determined when the change between two iterations is small, indicating that a solution satisfying the constraints has been found. ; in, For tolerance.

[0013] Preferably, obtaining the water-receiving area and the water-demanding area specifically includes the following steps: By estimating the local surface water supply, groundwater supply, and reclaimed water supply in each water-receiving area of ​​the study region, the time period was obtained. No. Local water supply in the water-receiving area ; Estimate the water consumption of the water-receiving area during the same period under the scenario of implementing the strictest water conservation measures. Water consumption under normal water usage scenarios Water use types in various scenarios include domestic water use, industrial water use, agricultural water use, and ecological water use. Based on the local available water supply and the water demand of each sector, the water demand range of the water-receiving area for this period is determined and denoted as follows: .

[0014] This invention also proposes a water resource optimization scheduling system, comprising: The water resources supply and demand analysis module is used to obtain the local available water volume of each water-receiving area under different water inflow frequencies in the study area, and the departmental water demand of each department under water-saving scenarios and normal water use scenarios. Based on the water demand of each department, the water demand range of the water-receiving area is determined. The water demand range of the water-receiving area is used to characterize the water consumption range under water-saving scenarios and normal water use scenarios. The multi-dimensional system module is used to calculate the economic output corresponding to water resource input, the ecological service value of water area, the quantitative results of water user satisfaction, and the carbon emissions of water transfer process under the common constraints of the local available water supply, the water demand of each sector and the water demand range of the water receiving area. The scheduling plan generation module is used to optimize the ecological benefits, water use satisfaction and carbon emission targets on a monthly basis, using economic output, water ecosystem service value, water user satisfaction and carbon emission as optimization indicators. It allocates local available water and external water transfer, optimizes the ecological benefits, water user satisfaction and carbon emission reduction targets on a monthly basis, and generates monthly water resource scheduling plans in a sequential iteration based on the optimization results. The monthly water resource scheduling plans of 12 months are integrated to form an annual water resource scheduling plan. The scheduling scheme optimization module is used to determine the scheduling scheme with the best comprehensive benefits from the annual water resource scheduling schemes, which will be used as the final water resource scheduling scheme.

[0015] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement any of the steps in the water resource optimization scheduling method.

[0016] The present invention also provides a computer-readable storage medium storing a computer program, which, when loaded by a processor, is capable of executing any of the steps in the water resource optimization scheduling method.

[0017] The water resource optimization scheduling method provided by this invention has the following beneficial effects: This invention, based on the premise of obtaining the water demand range of the water-receiving area, incorporates four categories of indicators: economic output, ecosystem service value, water user satisfaction, and carbon emissions. Using the water demand range as a constraint, it selects the optimal solution from the Pareto set through multi-attribute decision-making, achieving a balance of multiple objectives. Simultaneously, it optimizes ecological, satisfaction, and emission reduction targets on a monthly basis, generating monthly plans; these are then integrated into annual plans, and economic benefits are evaluated, forming a multi-scale nested structure from monthly to annual. This adapts to the temporal characteristics of the water resource system, enhances the adaptability of the plans, and realizes multi-scale, multi-objective optimized water resource scheduling, thereby effectively improving the comprehensive utilization efficiency of water resources. Attached Figure Description

[0018] To more clearly illustrate the embodiments and design schemes of the present invention, the accompanying drawings required for this embodiment will be briefly described below. The drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart of the water resource optimization scheduling method according to Embodiment 1 of the present invention; Figure 2 A flowchart of the algorithm for improving the multi-objective particle swarm optimization algorithm. Detailed Implementation

[0020] To enable those skilled in the art to better understand and implement the technical solutions of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and should not be construed as limiting the scope of protection of the present invention.

[0021] To address the problem that existing water resource allocation methods are insufficient to meet the needs of efficient, coordinated, and sustainable water resource management in the new era, this invention provides a water resource optimization allocation method, specifically a multi-objective, multi-scale water resource optimization allocation method based on a multi-dimensional system of water, economy, ecology, satisfaction, and carbon emissions.

[0022] First, this invention constructs a multi-dimensional system model of water-economy-ecology-satisfaction-carbon emissions. This multi-dimensional system model specifically includes a water economic production model, a water ecosystem service value model, a water user satisfaction model, and a carbon emission accounting model. The following is a detailed explanation of each model:

[0023] (1) Water economy production model: The improved Cobb-Douglas function with the introduction of technological progress factor is adopted, and the inputs of labor, capital, energy and water resources are included. The ridge regression method is used to fit the coefficients of each water-receiving area, calculate the economic output, and construct the water economy production model of each water-receiving area.

[0024] Specifically, assuming there are a total of Each water-receiving area, water-receiving area number A water economy production model is obtained by employing an improved Cobb-Douglas function that incorporates a technology progress factor, in the water-receiving area. The water economy production model is as follows:

[0025] ; in, The numbering of the water-receiving area for The economic output corresponding to water resource input in the water-receiving area; Based on basic production efficiency parameters; This indicates technological progress or productivity growth. The rate of technological progress or the growth rate of production efficiency. To fit the total number of years from the beginning of the period to the planning year, this represents the duration of technological progress and indicates the cumulative impact of technological progress on production efficiency over time. , , , These represent the four factors of production: labor input, capital input, energy input, and water resource input (the data on these factors of production need to be standardized). , , , These are the elasticity coefficients of each production factor, and ridge regression is used to fit the coefficients. The water demand interval is used to limit water resource input. The boundary of values, Locally supplied water Water diversion volume from outside the water receiving area The composition, specifically the formula, is as follows:

[0026] .

[0027] in, T This indicates the total number of time periods within the scheduling cycle.

[0028] (2) Water Ecosystem Service Value Model: Based on quantitative research on the ecosystem service value of China's terrestrial ecosystems, this model quantifies the ecosystem service value of water bodies based on water area (related to water storage capacity), unit area equivalent factor, and unit equivalent economic value. The specific quantification of the ecosystem service value of water bodies is as follows:

[0029] ; in, for The ecological service value of water bodies within the time system. The equivalent factor of ecosystem service value per unit area of ​​water. The economic value per unit equivalent; for The system's water area and water storage capacity at any given time. A functional relationship exists. Furthermore, assume the number of pumps and gates in the water diversion system is... One, pump gate number , The water volume drawn or discharged during the period is , The value is constrained by the pump gate itself and the water passage capacity of the adjacent river channel. Following the principle of water balance, by , The decision is made jointly and is subject to upper and lower limits on water storage.

[0030] (3) Water Use Satisfaction Model: Based on the relationship between the total water supply and the water demand range, the satisfaction level (basic satisfaction plus supply-demand matching adjustment value) is calculated, and the degree of water use satisfaction is quantified according to the water supply and demand situation. The water use satisfaction model is as follows:

[0031] ; in, for Quantitative results of water user satisfaction in the water-receiving area To achieve the basic satisfaction level at the lower limit of water demand, and These are the upper and lower limits of water demand, respectively. For the local water supply, for Water diversion volume from outside the water receiving area.

[0032] (4) Carbon emission accounting model for water transfer system: quantification of the first Energy consumption and carbon emissions of pumping stations during time-limited water diversion Changes in carbon sequestration in lakes and wetlands due to variations in the total amount of water resources within the system The carbon emission accounting model is as follows:

[0033] ; ; ; in, This refers to the total carbon emissions during the water diversion process. Carbon emission coefficient per unit energy consumption This refers to the total energy consumption generated by the pumping station during the water diversion process. For pumping stations energy intensity For pumping stations The operating efficiency. Carbon sequestration per unit area of ​​lakes and wetlands This represents the change in lake and wetland area caused by human-induced water diversion; an increase in area is positive, and a decrease in area is negative. The value is determined by the system's water storage capacity in the previous period. as well as , A joint decision.

[0034] Based on this, the present invention establishes a multi-scale nested bi-layer optimization model that includes annual-scale economic benefit targets and monthly-scale ecological benefit, water use satisfaction, and carbon emission targets.

[0035] (1) The economic benefit target is the outer annual target, and the optimization model for the outer annual target is: ; in, For the purpose of economic benefit, for Economic output of the water-receiving area.

[0036] (2) The ecological benefit target, water user satisfaction target, and carbon emission reduction target are the inner-layer monthly targets. The optimization model for the inner-layer monthly targets is as follows: ; ; ; in, , To serve the value goal of ecology For the first The ecological service value of water bodies within the time period system. To achieve the goal of water user satisfaction, For the first Time period Water satisfaction in the water-receiving area. To achieve carbon emission reduction targets, For the first Carbon emissions of the system during a given time period.

[0037] In terms of solution algorithms, existing optimization methods still face many challenges when dealing with water resource systems with complex constraints. Especially in water transfer systems involving multiple pumping stations, reservoirs, and water conveyance channels, traditional algorithms are prone to getting trapped in local optima or failing to obtain feasible solutions, which seriously restricts the feasibility of scheduling schemes and the realization of system benefits.

[0038] Therefore, this invention, based on the original multi-objective particle swarm optimization algorithm, introduces a dynamic inertia weight strategy and an alternating projection correction algorithm to obtain an improved multi-objective particle swarm optimization algorithm. The specific improvements to the multi-objective particle swarm optimization algorithm are as follows:

[0039] (1) Dynamic Inertia Weight Strategy: Inertia weight and learning factor are introduced, and the velocity and position of particles are updated by combining individual extreme values ​​and group extreme values. Assuming that in 3D search space, by Each particle forms a population , of which Each particle can be represented as one dimensional vector Then its core formula can be expressed as:

[0040] ; ; in, , These are the individual extreme value and the group extreme value, respectively. Inertial weights; For the dimensions of the search space; This represents the current iteration number; For the first The velocity vector of each particle; , It is a non-negative constant, representing the particle's learning factor (acceleration factor). , It is distributed in Random numbers between; The duration of the exercise is usually taken as 1.

[0041] (2) Alternating projection correction algorithm, the steps are as follows: Equality constraint projection correction: Denote the equality constraint as In order to correct To satisfy this equality constraint, the least squares method is used to minimize the error. For the current... The steps for projection are as follows:

[0042] ; in, It is a matrix for calculating the correction direction, adjusting the error along the direction of the equality constraint; It is the residual (i.e., error) of the equality constraint, representing the current... The degree to which the constraint was violated. Therefore, subtract the correction amount. , It is then projected onto the subspace of the equality constraint.

[0043] Adjustment of upper and lower limits for independent variables: Independent variables have upper and lower limit constraints ( If the value exceeds the upper or lower limit, a truncation method can be used for correction. ; Correct out-of-range values ​​to between the upper and lower limits of the independent variable to ensure the new... It satisfies its own upper and lower limit constraints.

[0044] Other inequality constraint corrections: In addition to the upper and lower bound constraints of the independent variable itself, there may be other more complex inequality constraints. Identify cases where constraints are not met, and use a fixed-step direct correction method to gradually correct them until the lake's water storage constraints are satisfied, then return to the latest... value.

[0045] Alternating iteration: The above three steps are executed alternately to gradually approach the value that simultaneously satisfies the equality and inequality constraints.

[0046] Convergence is determined when the change between two iterations is small, indicating that a solution satisfying the constraints has been found. ; in, For tolerance.

[0047] The computational process of the improved multi-objective particle swarm optimization algorithm is as follows: Figure 2 As shown, it includes the following steps: (1) Variable initialization. The initial time period is set to 1. Read the local available water volume, water demand range and other initial data of the water receiving area in the current time period.

[0048] (2) Particle initialization. An alternating projection correction algorithm is used to determine the initial values ​​and velocities of particles within the feasible region.

[0049] (3) Optimization process. Optimize each time period, calculate the fitness of the current scheme, and determine the individual optimal and global optimal schemes; iteratively optimize, update the velocity and position of particles, recalculate and update the individual optimal and group optimal, and archive the non-dominated solutions to obtain the non-dominated solution set; apply the subjective and objective combined weighting method to determine the weight of each dimension, and select the optimal scheduling scheme for the current time period based on the weight; optimize each time period in turn, for a total of 12 time periods, corresponding to the 12 months of the year, and output the annual optimized scheduling scheme.

[0050] (4) Multiple rounds of looping. The above process is repeated, and the annual scheduling schemes optimized in each round are stored until the set maximum number of loops is reached, at which point the loop ends and the annual scheduling scheme set is obtained.

[0051] (5) Output. Calculate the annual scheduling scheme with the highest economic benefit, and output it as the optimal scheme.

[0052] Example 1 Based on the multi-dimensional system module, multi-scale nested two-layer optimization model, and improved multi-objective particle swarm optimization algorithm proposed in this invention, this embodiment provides a water resource optimization scheduling method, such as... Figure 1As shown, the method includes the following steps: S1. Analyze and estimate the local available water supply (e.g., surface water, groundwater, reclaimed water supply) and water demand (e.g., domestic, industrial, agricultural, ecological water use) of each water-receiving area under different water inflow frequencies. Based on the water demand of each water-receiving area under water-saving and normal water use scenarios, determine the demand range of each water-receiving area. The local available water supply and demand range of each water-receiving area provide clear water volume boundaries for the generation of subsequent scheduling plans.

[0053] Specifically, this embodiment focuses on 20 water-receiving areas within a certain study area. The local available water supply and water demand range of each water receiving area under three water inflow frequencies: 75%, 95%, and 15%. P It's the probability of water coming in. P =50% represents the water inflow that the region can achieve under 50% conditions. By estimating the local surface water supply, groundwater supply, and reclaimed water supply in each water-receiving area of ​​the study region, the first... Months Local water supply in the water-receiving area .

[0054] Estimate the water consumption of each water-receiving area in each month under the scenario of implementing the strictest water conservation measures. Water consumption under normal water usage scenarios This mainly includes domestic water use, industrial water use, agricultural water use, and ecological water use. The water demand range for the receiving area in that month is obtained and denoted as... .

[0055] Table 1. Estimated water demand ranges for various water-receiving areas and departments under different water inflow scenarios (Unit: 100 million cubic meters) S2. Under the combined constraints of local water supply and water demand in each water-receiving area, calculate the economic output corresponding to water resource input, the ecological service value of the water area, the quantitative results of water user satisfaction, and the carbon emissions of the water transfer process. In practice, in addition to these constraints, there are also constraints such as the water-carrying capacity of water conservancy projects such as pumping stations and river channels, and the water storage constraints of lakes.

[0056] (1) Calculate the economic output corresponding to water resource input using a water economic production model: ; Specifically, the water demand range determined by S1 (the range of water demand under water-saving scenarios and normal water use scenarios) is the water resource input in the water economy production model. WValue boundaries were defined. When calculating economic output, the water resource input must be selected within this range (neither lower than the minimum water demand under water-saving scenarios nor exceeding the maximum water demand under normal water use scenarios) to ensure that water resource input meets the reasonable range of actual water demand. Furthermore, the economic output of each water-receiving area is calculated using an improved Cobb-Douglas function, and finally summarized into total economic output, providing data support for optimizing annual-scale economic benefit targets.

[0057] The water economy production model quantifies water resource input and calculates economic output under the constraints of water demand range, ensuring the rationality and practical applicability of the model results.

[0058] Table 2. Fitting results of water economy production models in various water-receiving areas (2) Quantify the ecosystem service value of water bodies using a water ecosystem service value model: ; In this embodiment, the local available water volume in the water-receiving area It is the total water supply. An important component. and The difference needs to be obtained from the water transfer system, i.e., the amount of water transferred externally. The amount of water transferred externally will directly affect the system's water storage (such as the water storage of lakes, rivers, and other water bodies). In the water ecosystem service value model, there is a functional relationship between the water area A and the system's water storage (an increase or decrease in water storage will lead to the expansion or shrinkage of the water area), and the ecosystem service value is ultimately expressed through the water ecosystem service value model.

[0059] The water demand range determines the target boundary of the total water supply. The water demand within the demand zone must be met, and its value directly affects the amount of water transferred out, thus impacting the balance of the system's water storage. If the total water supply is excessive, especially with excessive water transfer, it may lead to water area shrinkage (reduced water storage → smaller water area → lower ecosystem service value). Conversely, a reasonable water supply can maintain the stability of the aquatic ecosystem (stable water storage → stable water area → maintained ecosystem service value at a reasonable level). Therefore, the water demand zone, by constraining the total water supply, indirectly affects the system's water storage and water area, ultimately influencing the calculation of ecosystem service value.

[0060] In summary, the local available water volume and water demand range affect the relationship between system water storage and water area, thus becoming an important indirect basis for quantifying the ecological value of water bodies in the water ecosystem service value model.

[0061] (3) Calculate the quantitative results of water use satisfaction using the water use satisfaction model: ; In this embodiment, the water demand interval is the core parameter boundary of the water use satisfaction model and is directly used as a key parameter in the model formula. and It defines the upper and lower limits of the reasonable water demand range.

[0062] In the water user satisfaction model, the total water supply W Including local available water volume (Including surface water, groundwater, and reclaimed water supply, estimated in step S1) and externally diverted water (water diverted from external sources when local available water is insufficient). Therefore, yes W The basic components, the size of which directly affects Can it cover the water demand area: If the local water supply is sufficient, It can be made up of local water supply alone; if insufficient, it needs to be supplemented by water from other regions. The water demand reaches a reasonable value within the required range.

[0063] Therefore, the water demand range defines the reasonable water demand area, and the local available water supply is the basis for the actual supply capacity; both together determine the water demand. The degree of matching with the water demand area.

[0064] (4) Calculate the carbon emissions during the water transfer process using a carbon emission accounting model: ; Local available water volume With total water supply The difference determines the scale of water diversion, directly impacting the energy consumption and carbon emissions of the pumping station. In the carbon emission accounting model, the total carbon emissions during the water diversion process... C It consists of two parts: carbon emissions from pump station energy consumption and changes in carbon sequestration by the ecosystem.

[0065] like ≥ Then there is no need to divert water from outside; if < In such cases, water needs to be diverted from outside the pumping station to fill the gap. The larger the volume of water diverted, the higher the pumping distance and energy consumption. C The larger.

[0066] In this embodiment, the water demand area is defined. The fluctuation range of water volume affects the fluctuation range of water diversion, and thus affects... E The dynamic changes. When all water-receiving areas adopt water-saving measures, the amount of water transferred out is relatively small, corresponding to... C Smaller; conversely, corresponding C Relatively large.

[0067] and These factors collectively influence the water storage capacity of the water diversion system. Changes in the water storage capacity of the system alter ecosystem elements such as water area and wetland area, thereby changing the ecosystem's carbon sequestration capacity and consequently affecting the total amount of carbon sequestrated. C .

[0068] Therefore, the water demand range constrains the total water supply, and the gap between local available water and the total water supply determines the absolute scale of water diversion. Both factors, through influencing pump station energy consumption and ecosystem carbon sequestration, ultimately determine the total carbon emissions calculated by the carbon emission accounting model. The greater the water demand, the smaller the local available water, and the greater the amount of water diverted, the higher the total carbon emissions are typically.

[0069] S3. Using a monthly time scale as the time unit, and with economic output, water ecosystem service value, water user satisfaction quantification results, and carbon emissions as optimization indicators, the system allocates local available water and external water transfer volume, optimizes ecological benefit targets, water user satisfaction targets, and carbon emission reduction targets on a monthly basis, and iterates to generate monthly water resource scheduling plans based on the optimization results. The monthly water resource scheduling plans for 12 months are then integrated to form an annual water resource scheduling plan.

[0070] Among them, the economic benefit target is obtained through the optimization model of the outer annual target. The ecosystem service value target is obtained through the optimization model of the inner monthly targets. , No. Water ecosystem service value within the time period system To achieve the goal of water user satisfaction and carbon emission reduction targets , For the first Carbon emissions of the system during a given time period.

[0071] S4. Determine the scheduling plan with the best overall benefits from the annual water resource scheduling plans, and use it as the final water resource scheduling plan.

[0072] Specifically, this embodiment uses an annual time scale as the time unit, calculates the economic benefit target of the annual water resource scheduling plan based on the economic output corresponding to the water resource input, and stores the annual water resource scheduling plan and its corresponding economic benefit target in the candidate solution set; wherein, the economic output of each month is accumulated and summarized to form the total annual economic output, which serves as the economic benefit target.

[0073] Repeat the monthly-scale solution and annual-scale evaluation process, continuously adding qualified solutions to the candidate solution set; until the preset maximum number of iterations is reached, completing the accumulation of the candidate solution set.

[0074] An improved multi-objective particle swarm optimization algorithm is used to solve for the candidate solution set. A dynamic inertia weight strategy is introduced to enhance the global search capability, and an alternating projection correction algorithm is used to handle complex constraints, thus obtaining the Pareto solution set. In this embodiment, the population size is set to 50, the number of iterations is 1000, the inertia weight is reduced from 0.8 to 0.2, the individual learning factor and the group learning factor are both 1.5, and the number of iterations is 20.

[0075] An improved multi-objective particle swarm optimization algorithm is used to solve for the candidate solution set. The final Pareto solution set is essentially a set of feasible multi-objective optimization schemes for water resource scheduling that satisfy all constraints. This represents a combination of key decision variables for the water resource scheduling scheme, which must simultaneously satisfy complex constraints such as local water availability, water demand range, water transfer capacity, ecological protection, and carbon emission thresholds, and achieve multi-objective collaborative optimization. Specifically, this includes:

[0076] The monthly local water resource allocation (the proportion of surface water, groundwater, and reclaimed water used) for each water-receiving area, the monthly external water transfer volume (the volume of water transferred from external sources) for each water-receiving area, and the pump station operation parameters related to water transfer (such as energy consumption, water transmission routes, etc., affecting carbon emissions) are all considered. Each solution in the Pareto solution set is a non-dominated solution: that is, it is impossible to improve one objective (such as increasing economic output) without harming other objectives (such as reducing ecosystem service value or increasing carbon emissions). These solutions collectively constitute the boundary of the multi-objective trade-off, providing a foundation for subsequent selection of the optimal solution using fuzzy analytic hierarchy process (AHP) and entropy weight method.

[0077] Therefore, the improved multi-objective particle swarm optimization algorithm in this embodiment solves a set of water resource scheduling schemes that can balance annual economic benefits with monthly ecological, social, and environmental benefits under complex constraints, ultimately providing a basis for selecting the optimal scheduling scheme that can be implemented.

[0078] S5. Using fuzzy hierarchical analysis and entropy weight method for multi-attribute decision-making, the final water resource allocation scheme is selected from the Pareto solution set. This includes the following steps: S51. Determine decision indicators and decision objects. Decision indicators: The core indicators are the multi-scale objectives corresponding to each scheme in the Pareto solution set, including: Annual scale: Annual economic output is the economic benefit target.

[0079] Monthly scale: The monthly water ecosystem service value is the ecological benefit target, the monthly water user satisfaction is the social benefit target, and the monthly water diversion carbon emissions are the environmental benefit target.

[0080] Decision object: All non-inferior solutions in the Pareto solution set, that is, feasible solutions that cannot improve one objective without reducing other objectives.

[0081] S52. Determine subjective and objective weights monthly. (1) Calculate subjective weights using fuzzy analytic hierarchy process. Based on expert experience and decision-making preferences, the importance of each monthly target indicator (ecosystem service value, water user satisfaction, and carbon emissions) is ranked, and a fuzzy judgment matrix is ​​constructed.

[0082] By using hierarchical single ranking and consistency testing, the subjective weight of each indicator is quantified. The subjective weight reflects the degree of importance that people attach to the goal. For example, the weight of water satisfaction may be increased during the dry season.

[0083] (2) Calculate the objective weights using the entropy weight method Based on the monthly indicator data of each scheme in the Pareto solution set, the information entropy of each indicator is calculated. The smaller the entropy value, the greater the difference in indicator data, and the more significant the impact on decision-making.

[0084] Objective weights are derived from information entropy. Objective weights reflect the dispersion of the data itself. For example, when carbon emission data fluctuates greatly, its objective weight is higher.

[0085] (3) Combination of subjective and objective weights This invention employs a combined subjective and objective weighting method to determine the weights of each decision indicator monthly. Subjective weights are determined monthly using fuzzy hierarchical analysis, while objective weights are determined by analyzing the non-dominated solution set using the entropy weight method. Combining the results of these two calculations, the combined weights are: ; in, For combination coefficients, For combined weights, Subjective weighting, For objective weighting.

[0086] This embodiment calculates the subjective and objective weights for each month, using 0.7 as the combination coefficient, and combines the subjective and objective weights according to the following formula: ; By combining weights, expert experience and data objectivity can be balanced.

[0087] S53. Evaluate the scheme based on combined weights and multiple attributes. For each scheme in the Pareto solution set, a weighted score is assigned to its monthly indicators (ecosystem service value, water user satisfaction, and carbon emissions) according to the combined weights each month.

[0088] By combining the annual economic output target with the monthly scoring results, the overall benefit score of each plan is calculated.

[0089] S54, Preferred Final Solution The scheme with the highest comprehensive benefit score is selected as the final water resource allocation scheme. This scheme must simultaneously meet the following requirements: optimal economic benefits on an annual scale, coordinated balance of ecological, social and environmental benefits on a monthly scale, and all indicators must be within the constraints of local water supply, water demand range, carbon emission threshold, etc.

[0090] This embodiment solves the above optimization model month by month to obtain the monthly Pareto solution set, performs multi-attribute decision-making, determines the scheduling scheme for that month, and then sequentially performs subsequent solutions and decisions until the annual decision is completed, obtaining the monthly scheduling scheme for the whole year. This embodiment uses a weighting method that combines subjective and objective factors, reflecting both the expert's priority judgment of objectives for different time periods (such as emphasizing agricultural water use satisfaction during the peak irrigation season) and using the characteristics of the data itself to correct for weight biases. Finally, it selects the optimal scheme from the Pareto solution set that takes into account the multi-dimensional benefits of "economic, ecological, social, and environmental" factors, solving the problem that single decision-making methods may have excessive subjectivity or ignore the actual data characteristics.

[0091] In this embodiment, the annual monthly scheduling scheme achieves refined water resource scheduling through dynamic allocation and multi-constraint balancing, specifically including: (1) Water allocation rules Prioritize the use of locally available water. When local water supply is insufficient, supplement it with water from other regions to ensure that the total water supply falls within the water demand range of the receiving area, thus meeting the minimum demand for water-saving scenarios without exceeding the maximum demand for normal scenarios.

[0092] In monthly allocation, the ratio of local water to external water transfer is dynamically adjusted according to seasonal water demand fluctuations (such as peak agricultural irrigation season and peak ecological water demand). For example, during the rainy season, local surface water use is increased and external water transfer is reduced to reduce carbon emissions; during the dry season, external water transfer is increased to ensure water user satisfaction.

[0093] (2) Target Cooperative Scheduling Economic objective: Maximize annual economic output by controlling water resource input (within the water demand range) and combining it with other production factors (labor, capital, etc.), while avoiding excessive water diversion for short-term economic gains.

[0094] Ecological objectives: To ensure the stability of ecosystem service value by maintaining a reasonable water storage capacity (related water area) and to avoid the shrinkage of local water bodies due to excessive water diversion.

[0095] Social goals: Ensure that the total water supply is close to the upper limit of the water demand range and improve water user satisfaction (e.g., prioritize domestic water use and balance industrial and agricultural water use).

[0096] Environmental objectives: Optimize water transfer routes and pump station operations to reduce energy consumption and carbon emissions, while maintaining the ecosystem's carbon sequestration capacity through water transfer (such as protecting wetland areas).

[0097] (11) Constraints and Guarantees All scheduling actions are constrained by the local available water supply limit and the water demand zone boundary to ensure the feasibility of the plan, and ecological water use shall not be lower than the lower limit of the water demand zone.

[0098] In summary, the final scheduling scheme, through multi-scale nested optimization, quantitative trade-offs of multiple objectives, and dynamic constraint allocation, achieves comprehensive optimal utilization of water resources across economic, ecological, social, and environmental dimensions. The final scheme involves adjusting the monthly pumping and releasing volumes of each pumping station and the monthly water intake volumes of each water-receiving area.

[0099] The benefits of the optimal scheduling scheme under different water inflow scenarios are as follows: Table 3. Benefits of the Optimal Scheduling Scheme under Different Water Inflow Scenarios To verify the superiority of this method in coordinating multiple objectives, the above model is used as the comprehensive scenario (CS). Based on this, six comparative scenarios are designed by prioritizing or ignoring a certain objective: Scenario 1 (disregarding emission reduction), Scenario 2 (disregarding ecosystem service value), Scenario 3 (disregarding satisfaction), Scenario 4 (emphasizing satisfaction), Scenario 5 (emphasizing ecosystem service value), and Scenario 6 (emphasizing emission reduction). The limitations of the one-sided optimization strategy are analyzed. Taking the incoming water scenario as an example, the water resources were optimized and scheduled. The average annual water user satisfaction, total annual economic benefits, ecological service value of the water diversion system, and carbon emissions of all water-receiving areas are shown in Table 4.

[0100] Table 4 System scheduling benefits under different target scenarios Calculations show that the CS scenario achieves a balance among various objectives through a comprehensive optimization strategy, with overall performance slightly better than other scenarios. Although the economic benefits of the CS scenario are about 0.26% lower than other high-economic-return scenarios, this small difference leads to a 10.08% increase in water user satisfaction, a 10.93% reduction in carbon emissions, and stable ecosystem service value. Taking the P=50% inflow scenario as an example, the discharge volume of each pumping station under the comprehensive optimization strategy ( ) and water intake of each water receiving area ( )as follows:

[0101] Table 5. Comprehensive Optimization Strategy and Scheduling Plan under the Scenario of P=50% Inflow (Unit: 100 million cubic meters) Table 5 shows the scheduling scheme developed through a comprehensive optimization strategy under the scenario of P=50% inflow. This scheme involves 42 variables, covering the water transfer volume over 12 months, in billions of cubic meters. In the table, x1~x 25For the example, the monthly water pumping or releasing volume of 25 water conservancy projects (including pumps and gates) in the water diversion system during the planned year is y1~y 20 The monthly water transfer volume is calculated for 20 water-receiving areas. Based on the river network topology, the water volumes of the variables are interrelated, and some variables have equation relationships. All variables form a close-knit water transfer network in the scheduling scheme.

[0102] The novel water resource optimization and scheduling method proposed in this invention, which combines multi-objective collaborative optimization capabilities and multi-timescale coupling characteristics, has the following advantages: (1) The constructed target system covers multiple aspects such as economic benefits, water ecosystem service value, carbon emission benefits and water user satisfaction, which can more comprehensively and objectively reflect the comprehensive needs of multi-objective water resource scheduling; it realizes multi-scale and multi-objective water resource optimization scheduling, and thus can effectively improve the comprehensive utilization efficiency of water resources. (2) The proposed two-level optimization scheduling model can effectively realize the synergistic optimization of annual and monthly scales. Through the multi-scale coupling and nesting mechanism, it enhances the adaptability of the scheduling model in terms of time-series control and resource coordination. (3) The solution model has stronger adaptability and stability when facing complex constraints, and can efficiently search the feasible solution space, significantly improving the solution efficiency and convergence speed. (4) The obtained scheduling scheme achieves better comprehensive performance under multi-objective trade-offs, and the comprehensive benefits obtained are higher than those of existing scheduling methods. This invention can coordinate the ecological and social benefits of water transfer in a water transfer system. While ensuring the water demand of the receiving area, it can significantly improve the ecological service value of the water area and reduce carbon emissions, thus achieving comprehensive optimization of water resource allocation. It provides a scientific basis and decision support for the operation of water transfer projects, so as to better serve the scientific management and efficient operation of complex water resource systems.

[0103] Meanwhile, this method can also be applied to other problems in the optimization of water conservancy projects, providing theoretical support for the research on the optimal scheduling of complex water transfer systems.

[0104] Based on the same inventive concept, this invention also proposes a water resource optimization scheduling system, comprising: The water supply and demand analysis module is used to obtain the local available water volume under different water inflow frequencies in the study area, and the departmental water demand under water-saving scenarios and normal water use scenarios; based on the local available water volume and the water demand of each department, the water demand range of the water receiving area is determined; the water demand range of the water receiving area is used to characterize the water consumption range under water-saving scenarios and normal water use scenarios.

[0105] The multidimensional system module is used to calculate the economic output corresponding to water resource input, the ecological service value of water bodies, the quantitative results of water user satisfaction, and the carbon emissions of the water transfer process under the common constraints of local water supply, water demand of various sectors, and water demand range of the water receiving area.

[0106] The scheduling plan generation module is used to optimize the ecological benefits, water use satisfaction, and carbon emission targets on a monthly basis, using economic output, water ecosystem service value, water user satisfaction, and carbon emission as optimization indicators. It allocates local available water and external water transfer, optimizes ecological benefits, water user satisfaction, and carbon emission reduction targets on a monthly basis, and iteratively generates monthly water resource scheduling plans based on the optimization results. The monthly water resource scheduling plans for 12 months are integrated to form an annual water resource scheduling plan.

[0107] The scheduling scheme optimization module determines the scheduling scheme with the best comprehensive benefits from the annual water resource scheduling schemes, which is then used as the final water resource scheduling scheme.

[0108] Each module in the aforementioned water resource optimization and scheduling system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0109] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the steps in the water resource optimization scheduling method embodiment. Specific implementation methods can be found in the method embodiment, and will not be repeated here.

[0110] Furthermore, the present invention also provides a non-transitory computer-readable storage medium containing instructions, on which a computer program is stored. For example, a memory containing instructions that can be executed by a processor of a computer device to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc. When the computer program is executed by the processor, it can implement the steps in the water resource optimization scheduling method embodiment. Specific implementation methods can be found in the method embodiment, and will not be repeated here.

[0111] Those skilled in the art will understand that embodiments of the present invention can provide methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0112] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0113] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0114] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0115] It should be noted that the specific embodiments described above enable those skilled in the art to more fully understand the present invention, but do not limit the present invention in any way. Therefore, although the present invention has been described in detail in this specification and embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the present invention; and all technical solutions and improvements that do not depart from the spirit and scope of the present invention are covered within the protection scope of the present invention patent. No reference numerals in the claims should be construed as limiting the scope of the claims. Any simple variations or equivalent substitutions of technical solutions that can be readily obtained by those skilled in the art within the scope of the technology disclosed in the present invention are within the protection scope of the present invention.

Claims

1. A method for optimizing water resource allocation, characterized in that, include: The local available water volume of each water-receiving area under different water inflow frequencies in the study area, and the departmental water demand under water-saving scenarios and normal water use scenarios are obtained. Based on the water demand of each department, the water demand range of the water-receiving area is determined. The water demand range of the water-receiving area is used to characterize the water consumption range under water-saving scenarios and normal water use scenarios. Under the common constraints of the local available water supply, the water demand of each sector and the water demand range of the water receiving area, the economic output corresponding to water resource input, the ecological service value of the water area, the quantitative results of water user satisfaction and the carbon emissions of the water transfer process are calculated. Using a monthly timeframe as the unit of time, and with economic output, water ecosystem service value, water user satisfaction quantification results, and carbon emissions as optimization indicators, the system allocates local available water and external water transfer volume, optimizes ecological benefit targets, water user satisfaction targets, and carbon emission reduction targets on a monthly basis, and generates monthly water resource scheduling plans iteratively based on the optimization results. The monthly water resource scheduling plans for 12 months are then integrated to form an annual water resource scheduling plan. The optimal water resource allocation plan with the best overall benefits is determined from the annual water resource allocation plans and used as the final water resource allocation plan.

2. The water resource optimization scheduling method according to claim 1, characterized in that, The process of determining the optimal water resource allocation plan from the annual water resource allocation plans, as the final water resource allocation plan, specifically includes the following steps: Using an annual time scale as the unit of time, the economic benefit target of the annual water resource allocation plan is calculated based on the economic output corresponding to the water resource input, and the annual water resource allocation plan and its corresponding economic benefit target are stored in the candidate solution set; wherein, the economic output of each month is accumulated and summarized to form the total annual economic output, which is used as the economic benefit target; Repeat the monthly-scale solution and annual-scale evaluation process, continuously adding qualified solutions to the candidate solution set; until the preset maximum number of iterations is reached, and the candidate solution set is accumulated. An improved multi-objective particle swarm optimization algorithm is used to solve for the candidate solution set, and the Pareto solution set is obtained; the Pareto solution set is the set of feasible multi-objective optimization schemes for water resource scheduling that satisfy all constraints. By using fuzzy hierarchical analysis and entropy weight method for multi-attribute decision-making, the final water resource allocation scheme is selected from the Pareto solution set.

3. The water resource optimization scheduling method according to claim 2, characterized in that, The method of using fuzzy hierarchical analysis and entropy weight method to perform multi-attribute decision-making and select the final water resource allocation scheme from the Pareto solution set specifically includes the following steps: The decision indicators and decision objects are determined; the decision indicators include annual economic benefit targets, monthly ecological benefit targets, social benefit targets, and environmental benefit targets; the decision objects are all non-dominated solutions in the Pareto solution set. Based on decision indicators and decision objects, subjective weights are calculated using fuzzy hierarchical analysis and objective weights are calculated using entropy weight method. The subjective weights and objective weights are combined to obtain the combined weights; Based on the combined weights, the schemes in the Pareto solution set are evaluated for multiple attributes, and the final water resource scheduling scheme is selected based on the evaluation results.

4. The water resource optimization scheduling method according to claim 2, characterized in that, The economic output corresponding to water resource input is calculated using a water economic production model. The improved Cobb-Douglas function, which incorporates a technological progress factor, is used to obtain the water economic production model, which is specifically as follows: ； in, The numbering of the water-receiving area for Economic output of the water-receiving area; Based on basic production efficiency parameters; This indicates technological progress or productivity growth. The rate of technological progress or the growth rate of production efficiency. This is the total number of years from the beginning of the fitting period to the planning year; , , , These represent the four factors of production: labor input, capital input, energy input, and water resource input. , , , These are the elasticity coefficients of each production factor; The water ecosystem service value is quantified using a water ecosystem service value model, which specifically includes: ； in, for The ecological service value of water bodies within the time system. The equivalent factor of ecosystem service value per unit area of ​​water. The economic value per unit equivalent; for The system's water area and water storage capacity at any given time. There is a functional relationship; The quantitative result of water use satisfaction is calculated using a water use satisfaction model, which is specifically as follows: ； in, for Quantitative results of water user satisfaction in the water-receiving area To achieve the basic satisfaction level at the lower limit of water demand, and These are the upper and lower limits of water demand, respectively. For the local water supply, for Water diversion volume from outside the water receiving area; The carbon emissions from the water diversion process are calculated using a carbon emission accounting model, which specifically refers to: ； in, This refers to the total carbon emissions during the water diversion process. To quantify the carbon emissions generated by the energy consumption of pumping stations during water transfer, Changes in ecosystem carbon sequestration caused by variations in the total amount of water resources within the system. Carbon emission coefficient per unit energy consumption This refers to the total energy consumption generated by the pumping station during the water diversion process. Carbon sequestration per unit area of ​​lakes and wetlands This represents the change in the area of ​​lakes and wetlands caused by human water diversion; an increase in area is positive, and a decrease in area is negative.

5. The water resource optimization scheduling method according to claim 4, characterized in that, The economic benefit target is the outer annual target, and the optimization model for the outer annual target is: ； in, For economic benefit objectives; The ecological benefit target, water use satisfaction target, and carbon emission reduction target are the inner-layer monthly targets, and the optimization model for the inner-layer monthly targets is as follows: ； ； ； in, , To serve the value goal of ecology For the first The ecological service value of water bodies within a given time period. To achieve the goal of water user satisfaction, For the first Time period Water satisfaction in the water-receiving area To achieve carbon emission reduction targets, For the first Carbon emissions of the system during a given time period.

6. The water resource optimization scheduling method according to claim 2, characterized in that, The improved multi-objective particle swarm optimization algorithm is based on the original multi-objective particle swarm optimization algorithm, introducing a dynamic inertia weight strategy and an alternating projection correction algorithm, specifically including: Dynamic inertia weighting strategy: Introducing inertia weights and a learning factor, combining individual and group extreme values ​​to update particle velocity and position; assuming that in 3D search space, by Each particle forms a population. , of which Each particle can be represented as one dimensional vector Then its core formula is expressed as: ； ； in, , These are the individual extreme value and the group extreme value, respectively. Inertial weights; For the dimensions of the search space; This represents the current iteration number; For the first The velocity vector of each particle; , is a non-negative constant, and is the learning factor for the particle; , It is distributed in Random numbers between; For exercise time; The alternating projection correction algorithm consists of the following steps: Equality constraint projection correction: Denote the equality constraint as In order to correct To satisfy this equality constraint, the least squares method is used to minimize the error for the current... The steps for projection are as follows: ； in, It is a matrix for calculating the correction direction, adjusting the error along the direction of the equality constraint; It is the residual of the equality constraint, representing the current The degree to which the constraints were violated; Adjustment of upper and lower limits for independent variables: The independent variable has upper and lower limit constraints. If the limits are exceeded, a truncation method can be used for correction. ； Correct out-of-range values ​​to between the upper and lower limits of the independent variable to ensure the new... Satisfy its own upper and lower limit constraints; Other inequality constraint corrections: In addition to the upper and lower bound constraints of the independent variable itself, there may be other more complex inequality constraints. Identify cases where constraints are not met, and use a fixed-step direct correction method to gradually correct them until the lake's water storage constraints are satisfied, then return to the latest... value; Alternating iteration: The above three steps are executed alternately to gradually approach the value that simultaneously satisfies equality constraints and inequality constraints; Convergence is determined when the change between two iterations is small, indicating that a solution satisfying the constraints has been found. ； in, For tolerance.

7. The water resource optimization scheduling method according to claim 1, characterized in that, The specific steps for obtaining the water demand area of ​​the water receiving area include: By estimating the local surface water supply, groundwater supply, and reclaimed water supply in each water-receiving area of ​​the study region, the time period was obtained. No. Local water supply in the water-receiving area ; Estimate the water consumption of the water-receiving area during the same period under the scenario of implementing the strictest water conservation measures. Water consumption under normal water usage scenarios Water use types in various scenarios include domestic water use, industrial water use, agricultural water use, and ecological water use. Based on the local available water supply and the water demand of each sector, the water demand range of the water-receiving area for this period is determined and denoted as follows: .

8. A water resource optimization scheduling system, characterized in that, include: The water resources supply and demand analysis module is used to obtain the local available water volume of each water-receiving area under different water inflow frequencies in the study area, and the departmental water demand of each department under water-saving scenarios and normal water use scenarios. Based on the water demand of each department, the water demand range of the water-receiving area is determined. The water demand range of the water-receiving area is used to characterize the water consumption range under water-saving scenarios and normal water use scenarios. The multi-dimensional system module is used to calculate the economic output corresponding to water resource input, the ecological service value of water area, the quantitative results of water user satisfaction, and the carbon emissions of water transfer process under the common constraints of the local available water supply, the water demand of each sector and the water demand range of the water receiving area. The scheduling plan generation module is used to optimize the ecological benefits, water use satisfaction and carbon emission targets on a monthly basis, using economic output, water ecosystem service value, water user satisfaction and carbon emission as optimization indicators. It allocates local available water and external water transfer, optimizes the ecological benefits, water user satisfaction and carbon emission reduction targets on a monthly basis, and generates monthly water resource scheduling plans in a sequential iteration based on the optimization results. The monthly water resource scheduling plans of 12 months are integrated to form an annual water resource scheduling plan. The scheduling scheme optimization module is used to determine the scheduling scheme with the best comprehensive benefits from the annual water resource scheduling schemes, which will be used as the final water resource scheduling scheme.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement 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 loaded by the processor, it is able to perform the steps of the method according to any one of claims 1 to 7.

Images